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Sample details Pages: 25 Words: 7565 Downloads: 10 Date added: 2017/06/26 Category Finance Essay Type Narrative essay Did you like this example? The importance of programming is of prime value for Actuarial Science and for the actuarial profession. The complex calculations merged with routine task based calculations have made programming a viable source for automation. In this dissertation, we show how the programming language, R can be used for claim models to compute aggregate claims using poisson, binomial and negative binomial distributions. We also demonstrate how to use MortalitySmooth package to compute deaths and exposure data suitable for smoothing mortality data. An essential aspect of this method is that smoothening of data allows forecasting of mortality data to use in computing annuities for different countries. We explain these methods using Danish dataset for aggregate claims and Human mortality database (HMD , https://www.mortality.org), a collection of mortality data of various developed countries. DonÃ¢â¬â¢t waste time! Our writers will create an original "Importance Of Computer Automation To Insurance Companies Finance Essay" essay for you Create order Chapter 1 Introduction The insurance firms functions making insurance products attains profitability through charging premiums surpassing overall expenses of the firm and making wise investment decisions in maximizing returns under optimal risk conditions. The method of charging premiums depends on so many underlying factors such as number of policy holders, number of claims, amount of claims, health condition, age, gender of the policy holder and so on. Some of these factors such as loss aggregate claims and human mortality rates have adverse impact on determining the premium calculation to remain solvent. Likewise, these factor need to be modelled using large amount of data, loads of simulations and complex algorithms to determine and manage risk. In this dissertation we shall consider two important factors affecting the premiums, the aggregate loss claims and human mortality. We shall use theoretical simulations using R and use Danish data to model aggregate claims and human mortality database to obtain human mortality rates and smoothen to price life insurance products respectively. In chapter 2 we shall examine the concepts of compounds distribution in modelling aggregate claim and perform simulations of the compound distribution using R packages such as MASS and Actuar. Finally we shall analyse Danish loss insurance data from 1980 to 1990 and fit appropriate distributions using customized generically implemented R methods. In chapter 3 we shall explain briefly on concepts of graduation, generalised linear models and smoothening techniques using B-splines. We shall obtain deaths and exposure data from human mortality database for selected countries Sweden and Scotland and shall implement mortality rates smoothing using mortalitysmooth package. We compare the mortality rates based on various sets such as Males and females for specific country or total mortality rates across countries like Sweden and Scotland for a given time frame ranging age wise or year wise. In chapter 4 we shall look into various life insurance and pension related products widely used in the insurance industry and construct life tables and commutation functions to implement annuity values. Finally chapter 5 we present concluding comments to the dissertation. Chapter 2 Aggregate Claim distribution 2.1 Background Insurance based companies implement numerous techniques to evaluate the underlying risk of their assets, products and liabilities on a day- to-day basis for many purposes. These include Computation of premiums Initial reserving to cover the cost of future liabilities Maintain solvency Reinsurance agreement to protect from large claims In general, the occurrence of claims is highly uncertain and has underlying impact on each of the above. Thus modelling total claims is of high importance to ascertain risk. In this chapter we shall define claim distributions and aggregate claims distributions and discuss some probabilistic distributions fitting the model. 2.2 Modelling Aggregate Claims The dynamics of insurance industry has different effects on the number of claims and amount of claims. For instance, Expanding insurance business would have proportional increase on number of claims but negligible or no impact on amount of claims. Conversely, cost control initiatives, technology innovations have adverse effect on amount of claims but has zero effect on number of claims. Consequently, the aggregate claim is modelled based on the assumption that the number of claims occurring and amount of claims are modelled independently. 2.2.1 Compound distribution model We define compound distribution as S Random variable denoting the total claims occurring in a fixed period of time. Denote the claim amount representing the i-th claim. N Non-negative, independent random variable denoting number of claims occurring in a time period. Further, is a sequence of i.i.d random variables with probability density function given by and cumulative density function by with probability of 0 is 1 for 1iN. Then we obtain the aggregate claims S as follows With Expectation and variance of S found as follows Thus S, the aggregate claims is computed using Collective Risk Model and follows compound distribution. (pg 86 Non-life actuarial model theory, methods and evaluation) 2.3 Compound distribution for aggregate claims As discussed in Section 2.1 S, follows compound distribution. were N, the number of claims is the primary distribution and X, the amount of claims being secondary distribution In this section we shall describe the three main compound distributions widely used to model aggregate claims models. The primary distribution, N can be modelled based on non-negative integer valued distributions like poisson, binomial and negative binomial. The selection of a distribution depends from case to case. 2.3.1 Compound Poisson distribution The Poisson distribution is referred to distribution of occurrence of rare events, Number of accidents per person, number of claims per insurance policy and number of defects found in product manufacturing are some of the real time examples of Poisson distribution. Here, the primary distribution N has a Poisson distribution denoted by N ~ P(ÃÆ'Ã ½ÃâÃ » with parameter ÃÆ'Ã ½ÃâÃ ». The probability density function, expectation and variance are given as follows for x=0,1. Then S has a compound Poisson distribution with parameters ÃÆ'Ã ½ÃâÃ » and denoted as follows S ~ CP(ÃÆ'Ã ½ÃâÃ », and 2.3.2 Compound Binomial distribution The binomial distribution is referred to distribution of number of success occurring in an event, the number of males in a company, number of defective components in random sample from a production process is real time examples representing this distribution. The compound binomial distribution is a natural choice to model aggregate claims when there is an upper limit on the number of claims in a given time period. Here, the primary distribution N has a binomial distribution with parameters n and p denoted by N ~ B(n,p. The probability density function, expectation and variance are given as follows For x=0,1,2.n Then S has a compound binomial distribution with parameters ÃÆ'Ã ½ÃâÃ » and denoted as follows S ~ CB(n, p , -p) 2.3.3 Compound Negative Binomial distribution The compound negative binomial distribution models aggregate claim models. The variance of negative binomial is greater than its mean and thus we can use negative binomial over Poisson distribution if the data has greater variance than its mean. This distribution provides a better fit to the data. Here, the primary distribution N has a negative binomial distribution with parameters n and p denoted by N ~ NB(n,p with n0 and 0p1. The probability density function, expectation and variance are given as follows for x=0,1,2. / Then S has a compound negative binomial distribution with parameters ÃÆ'Ã ½ÃâÃ » and denoted as follows S ~ CNB(n,p, 2.4 Secondary distributions claim amount distributions. In Section 2.3, we defined the three different compound distributions widely used. In this section, we shall define the generally used distributions to model secondary distributions for claim amounts. We use positive skewed distributions. Some of these distributions include Weibull distribution used frequently in engineering applications. we shall also look into specific distributions such as Pareto and lognormal which are widely used to study loss distributions. 2.4.1 Pareto Distribution The distribution is named after Vilfredo Pareto who used it in modelling economic welfares. It is used these days to model income distribution in economics. The random variable X has a Pareto distribution with parameters and ÃÆ'Ã ½ÃâÃ » where, ÃÆ'Ã ½ÃâÃ » 0 and is denoted by X~ ( or X ~ Pareto(, ÃÆ'Ã ½ÃâÃ ») The probability density function, expectation and variance are given as follows For x0 2.4.2 Log normal Distribution The random variable X has a Log normal distribution with parameters and where, 0 and is denoted by X ~ LN(, ), Where, and are the mean and variance of Log(X). The log normal distribution has a positive skew and is a very good distribution to model claim amount. The probability density function, expectation and variance are given as follows For x0 and 2.4.3 Gamma distribution The gamma distribution is very useful to model claim amount distribution it has , the shape parameter and ÃÆ'Ã ½ÃâÃ » the scale parameter. The random variable X has a Gamma distribution with parameters and ÃÆ'Ã ½ÃâÃ » where, ÃÆ'Ã ½ÃâÃ » 0 and is denoted by X~ ( or X ~ Gamma(, ÃÆ'Ã ½ÃâÃ ») The probability density function, expectation and variance are given as follows For x0 2.4.4 Weibull Distribution The Weibull distribution is extreme valued distributions, because of its survival function it is used widely in modelling lifetimes. The random variable X has a Weibull distribution with parameters and ÃÆ'Ã ½ÃâÃ » where, ÃÆ'Ã ½ÃâÃ » 0 and is denoted by X~ ( The probability density function, expectation and variance are given as follows For x0 2.5 Simulation of Aggregate claims using R In section 2.3 we discussed about aggregate claims and the various compound distributions used to model it. In this section we shall perform random simulation using R program. 2.5.1 Simulation using R The simulation of aggregate claims was implemented using packages like Actuar, MASS. The generic R code available in Programs/Aggregate_Claims_Methods.r implements simulation of random generated aggregate claim of any compound distribution samples. The following R code below generates simulated aggregate claim data for Compound Poisson distribution with gamma as the claim distribution denoted by CP(10,. require(actuar) require(MASS) source(Programs/Aggregate_Claims_Methods.r) Sim.Sample = SimulateAggregateClaims (ClaimNo.Dist=pois, ClaimNo.Param =list(lambda=10),ClaimAmount.Dist=gamma,ClaimAmount.Param= list(shape = 1, rate = 1),No.Samples=2000 ) names(Sim.Sample) The SimulateAggregateClaims method in Programs/Aggregate_Claims_Methods.r generates and returns simulated aggregate samples along with expected and observed moments. The simulated data can then be used to perform various tests, comparisons and plots. 2.5.2 Comparison of Moments The moments of expected and observed are compared to test the correctness of the data. The following R code returns the expected and observed mean and variance of the simulated data Respectively. Sim.Sample$Exp.Mean;Sim.Sample$Exp.Variance Sim.Sample$Obs.Mean;Sim.Sample$Obs.Variance Table 2.1 Comparison of Observed and Expected moments for different sample size. The Table 2.1 above shows the simulated values for different Sample size. Clearly the observed and expected moments are similar and the difference between them converges as Number of sample increases. 2.5.3 Histogram with curve fitting distributions Histograms can provide useful information on skewness, information on extreme points in the data, the outliers and can be graphically measured or compared with shapes of standard distributions. The figure 2.1 below shows the fitted histogram of simulated data compared with standard distributions like Weibull, Normal, Lognormal and Gamma respectively. Figure 2.1 Histogram of simulated aggregate claims with fitted standard distribution curves. Figure 2.1 represents the histogram of the stimulated data along with the fitted curves for different distributions. The histogram is plotted by dividing them in to breaks of 50. The simulated data is then fitted using the fitdistr() function in the MASS package and fitted for various distributions like Normal,Lognormal,Gamma and Weibull distribution. The following R program describes how the fitdistr method is used to compute the Gamma parameters and plot the respective curve as described in Figure 2.1 gamma = fitdistr(Agg.Claims,gamma) Shape = gamma$estimate Rate= gamma$estimate Scale=1/Rate Left = min(Agg.Claims) Right = max(Agg.Claims) Seq = seq(Left,Right,by= 0.01) lines(Seq,dgamma(Seq,shape=Shape, rate= Rate, scale=Scale), col = blue) 2.5.4 Goodness of fit The goodness of fit compare the closeness of expected and observed values to conclude whether it is reasonable to accept that the random sample fits a standard distribution or not. It is type of hypothesis testing were the hypotheses are defined as follows. : Data fits with the standard distribution : Data does not fit with the standard distribution The chi-square test is one of the ways to test goodness of fit. The test uses histogram and compares it with the fitted density. It is used by grouping data into different intervals using k breaks. The breaks are computed using quantiles. This computes the expected frequency,. , the observed frequency is calculated using the product of difference of the c.d.f with sample size. The test statistic is defined as Where is the observed frequency and is expected frequency for k breaks respectively. To perform simulation we shall use breaks of 100 to split the data into equal cells of 100 and use histogram count to group the data based on the observed values. Large values of leads to rejecting null hypothesis The test statistic follows distribution with k-p-1 degrees of freedom where p is the number of parameters in sample data. The p-value is computed using 1- pchisq() and is accepted if p-value is greater than the significance level . The following R code computes chi-square test Test.ChiSq=PerformChiSquareTest( Samples.Claims= Sim.Sample$AggregateClaims,No.Samples=N.Samples) Test.ChiSq$DistName Test.ChiSq$X2Val;Test.ChiSq$pvalue Test.ChiSq$Est1; Test.ChiSq$Est2 Table 2.3 Chi-Square and p-value for compound Poisson distribution The highest p-value signifies better fit of data with the standard distribution. Weibull distribution is a better fit with the following parameters shape =2.348 and scale = 11.32. 2.6 Fitting Danish Data 2.6.1 The Danish data source of information In this section we shall use a statistical model and fit a compound distribution to compute aggregate claims using historical data. Fitting data into a probability distribution using R is an interesting exercise, and is worth quoting All models are wrong, some models are useful. In previous section we explained fitting distribution, comparison of moments and goodness of fit to simulated data. The data source used is Danish data composed from Copenhagen Reinsurance and contains over 2000 fire loss claims details recorded during 1980 to 1990 period of time. This data is adjusted for inflation replicating 1985 values and are expressed in Danish Krone (DKK) currencies in millions. The data recorded are large values and are adjusted for inflation. There are 2167 rows of data over 11 years. Grouping the data over years results in 11 aggregate samples of data. This would be insufficient information to fit and plot the distribution. Therefore, the data is grouped month-wise aggregating to 132 samples. The expectation and variance of the aggregate claims are 55.572 and 1440.7 respectively. The figure 2.2 shows the time series plot against the claim numbers inferring the different claims occurred monthly from 1980 to 1990, it also shows the extreme values of loss claims and the time of occurrence. There are no seasonal effects on the data as the 2 sample test for summer and winter data is compared and the t-test value infers there is no difference and conclude that there is no seasonal variation. Figure 2.2 Time series plot of Danish fire loss insurance data month wise starting 1980-1990. The data is plotted and fitted into an histogram using fitdistr() function in MASS package of R. 2.6.2 Analysis of Danish data We shall do the following steps to analyse and fit the data. Obtain the claim numbers and loss aggregate claim data month wise. Choose primary distribution to be Poisson or negative binomial and use fitdistr() function to obtain the parameters. Assume Gamma distribution as the default loss claim distribution and use fitdistr() to obtain the shape and rate parameters. Simulate for 1000 samples using section 2.5.1, also plot the histogram along with the fitted standard distributions as described in section 2.5.2. Perform chi-square test to identify the optimal fit and obtain the distribution parameters. Finally implement another simulation using the primary distribution and fitted secondary distribution. 2.6.3 R Implementation We will do the following to implement R. The Danish data is assumed to take gamma distribution Plot the computed aggregate claims and use fitdistr() to get the parameters using gamma or lognormal. Now, using generic R implementation discussed in Section 2.5 we simulate using the new dataset and finally fit with standard distributions. The following R code reads the Danish data available in DataDanishData.txt, segregate the claims month and year wise, to calculate sample mean and variance and plots the histogram with fitted standard distributions. require(MASS) source(Programs/Aggregate_Claims_Methods.r) Danish.Data = ComputeAggClaimsFromData(Data/DanishData.txt) Danish.Data$Agg.ClaimData = round(Danish.Data$Agg.ClaimData, digits = 0) #mean(Danish.Data$Agg.ClaimData) #var(Danish.Data$Agg.ClaimData) #Danish.Data$Agg.ClaimData #mean(Danish.Data$Agg.ClaimNos) #var(Danish.Data$Agg.ClaimNos) Figure 2.3 Actual Danish fire loss data fitted with standard distributions of 132 samples. In the initial case N, the primary distribution is assumed to be Negative Binomial distributed with parameter; k= 25.32 and p=.6067 and the secondary distribution is assumed to be gamma distribution with parameters; Shape =3.6559 and rate =.065817. We simulate using 1000 samples and obtain aggregate claim samples using Section 2.5.1. The plot and chi square test values are defined below as follows. The generic function PerformChiSquareTest, previously discussed in Section 2.4 is used here to compute values of and p-value pertaining to = distribution. The corresponding values are tabulated in table 2.2 below. Figure 2.4 Histogram of simulated samples of Danish data fitted with standard distributions The figure 2.4 shows simulated samples of Danish data calculated for sample size 1000, The figure also shows the different distribution curves fitted to the simulated data. These results suggest that the best possible choice of model is Gamma distribution with parameters Shape = 8.446 and Rate = .00931 Chapter 3 Survival models Graduation In the previous chapter 2, we discussed about aggregate claims and how it can be modelled and simulated using R programming. In this chapter we shall discuss on one of the important factors which has direct impact on arise of a claim, the human mortality. Life insurance companies use this factor to model risk arising out of claims. We shall analyse and investigate the crude data presented in human mortality database for specific countries like Scotland and Sweden and use statistical techniques. Mortality smooth package is used in smoothing the data based on Bayesian information criterion BIC, a technique used to determine smoothing parameter; we shall also plot the data. Finally we shall conclude by performing comparison of mortality of two countries based on time. 3.1 Introduction Mortality data in simple terms is recording of deaths of species defined in a specific set. This collection of data could vary based on different variables or sets such as sex, age, years, geographical location and beings. In this section we shall use human data grouped based on population of countries, sex, ages and years. Human mortality in urban nations has improved significantly over the past few centuries. This has attributed largely due to improved standard of living and national health services to the public, but in latter decades there has been tremendous improvement in health care in recent measures which has made strong demographic and actuarial implications. Here we use human mortality data and analyse mortality trend compute life tables and price different annuity products. 3.2 Sources of Data Human mortality database (HMD) is used to extract data related to deaths and exposure. These data are collected from national statistical offices. In this dissertation we shall look into two countries Sweden and Scotland data for specific ages and years. The data for specific countries Sweden and Scotland are downloaded. The deaths and exposure data is downloaded from HMD under Sweden Deaths https://www.mortality.org/hmd/SWE/STATS/Deaths_1x1.txt They are downloaded and saved as .txt data files in the respective hard disk under /Data/Conutryname_deaths.txt and /Data/Conutryname_exposures.txt respectively. In general the data availability and formats vary over countries and time. The female and male death and exposure data are shared from raw data. The total column in the data source is calculated using weighted average based on the relative size of the two groups male and female at a given time. 3.3 Gompertz law graduation A well-known actuary, Benjamin Gompertz observed that over a long period of human life time, the force of mortality increases geometrically with age. This was modelled for single year of life. The Gompertz model is linear on the log scale. The Gompertz law states that the mortality rate increases in a geometric progression. Hence when death rates are A0 B1 And the liner model is fitted by taking log both sides. = a + bx Where a = and b = The corresponding quadratic model is given as follows 3.3.1 Generalized Linear models are P-Splines in smoothing data Generalized Linear Models (GLM) are an extension of the linear models that allows models to be fit to data that follow probability distributions like Poisson, Binomial, and etc. If is the number of deaths at age x and is central exposed to risk then By maximum likelihood estimate we have and by GLM, follows Poisson distribution denoted by with a + bx We shall use P-splines techniques in smoothing the data. As mentioned above the GLM with number of deaths follows Poisson distribution, we fit a quadratic regression using exposure as the offset parameter. The splines are piecewise polynomials usually cubic and they are joined using the property of second derivatives being equal at those points, these joints are defined as knots to fit data. It uses B-splines regression matrix. A penalty function of order linear or quadratic or cubic is used to penalize the irregular behaviour of data by placing a penalty difference. This function is then used in the log likelihood along with smoothing parameter .The equations are maximised to obtain smoothing data. Larger the value of implies smoother is the function but more deviance. Thus, optimal value of is chosen to balance deviance and model complexity. is evaluated using various techniques such as BIC Bayesian information criterion and AIC Akaikes information criterion techniques. Mortalitysmooth package in R implements the techniques mentioned above in smoothing data, There are different options or choices to smoothen using p-splines, The number of knots ndx ,the degree of p-spine whether linear,quadratic or cubic bdeg and the smoothning parameter lamda. The mortality smooth methods fits a P-spline model with equally-spaced B-splines along x There are four possible methods in this package to smooth data, the default value being set is BIC. AIC minimization is also available but BIC provides better outcome for large values. In this dissertation, we shall smoothen the data using default option BIC and using lamda value. 3.4 MortalitySmooth Package in R program implementation In this section we describe the generic implementation of using R programming to read deaths and exposure data from human mortality database and use MortalitySmooth package to smoothen the data based on p-splines. The following code presented below loads the require(MortalitySmooth) source(Programs/Graduation_Methods.r) Age -30:80; Year - 1959:1999 country -scotland ;Sex - Males death =LoadHMDData(country,Age,Year,Deaths,Sex ) exposure =LoadHMDData(country,Age,Year,Exposures,Sex ) FilParam.Val -40 Hmd.SmoothData =SmoothenHMDDataset(Age,Year,death,exposure) XAxis - Year YAxis-log(fitted(Hmd.SmoothData$Smoothfit.BIC)[Age==FilParam.Val,]/exposure[Age==FilParam.Val,]) plotHMDDataset(XAxis ,log(death[Age==FilParam.Val,]/exposure[Age==FilParam.Val,]) ,MainDesc,Xlab,Ylab,legend.loc ) DrawlineHMDDataset(XAxis , YAxis ) The MortalitySmooth package is loaded and the generic implementation of methods to execute graduation smoothening is available in Programs/Graduation_Methods.r. The step by step description of the code is explained below. Step:1 Load Human Mortality data Method Name LoadHMDData Description Return an object of Matrix type which is a mxn dimension with m representing number of Ages and n representing number of years. This object is specifically formatted to be used in Mortality2Dsmooth function. Implementation LoadHMDData(Country,Age,Year,Type,Sex) Arguments Country Name of the country for which data to be loaded. If country is Denmark,Sweden,Switzerland or Japan the SelectHMDData function of MortalitySmooth package is called internally. Age Vector for the number of rows defined in the matrix object. There must be atleast one value. Year Vector for the number of columns defined in the matrix object. There must be atleast one value. Type A value which specifies the type of data to be loaded from Human mortality database. It can take values as Deaths or Exposures Sex An optional filter value based on which data is loaded into the matrix object. It can take values Males, Females and Total. Default value being Total Details The method LoadHMDData in Programs/Graduation_Methods.r reads the data availale in the directory Data to load deaths or exposure for the given parameters. The data can be filtered based on Country, Age, Year, Type based on Deaths or Exposures and lastly by Sex. Figure: 3.1 Format of matrix objects Death and Exposure. The Figure 3.1 shows the format used in objects Death and Exposure to store data. A matrix object representing Age in rows and Years in column. The MortalitySmooth package contains certain features for specific countries listed in the package. They are Denmark,Switzerland,Sweden and Japan. These data for these countries can be directly accessed by a predefined function SelectHMDData. LoadHMDData function checks the value of the variable country and if Country is equal to any of the 4 countries mentioned in the mortalitysmooth package then SelectHMDData method is internally called or else customized generic function is called to return the objects. The return objects format in both functions remains exactly the same. Step 2: Smoothen HMD Dataset Method Name SmoothenHMDDataset Description Return a list of smoothened object based BIC and Lamda of matrix object type which is a mxn dimension with m representing number of Ages and n representing number of years. This object is specifically formatted to be used in Mortality2Dsmooth function. Returns a list of objects of type Mort2Dsmooth which is a two-dimensional P-splines smooth of the input data and order fixed to be default. These objects are customized for mortality data only. Smoothfit.BIC and Smoothfit.fitLAM objects are returned along with fitBIC.Data fitted values. SmoothenHMDDataset (Xaxis,YAxis,ZAxis,Offset.Param) Arguments Xaxis Vector for the abscissa of data used in the function Mortality2Dsmooth in MortalitySmooth package in R. Here Age vector is value of XAxis. Yaxis Vector for the ordinate of data used in the function Mortality2Dsmooth in MortalitySmooth package in R. Here Year vector is value of YAxis. .ZAxis Matrix Count response used in the function Mortality2Dsmooth in MortalitySmooth package in R. Here Death is the matrix object value for ZAxis and dimensions of ZAxis must correspond to the length of XAxis and YAxis. Offset.Param A Matrix with prior known values to be included in the linear predictor during fitting the 2d data. Here exposure is the matrix object value and is the linear predictor. Details. The method SmoothenHMDDataset in Programs/Graduation_Methods.r smoothens the data based on the death and exposure objects loaded as defined above in step 1. The Age, year and death are loaded as x-axis, y-axis and z-axis respectively with exposure as the offset parameter. These parameters are internally fitted in Mortality2Dsmooth function available in MortalitySmooth package in smoothing the data. Step3: plot the smoothened data based on user input Method Name PlotHMDDataset Description Plots the smoothened object with the respective axis, legend, axis scale details are automatics customized based on user inputs. Implementation PlotHMDDataset (Xaxis,YAxis,MainDesc,Xlab,Ylab,legend.loc,legend.Val,Plot.Type,Ylim) Arguments Xaxis Vector for plotting X axis value. Here the value would be Age or Year based on user request. Yaxis Vector for plotting X axis value. Here the value would be Smoothened log mortality vales filtered for a particular Age or Year. MainDesc Main details describing about the plot. Xlab X axis label. Ylab Y axis label. legend.loc A customized location of legend. It can take values topright,topleft legend.Val A customized legend description details it can take vector values of type string. Val,Plot.Type An optional value to change plot type. Here default value is equal to default value set in the plot. If value =1, then figure with line is plotted Ylim An optional value to set the height of the Y axis, by default takes max value of vector Y values. Details The generic method PlotHMDDataset in Programs/Graduation_Methods.r plots the smoothened fitted mortality values with an option to customize based on user inputs. The generic method DrawlineHMDDataset in Programs/Graduation_Methods.r plots the line. Usually called after PlotHMDDataset method. 3.5 Graphical representation of smoothed mortality data. In this section we shall look into graphical representation of mortality data for selected countries Scotland and Sweden. The generic program discussed in previous section 3.4 is used to implement the plot based on customized user inputs. Log mortality of smoothed data v.s actual fit for Sweden. Figure 3.3 Left panel: Plot of Year v.s log(Mortality) for Sweden based on age 40 and year from 1945 to 2005. The points represent real data and red and blue curves represent smoothed fitted curves for BIC and Lamda =10000 respectively. Right panel: Plot of Age v.s log(Mortality) for Sweden based on year 1995 and age from 30 to 90. The points represent real data red and blue curves represent smoothed fitted curves for BIC and Lamda =10000 respectively. Log mortality of smoothed data v.s actual fit for Scotland Figure 3.4 Left panel: Plot of Year v.s log(Mortality) for Scotland based on age 40 and year from 1945 to 2005. The points represent real data and red and blue curves represent smoothed fitted curves for BIC and Lamda =10000 respectively. Right panel: Plot of Age v.s log(Mortality) for Scotland based on year 1995 and age from 30 to 90. The points represent real data red and blue curves represent smoothed fitted curves for BIC and Lamda =10000 respectively. Log mortality of Females Vs Males for Sweden The Figure 3.5 given below represents the mortality rate for males and females in Sweden for age wise and year wise. 3.5 Left panel reveals that the mortality of male is more than the female over the years and has been a sudden increase of male mortality from mid 1960s till late 1970s for male The life expectancy for Sweden male in 1960 is 71.24 vs 74.92 for women and it had been increasing for women to 77.06 and just 72.2 for male in the next decade which explains the trend. Figure 3.5 Left panel: Plot of Year v.s log(Mortality) for Sweden based on age 40 and year from 1945 to 2005. The red and blue points represent real data for males and females respectively and red and blue curves represent smoothed fitted curves for BIC males and females respectively. Right panel: Plot of Age v.s log(Mortality) for Sweden based on year 2000 and age from 25 to 90. The red and blue points represent real data for males and females respectively and red and blue curves represent smoothed fitted curves for BIC males and females respectively. The Figure 3.5 represents the mortality rate for males and females in Sweden for age wise and year wise. 3.5 Left panel reveals that the mortality of male is more than the female over the years and has been a sudden increase of male mortality from mid 1960s till late 1970s for male The life expectancy for Sweden male in 1960 is 71.24 vs 74.92 for women and it had been increasing for women to 77.06 and just 72.2 for male in the next decade which explains the trend. (https://www.scb.se/Pages/TableAndChart____26041.aspx) The 3.5 Right panel shows the male mortality is more than the female mortality for the year 1995, The sex ratio for male to female is 1.06 at birth and has been consistently decreasing to 1.03 during 15-64 and .79 over 65 and above clearly explaining the trend for Sweden mortality rate increase in males is more than in females. (https://www.indexmundi.com/sweden/sex_ratio.html) Log mortality of Females Vs Males for Scotland Figure 3.6 Left panel: Plot of Year v.s log(Mortality) for Scotland based on age 40 and year from 1945 to 2005. The red and blue points represent real data for males and females respectively and red and blue curves represent smoothed fitted curves for BIC males and females respectively. Right panel: Plot of Age v.s log(Mortality) for Scotland based on year 2000 and age from 25 to 90. The red and blue points represent real data for males and females respectively and red and blue curves represent smoothed fitted curves for BIC males and females respectively. The figure 3.6 Left panel describes consistent dip in mortality rates but there has been a steady increase in mortality rates of male over female for a long period starting mid 1950s and has been steadily increasing for people of age 40 years.The 3.6 Right panel shows the male mortality is more than the female mortality for the year 1995, The sex ratio for male to female is 1.04 at birth and has been consistently decreasing to .94 during 15-64 and .88 over 65 and above clearly explaining the trend for Scotland mortality rate increase in males is more than in females. https://en.wikipedia.org/wiki/Demography_of_Scotland . Log mortality of Scotland Vs Sweden Figure 3.7 Left panel:- Plot of Year v.s log(Mortality) for countries Sweden and Scotland based on age 40 and year from 1945 to 2005. The red and blue points represent real data for Sweden and Scotland respectively and red and blue curves represent smoothed fitted curves for BIC Sweden and Scotland respectively. Right panel:- Plot of Year v.s log(Mortality) for countries Sweden and Scotland based on year 2000 and age from 25 to 90. The red and blue points represent real data for Sweden and Scotland respectively and red and blue curves represent smoothed fitted curves for BIC Sweden and Scotland respectively. The figure 3.7 Left Panel shows that the mortality rates for Scotland are more than Sweden and there has been consistent decrease in mortality rates for Sweden beginning mid 1970s where as Scotland mortality rates though decreased for a period started to show upward trend, this could be attributed due to change in living conditions. Chapter 4 Pricing Life insurance products using mortality rates In the previous chapter 3 we discussed the methodology used in constructing mortality rates from Human Mortality Database and smoothing them using MortalitySmooth package. The smoothed graduated data is then used in life insurance companies to estimate pricing in insurance products like annuity and life insurance. Decline in mortality in general has posed one of the key challenges to actuaries in planning, estimating and designing public retirement and life annuities for smooth functioning of the business. Also, calculation of optimal expected present values required in pricing and reserving of long-term benefits depends on projected mortality values. This process eliminates the scope of future insolvency situations and safeguards from wrong projection of future cost. Therefore, actuaries use lifetables to analyse risk and estimate them efficiently. In this chapter we shall discuss about different methods involved in constructing lifetables and commutation functions using mortality rates. These computed values are used to price different insurance products like annuity, term annuity, deferred annuity, life insurance, term insurance, deferred insurance and so on. 4.1 Life insurance systems and commutation functions In this section we shall briefly describe some of the basic insurance products used in insurance industry and state the respective commutation functions. In view of the fact that, most calculations involves computation of expected present values for death benefits paid to the insurer or periodic annuity payments until death of the policy holder. Thus we define basic notations as follows discounted value for x years, where interest rate i is assumed to be .04 and Expected number of survivors at aged x. We can assume to be 100000 Expected number of deaths between x and x + 1. and and and 4.2 Life annuity Whole life annuity payable in advance Payment of 1 made at the beginning of each year while the policy taken at age x by the policy holder is alive. Whole life annuity payable in arrears Payment of 1 made at the end of each year while the policy taken at age x by the policy holder is alive. = Whole life annuity payable continuously Payment of 1 made at the end of each year while the policy taken at age x by the policy holder is alive. n year Temporary annuity payable in advance Payment of 1 made at the beginning of each year while the policy taken at age x by the policy holder is alive for a maximum of n years. n-year Deferred annuity payable in advance Payment of 1 made at the beginning of each year while the policy taken at age x by the policy holder is alive. The first payment is made to the policy holder at age x + n. The commutation function is given as follows Increasing annuity Immediate annuity due paying 1 now, 2 in next year and so on provided the policy holder is alive when the payment is due. The commutation function is defined as follows. 4.3 Life insurance Whole life insurance Death benefit of 1 payable at end of year of death of a policy holder currently aged x for death occurring anytime in near future. n-year Term insurance Death benefit of 1 payable at end of year of death of a policy holder currently aged x for death occurring within n years. n-year Pure Endowment Benefit of 1 payable at end of n years of period provided the policy holder is still alive. n-year Endowment Benefit of 1 payable immediately on the death of policy holder within n years or at the end of n years if policy holder is still alive at age x + n. This shows it is the sum of n-year term insurance and n-year pure endowment as follows. Increasing Whole life insurance Benefit payable at end of the year of death of the policy holder where the amount of payment is k+1 if policy holder dies between age x+k and x+k+1. The commutation function is defined as follows. 4.4 R program implementation In this section we shall explain the different steps applied to price insurance products. 4.4.1 Construct lifetables and commutation functions. The smoothed mortality data is used to compute other lifetables values such as ,, etc. These vector values are in turn used to construct commutation functions variable values such as , , , , . Finally, Annuity and life insurance products are calculated, plotted and tabulated. CalculateCommFunctions Method Name CalculateCommFunctions Description Construct life table values and commutation function values and returns a list of commutation function variables using as input values. Implementation CalculateCommFunctions (mux) Arguments mux Vector value of smoothened data. Details The function CalculateCommFunctions is used to return computed commutation function values. The is assumed as 100000 and values of is used to compute . These values are looped to calculate respective commutation function variables and returned as a list. Computation and graphical representation of Life insurance products Whole life annuity Method Name ComputeAnnuity.Life Description Returns vector value containing computed annuity life payable in advance. The interest rate is assumed at 4% Implementation ComputeAnnuity.Life (index,CommFunc) Arguments index length of the annuity vector. CommFunc list containing the required values of commutation variable required to compute annuity values. Details The function Calculates life annuity using, vector as input values in the CommFunc parameter as list. Figure 4.1 Plot of age v.s annuity prices for males and females based on year 2000 and age from 20 to 90. The red and blue curves represent smoothed fitted curves for males and females respectively. The Left panel represents plot for Sweden and right panel represents plot for Scotland. From figure 4.1 we infer that annuity prices for males and females in Scotland are more expensive than males and females in Sweden, It is because the mortality rates of Sweden is lesser than mortality rates of Scotland as discussed in Section 3.5. Also In general Males annuity prices are more expensive than females in each country because mortality rates of males are more than the females as discussed in Section 3.5. ComputeWholeInsurance.Life Method Name ComputeWholeInsurance.Life Description Returns vector value containing computed whole insurance life. Implementation ComputeWholeInsurance.Life (index,CommFunc) Arguments index length of the annuity vector. CommFunc list containing the required values of commutation variable required to compute whole insurance values. Details The function calculates whole life insurance using, vector values as mentioned above in previous section. Figure 4.2 Plot of age v.s Whole life insurance prices for males and females based on year 2000 and age from 20 to 90. The red and blue curves represent smoothed fitted curves for males and females respectively. The Left panel represents plot for Sweden and right panel represents plot for Scotland. From figure 4.2 we infer that whole life insurance prices increases as age increases and based on the y axis scales we can infer that Scotland whole life insurance prices are more than the Sweden. In general, females whole life insurance are less expensive than males due to lesser mortality rates as discussed in Section 3.5. Compute Increasing WholeInsurance.Life Method Name ComputeIncreasingWholeInsurance.Life Description Returns vector value containing computed increasing whole insurance life. Implementation ComputeIncreasingWholeInsurance.Life (index,CommFunc) Arguments Index length of the annuity vector. CommFunc list containing the required values of commutation variable required to compute whole insurance values. Details The function calculates whole life insurance using, vector values as mentioned above in previous section. Figure 4.3 Plot of age v.s Increasing Whole life insurance prices for males and females based on year 2000 and age from 20 to 90. The red and blue curves represent smoothed fitted curves for males and females respectively. The Left panel represents plot for Sweden and right panel represents plot for Scotland. From figure 4.3 we infer that whole life insurance prices increases as age increases until 60 and decrease rapidly till age reaches 90 and based on the y axis scales we can infer that Scotland whole life insurance prices are more than the Sweden. In general, females increasing whole life insurance are less expensive than males but converges as age appoaches to 90 this is due to lesser mortality rates as discussed in Section 3.5. Compute Increasing Annuity.Life Method Name ComputeIncreasingAnnuity.Life Description Returns vector value containing computed increasing annuity life. The interest rate is assumed at 4% Implementation ComputeIncreasingAnnuity.Life (index,CommFunc) Arguments Index length of the annuity vector. CommFunc list containing the required values of commutation variable required to compute whole insurance values. Details The function calculates whole life insurance using, vector values as mentioned above in previous section. Figure 4.4 Plot of age v.s Increasing Whole life insurance prices for males and females based on year 2000 and age from 20 to 90. The red and blue curves represent smoothed fitted curves for males and females respectively. The Left panel represents plot for Sweden and right panel represents plot for Scotland. From figure 4.4 we infer that increasing Annuity prices decreases as age increases Also, Scotland increasing Annuity prices are slightly more than the Sweden. In general, females increasing Annuity prices are less expensive than males but converges as age approaches to 90. Conclusions In this dissertation, we set out to show how R packages such as actuar,Mortalitysmooth,MASS can be used to implement aggregate loss claims and human mortality. We used compound distribution to model aggregate claims using actuar and P-splines smoothing techniques to smooth mortality data using Mortalitysmooth package. We finally explained these concepts using real time data such as Danish data and Human Mortality database for Scotland and Sweden and priced life insurance products respectively. In chapter 2 we presented general background to compound distribution in modelling aggregate claim and performed simulation using compound Poisson distribution. Our analysis suggested that Weibull fits the loss claim distribution well using goodness of test fit. Finally we analysed Danish loss insurance data from 1980 to 1990 and used Negative binomial distribution for number of claims and simulated for 1000 samples using Gamma distribution and concluded that Gamma distribution gave a better fit using histogram and chi-square goodness of test fit. In chapter 3 we explained briefly on concepts of graduation, generalised linear models. The smoothening techniques using P-splines were presented and the smoothing parameter was calculated using Bayesian information criterion techniques. We obtained deaths and exposure data from Human Mortality Database for selected countries Sweden and Scotland and implemented mortality rates smoothing using mortalitysmooth package under R. Necessary graphs representing actual data, smoothed mortality data using Bayesian information criteria and smoothing parameter =10000 were presented for the selected countries. We also compared the mortality rates based on various sets such as Males and females for specific country or total mortality rates across countries like Sweden and Scotland for a given time frame ranging age wise or year wise. We finally concluded that mortality rates for Scotland are more than Sweden and in general the mortality rates for males are more than the females. In chapter 4 we looked into various life insurance and pension related products widely used in the insurance industry and constructed life tables and commutation functions to implement annuity values using the smoothed data derived using the methods discussed in chapter 3. We compared and plotted for some of the insurance products and concluded that whole life annuity price decrease as age increases and males annuity prices are more than the females.
Wednesday, May 6, 2020
In history there have been an uncountable amount of plays made, but there have only been two that fully captured the American dream like A Raisin in the sun and Death of a Salesman. In both plays the protagonist is trying to achieve the American dream, but it is near impossible when neither of them has the respect of their superiors or the people around them. It is amazing that two different plays can so closely parallel each other when they have a time gap of over 10 years. Both Miller and Lorraine created a theme of achieving goals, Willy Loman just wanted to earn the respect of the people around him while Walter Younger wanted to get rich quick and support his family. American politician Reubin Askew once said, Ã¢â¬Å"We must stop talkingÃ¢â¬ ¦show more contentÃ¢â¬ ¦But luckily they both have the support of a loving family to help them through it. Ruth Younger was one of the few things that kept Walter sane and their apartment intact, she kept up the apartment and remains emotio nally strong throughout the play, Ã¢â¬Å"goodbye misery! I donÃ¢â¬â¢t ever want to see your ugly face againÃ¢â¬ . A character from Ã¢â¬Å"Death of a SalesmenÃ¢â¬ that is almost identical to Ruth is Linda Loman. Linda nurtured a hurting family all those times when WillyÃ¢â¬â¢s misguided attempts at success miserably failed. She too held together her family with her emotional strength, without her Willy would have broken long before he did in the play. Linda was the one that kept a cool head in heavy situations, when everyone was freaking out she was the one to bring them down to earth. These two women played a huge role in keeping their family together; they knew when the tough times came they were the ones who needed to stay strong. Both plays have a character that gives the families some news they donÃ¢â¬â¢t want to hear. In Ã¢â¬Å"A Raisin in the SunÃ¢â¬ that character is Mr. Karl Lindner; he informs the Youngers that they are unwanted in a neighborhood that they jus t moved in to. He says that because of their ethnicity they will lower the value of the homes around them. Their excitement from finally buying a house of their own was quickly abolished. Howard Wagner was another prime example of someone that gives bad news, or in this case catastrophic news, he was theShow MoreRelated Comparing the American Dream in Millers Death of a Salesman and Hansberrys A Raisin in the Sun3400 Words Ã |Ã 14 PagesComparing the Destructive American Dream in Millers Death of a Salesman and Hansberrys A Raisin in the Sun America is a land of dreamers. From the time of the Spanish conquistadors coming in search of gold and everlasting youth, there has been a mystique about the land to which Amerigo Vespucci gave his name. To the Puritans who settled its northeast, it was to be the site of their Ã¢â¬Å"city upon a hillÃ¢â¬ (Winthrop 2). They gave their home the name New England, to signify their hope for aRead MoreMarketing Management 14th Edition Test Bank Kotler Test Bank173911 Words Ã |Ã 696 Pagesbusiness market B) global market C) nonprofit market D) consumer market E) exclusive market Answer: C Page Ref: 9 Objective: 2 Difficulty: Easy 19) Which of the following is true of business markets? A) Buyers are usually not skilled at comparing competitive product offerings. B) Buyers have limited purchasing power. C) Property rights, language, culture, and local laws are the most important concerns. D) Products sold in such markets are usually highly standardized. E) Business buyersRead MoreDeveloping Management Skills404131 Words Ã |Ã 1617 PagesConflict 375 SKILL LEARNING 376 Interpersonal Conflict Management 376 Mixed Feelings About Conflict 376 Diagnosing the Type of Interpersonal Conflict 378 Conflict Focus 378 Conflict Source 380 Selecting the Appropriate Conflict Management Approach 383 Comparing Conflict Management and Negotiation Strategies 386 Selection Factors 386 Resolving Interpersonal Confrontations Using the Collaborative Approach A General Framework for Collaborative Problem Solving 391 The Four Phases of Collaborative Problem Solving
Tuesday, May 5, 2020
Question: Describe the strategies that, you as a nurse, can implement to reduce the severity and impact of the disease for the individual and their family? Answer: Osteoarthritis(OA) is also known asdegenerative joint disease, degenerative arthritisoror osteoarthrosis, is a type of abnormalities which involves joints degradation, as well as subchondral bone and articular cartilage.The purpose of the assignment is to develop an instrument so as to assess the difficulties faced by the people in their daily lives due to the Osteoarthritis of the Knee (Biological measurement of osteoarthritis, 1993). The symptoms of the disease and the strategies that I will take as a nurse are highlighted in the assignment. Pathophysiology Osteoarthritis (OA) is considered a progressive joint disease and causes disability. It can occur to people of any age especially who are 45 years and above are more likely to be affected by the disease. The Arthritis Foundation says that that more than 27 million people living in the United States are have the chances of having osteoarthritis, where the knee is more likely area to be affected area. It was also seen that the women are more prone to osteoarthritis than the men. The disease is characterized by the loss of the arterial cartilage, subchondral sclerosis of the bone, formation of osteophyte and cysts. The remodeling of the subchondral bone plays an important role in the OA Pathophysiology (14 Evaluating pain in osteoarthritis, 2005). It is often called Osteoprotegerin (OPG). As the draining down of the hyaline cartilage progresses, hypertrophic changes can occur in the synovial tissue and also the underlying bone with which can form sclerosis and osteophyte. This often lea ds to narrowing of space of the joints and joint surface become rough and irregular which ultimately causes pain, and swelling in the joint deformity (Biological measurement of osteoarthritis, 1993). Symptoms The symptoms of the disease are severe pain in the knee, with dysfunction and discomfort in the area. Thus it can be said that the pain is the initial symptom of the disease. Osteoarthritis causes pain in the knee area and reduces the motion of the person affecting the quality of life. As time passes, the condition of the knee detoriate. There is no definite treatment for the disease, and it is managed by the controlling the pain and preserving the function of the affected area (Knee taping reduces symptoms associated with osteoarthritis, 2003). The control of the life style, changes in the diet, exercises, and the use of medication and orthosis can control the pain and improve the condition to some extent. In extreme stages it can be seen that the only possible way out is knee replacement. Treatment and care by nurses It can be said that the task of the nurses is to control the pain of the knee and the joint and to promote the quality of the life of the people who are suffering from the disease. As a nurse I can help the people by guiding them and letting them know the signs and symptoms of the disease. They can be guided with education so that they can take care of themselves in case of need by doing simple exercises (Ringdahl, Erika and Pandit, 2011). The nurses instruct the patients reduce their weight and use devices that can be used to support their walking like a walker or a walking stick. I as a nurse can encourage the people in active participation to control the progress of the disease. The nurses can teach the patients exercises and the use of various aid which will help the people to keep their knees in the state of motion (gutierrez, 2013). Moreover physiotherapy can help the people to reduce the level of pain and improvements can be seen. The nurses can follow up the medication prescr ibed by the doctors and monitor the improvements and also can implement new exercises if it is necessary. References 14 Evaluating pain in osteoarthritis. (2005).Osteoarthritis and Cartilage, 13, pp.S5-S5. Biological measurement of osteoarthritis. (1993).Osteoarthritis and Cartilage, 1(1), pp.21-21. gutierrez, L. (2013). Improvement of symptoms in the generalized osteoarthritis use of zoledronic acid zola study.Osteoarthritis and Cartilage, 21, p.S293. Knee taping reduces symptoms associated with osteoarthritis. (2003).BMJ, 327(7407), pp.0-c-0. Ringdahl, Erika, and Pandit, S. (2011). Treatment of Knee Osteoarthritis.American Family Physician, 83(11), pp.1287-1292.
Saturday, April 18, 2020
Zyrtec Essay Zyrtec also known as Cetirizine Hydrochloride is a second-generation anti-histamine that is generally used by children and adults to treat indoor or recurrent allergic rhinitis also known as hay fever or commonly known as allergy. It is also used to treat outdoor orÃ Ã seasonal allergic rhinitis and Ã¢â¬ËlivesÃ¢â¬â¢ or chronic urticaria in children over two years of age.Allergies especially in children are characterized by red itchy eyes, itchy runny nose and sneezing. In the US it is the number oneÃ Ã most prescribed branded antihistamine with approximately $1,287 million sales in U.S alone.CompanyThe company that manufactures and distributes Zyrtec is the renowned Pfizer U.S pharmaceutical Company. The U.S pharmaceutical giantÃ¢â¬â¢s guiding motto is Ã¢â¬Å"working together for a healthier worldÃ¢â¬ .Ã As the company puts it, it is inspired by a single goal, Ã¢â¬Å"your healthÃ¢â¬ .Ã Ã¢â¬Å"They are dedicated to developing new safe medicines to prevent and treat the worldÃ¢â¬â¢s most serious diseasesÃ¢â¬ .Therefore as the motto portrays, the companyÃ¢â¬â¢s goal is to delight its customers though innovative and technologically advanced medicine and treat most human ailments. The major commitment that the company has is to be Ã¢â¬Å"the global leader in health careÃ¢â¬ by bringing change to all people across the globe.The company would achieve this through provision of access to effective safe yet affordable medicines to those who need them. To also continue maintaining a leading role and to satisfy both customers and shareholders, the company is committed to remain focused on improving continually how they do business. This is done through transparency, and forging a strong relationship with its clients to enable the organization develops the abilities to listen and make appropriate decisions thereof.Charles Pfizer and Charles Errant who were cousins founded Pfizer Inc. in 1849. Ever since the company has co ntinued to evolve to keep pace with the ever changing stakeholder, customers and the societyÃ¢â¬â¢s expectations. The two cousins who were German chemists and entrepreneurs borrowed $2,500 from PfizerÃ¢â¬â¢s father and bought a red brick building in Brooklyn New York at the Williamsburg section. This building doubled up as an office, laboratory factory and warehouse.Santonin was the first of the company launches. Santonin was palatable anti-parasitic drug to treat intestinal worms, which was a common ailment in America in the mid 19th century. Santonin was an instant hit and the company was officially launched following this success. Pfizer continued on producing quality products especially in the areas of painkillers and other vital drugs.During the civil war of 1862 its sales soared because they ventured into domestic and cleaning agents, which also included other drugs that were in demand from the Union Army. Some of these drugs included such important drugs and cleanin g agents as iodine, morphine mercurial and camphor.By 1880 Pfizer had become AmericaÃ¢â¬â¢s number one citric acid producer, which was to become its main product. This was because new drinks like coca-cola, Pepsi-cola and Dr. Ripper gained popularity and had citric acid as an ingredient. But perhaps the greatest breakthrough the company ever came of with is the production of penicillin through deep tank fermentation. Penicillin was to be known as the Ã¢â¬Å"real defense against bacterial infectionÃ¢â¬ and there was no other drug like it before. This was a turning point not only for Pfizer as a company but for the whole human history as well, this was in 1944. Pfizer would again become the leading producer of Vitamin C or Ascorbic acid in the world in 1936 and by the late 1940s it became an established leader in the production of a wider range of vitamins. By 1950, Pfizer had established one of the best sales and marketing organization in the pharmaceutical industry and by 19 51 the company underwent a major international expansion and expanded to such countries like Brazil, Canada, Cuba, Panama, Puerto Rico, England and Mexico.By giving its international managers the autonomy to make decisions accelerated its growth across the globe. In 1952 the company established an agricultural division especially targeting animal health.Through 1995 the company maintained its growing streak and in this particular year acquired SmithKline BeechamÃ¢â¬â¢s animal health business giving it the leading role in production of livestock and animals pharmaceuticals. By 1997 it was named the worldÃ¢â¬â¢s most admired pharmaceutical company by the well-recognized Fortune Magazine.This was to be followed by the breakthrough invention for erectile dysfunction drug Viagra that took the world by storm.In 1999 while celebrating its 150th anniversary it was again named the Ã¢â¬Å" company of the yearÃ¢â¬ by Forbes Magazine and in 2000 the merger between Pfizer and Warner-Lambert created the Ã¢â¬Å"worldÃ¢â¬â¢s fastest growing major pharmaceutical companyÃ¢â¬ . Also, through its program of Share Card Program, it provided qualified low-income Medicare to low income Americans. One of the latest innovations coming from Pfizer is a prescription medicine that enables adults cease smoking, Chantix was launched in 2006 again a first in this category and just like Viagra it took the world by storm.With such an attractive and impressive track record marketing of Zyrtec would not be a very difficult thing to do especially is the US where the companyÃ¢â¬â¢s presence is well known and felt. To be able to understand clearly about the marketing aspect of Zyrtec it is very important to carry out a SWOT analysis that seeks to identify the strengths, weaknesses, opportunities and threats that are likely to face the organization in its quest to market the brand.StrengthsThe major strength that is easily discernable is the formidable background that th e company has created for the last 158 years. The background can be described as one of the most impressive for any organization. Through its innovativeness, creativity and solid background the company has continued to excite not only America but the whole world as well with the innovation of such first ones like penicillin in the 1920s, vitamin C in the 1950s, Viagra in the 1998s and many more life changing drugs. With such a track record the company is bound to be easily noticed.The pharmaceutical industry is a very sensitive industry because it deals with human health. A case of product backfiring would have a devastating effect on any organization. This notwithstanding, Pfizer as company because of its commitment to quality has created a Ã¢â¬Å"halo effectÃ¢â¬ on most of its range of products. The company is trusted by almost all its consumers and has created itself as a brand and a household name in the pharmaceutical industry.The company has established one of the best and technologically advanced laboratories that it continues to generate some of the best drugs to treat many major ailments.Another strength that the company has is its global presence where it exists in most major countries of the world. Some level of autonomy where respective managers are given a freehand to make quick decisions regarding the markets they represent characterizes its international presence. This is very important bearing in mind the dynamic world market. PfizerÃ¢â¬â¢s solid base that is matched with colorful and rich backgrounds enables the company to have an upper edge over its competitors.The companyÃ¢â¬â¢s backgrounds enables it to remain ahead of the pack especially in the consumerÃ¢â¬â¢s mind, who would likely entrust their health and those ones of their animals to such a solid company. With such catchy and unique innovations of the Viagra, enables the company to cut down on its promotion budget because such brands automatically get headline spaces in local and international and media outlets.Zyrtec is perhaps one of the companies leading anti-histaminic drug in America. Likely, the leading role can be directly attributed not only to the effectiveness of the brand but also PfizerÃ¢â¬â¢s record [F1]Ã as a company as well. Zyrtec being the leading antihistamine enables the company to cut down on its marketing budget and concentrate much on ensuring that the brand is well supported by making the drug better and readily available. This was to be reinforced by the over the counter authorization by the FDAIn any case the kind of marketing the company would likely to support is that one that ensures the brand does not loose the market share and not necessarily push for more sales. In marketing, brand loyalty is extremely important and Zyrtec as it is, has maintained a leading role in the U.S market. For any competitor to drive it out or to eat into itÃ¢â¬â¢s market would be a very daunting task.Counterfeits are one of the greatest threa ts in the pharmaceutical industries. This is because once a certain drug is counterfeited its quality of efficacy is brought into disrepute. To protect its consumers and ensure safe and effective medicines the company has continued to identify and employ innovative counterfeit proof mechanism a very good example is the Radio Frequency Identification (RFID) a first one in the industry. Such innovations ensure safety and quality are transferred to the consumers. Zyrtec, when first launched was a prescription drug. This changed in November 19, 2007 when the FDA approved sales over the counter meaning one can easily buy the drug without any prescription. Undoubtedly this is likely to boost the sales as one can get prescription online and many pharmacies have continued to push it via Internet.WeaknessesZyrtec is indeed a leading anti-histamine drug for the treatment of allergies in the world market. However this is bound to change it Pfizer does not do something about the side ef fects that the drug has on its patients. According to Askapatient database on a survey carried since 23/05/07 up to 9/21/2008 where 583 users of Zyrtec responded, the ratings were not very promising. Out of a scale of 1-5, Zyrtec is rated at 2.3 not a very good rating especially bearing in mind the publicity it gets in the U.S market.The drug it seems is receiving negative ratings from consumers some indicating severe side effects like weight increase, low libido, panic attacks and so on. In most situations companies of PfizerÃ¢â¬â¢s size are likely to ignore such comments or ratings yet it would not be business as usual. Competitors are likely to take advantage of this anomaly to penetrate the market. This is already happening with the development of Xyzal, which is using the weakness as an entry pointOpportunitiesPfizer as the manufacturer of Zyrtec has what it takes to push the allergy drug. With a well-developed network across the world makes the company the most ideal to effectively push its own brand. And the advantage that comes to fore is its various productions centers in Brazil, U.K and South Africa where it has the capacity to distribute and market Zyrtec to new markets within a very short time. Asia and Africa are a very attractive markets that it could choose to venture and establish itself as a renown brand name.With its strong base including an advanced research and development laboratory it can easily invent and role out new drugs on this particular line that would be best suitable for these areas. By riding on the crest of the good performance realized from its previous product lines, Pfizer is likely to penetrate such markets with ease as compared to other companies that are not well known in this areas. A good example of the various drugs that it can ride on includes Viagra that address erectile problems, and Chantix that enables smokers stop smoking.Pfizer also needs to utilize the online resources to propel its growth. Internet is tu rning out to be the single most important tool in the contemporary world that has the capacity to reach within a short time the target market. It is easy for the company to prescribe online and utilize powerful e-commerce organizations.However, there are certain risks that could be involved by utilizing the Internet. ThereÃ¢â¬â¢s also a great opportunity that is being through the Internet. There seems to be a great number of sites that are offering Zyrtec online. Most likely such sites are private companies that are selling the products on behalf of Pfizer, this is a great boost especially on cutting on the distribution, marketing and advertising budget.ThreatsPfizer has been in existence for over a century and a half. Its dominance in the world market has continued to attract intense competition. Zyrtec as a brand has attracted quite a formidable competition from U.C.BÃ¢â¬â¢s Group another global pharmaceutical giant that produces Xyzal.Xyzal is a brand in the line of Zyrte c because it is anti-histamine. Xyzal is better placed to compete with Zyrtec because it has been developed in such a way that it does not have any side effects on the patients unlike Zyrtec. Xyzal was launched in 2001 and became the number one anti-histamine drug in Europe beating Zyrtec. However, Zyrtec has continued to dominate the U.S market. With the claims of lack of any side effect as opposed to Zyrtec, Xyzal is likely to get into the US market and utilize its success in the European market to establish itself into the U.S market. ZyrtecÃ¢â¬â¢s sales were set to decline to 3% for the first quarter of 2004. This indeed is a gloomy picture for Pfizer. Another threat that would likely affect the growth of Zyrtec is the sticky issue of counterfeiting. Some factors have contributed greatly to the rise in this vice. These factors include the raise in the number of distributors in the medicine supply chain. Such distributors include under regulated wholesalers, increase in the number of internet pharmacies, repackages, technology advancement enabling criminals to counterfeit easily, lack of strict legislative measures in other countries. ConclusionZyrtec just like other Pfizer brands has continued to dominate the American market for some time. Pfizer as company has built quite a reputation in this market earning accolades for it innovativeness and quality products. The company has continued to dominate this particular market because of the experience and its ability to satisfy and surpass customer expectations.Zyrtec on its part is indeed a wonderful antihistamine going with its performance track record, however it stands a great risk from the competition especially Xyzal which is giving it a run for its money in the European market. Pfizer would strongly rely on its reputation to wade of any form of competition, however it is important for the company to address the complaints of the side effects from the consumers.
Saturday, March 14, 2020
The Pros and Cons of Freelance Writing Online The Pros and Cons of Freelance Writing Online The Pros and Cons of Freelance Writing Online By Guest Author Freelance writing online is often touted as a dream job. It certainly has its benefits, but it has its downfalls as well. In fact, the drawbacks to freelance writing are often the flip side to the positives of the profession. The Pros 1. Your schedule allows for a fair bit of flexibility. Because you are essentially working for yourself, the job allows you to write in just about any location: your home office, a neighborhood cafe, the beach (so long as your computer doesnÃ¢â¬â¢t run out of batteries and the wireless connection isnÃ¢â¬â¢t hampered)anywhere you want. Need a day off? No problem. There is no boss to check in with, and so long as you meet your deadlines and set time to deal with the backlog upon your return, youÃ¢â¬â¢re all set. 2. You have a multitude of clientsand can walk away from one if itÃ¢â¬â¢s not working with you. Most of us have had a boss that made our life miserable at some point in time. I used to dread going to work, and tried to plan my day around avoiding this person. Writing online allows you to work with a multitude of clients, so an occassional bad experience does not ruin your life. You also have the ability to stop working with a client if things arenÃ¢â¬â¢t copasetic. 3. You can make an unlimited amount of money. IÃ¢â¬â¢ve certainly met my share of six-figure freelancers, and those who make a decent living working part-time hours. Although there is a cap to the amount of writing you can do (and the pay per word or article you will find), publishing a book, developing a product, teaching workshops (in person or online) or speaking at live events are always options. Unlike working for a company or individual who keeps the hard-earned money you bring into the business, you are rewarded monetarily for your own innovation. 4. You are responsible for your own successes. This can be incredibly empowering and gratifying, especially over time. To see a viable, lucrative business, self-taught and self-made, develop and thrive from a simple fleeting idea thereÃ¢â¬â¢s nothing like it. The Cons 1. Your work can take over your life, if you let it. My partner in crime works normal business hours, and his presence keeps me somewhat sane. I know this because when heÃ¢â¬â¢s left for business trips, IÃ¢â¬â¢ve found myself sitting in a pile of papers and reference materials until the week morning hours. If you allow distractions (such as the telephone or social media) to creep in while youÃ¢â¬â¢re working, the boundaries can become inseparable and you can find yourself whittling the night away with little to show for it. Accepting an occasional last-minute project (particularly when rush fees are provided) and having a week or two with a heavier workload than normal is one thing. However, not creating space devoid of work (and being fiercely protective of it) is a recipe for burnout. 2. You will likely have less-than-savory interactions with clients and editors. Some are simply communication problems. Having to rewrite some copy because your client didnÃ¢â¬â¢t explain what they wanted in the first place, for example, can sometimes be prevented by getting very specific, detailed instructions. But some people are impossible to please, it seems, or perhaps they need to find a different writer. Disorganized editors who lose things and ask for them to be resent ad nauseum, people who take your ideas and run with them (without hiring you) and general poor manners and frustrating behavior is common. 3. You have to sort through the rubble. For every client that pays you a reasonable hourly fee, there will be ten who expect you to work for pennies (or worse, for free). Perhaps due in part to the proliferation of content mills, many writers have no problem working for cheap. There is certainly money out there for freelance writing online, but you have to look for it. 4. You are responsible for your own failure. No more going into work exhausted because you stayed up late reading Harry Potter, or checking your e-mail during work hours and picking up a paycheckunless youÃ¢â¬â¢re prepared to see the outcome in your bottom line. Freelance writing isnÃ¢â¬â¢t just a dream job. ItÃ¢â¬â¢s work. About the Author: Yael Grauer is a freelance writer and editor. She also provides proofreading and copyediting services to small businesses and creative entrepreneurs, to help spiff up their e-books, online courses and web copy. Find her at YaelWrites.com. Want to improve your English in five minutes a day? Get a subscription and start receiving our writing tips and exercises daily! Keep learning! Browse the Freelance Writing category, check our popular posts, or choose a related post below:What Is Irony? (With Examples)Latin Words and Expressions: All You Need to KnowHow to Address Your Elders, Your Doctor, Young Children... and Your CEO
Wednesday, February 26, 2020
The 17th and 18th centuries were a time of great political change in Europe - Research Paper Example To begin with, Hobbes being a scholar, his main aim was to place politics on a scientific grip, hence employing a strict logical method to his work. In contrast, Machiavelli having worked as one of the civil servants of Florentine Republic drew his conclusions, after observing how people behaved instead of how they should behave in an intangible and hypothetical world2. It is this methodology difference, which leads to differing political views of these two authors. Hobbes, writing Leviathan immediately after the end of civil war and unfruitful tries at republicanism in Europe, held less estimation of the nature of human beings that Machiavelli. According to Hobbes, if two individuals have desire of a certain thing, which they cannot enjoy at the same time, then they become enemies3. Hobbes argued that, people living in such state were continuously at war, and they could not differentiate between right and wrong. The two lived a life that was poor, solitary, short, brutish, and nasty. Resulting from his reductionist methodology, taking societal analysis to a position of human nature, he made his conclusion with a main realist assumption (anarchy). In such a state, every individual has his or her natural right for protection against injury or harm4. Therefore, HobbesÃ¢â¬â¢s arguments purports that there must be fundamental laws to avoid the state of war. On contrary, Machiavelli does not reflect a theoretical state of nature like Hobbes. Howev er, Machiavelli argues, Ã¢â¬Å"there is no secret hand, which brings human activities into natural harmonyÃ¢â¬ . Another factor differentiating these two political theorists is their diverging views about governance. According to Hobbes, it was the speculation on how a society functions without set rules. Hobbes felt that individuals would simply be doing things according to their own interests. Regarding how people should act, Hobbes makes
Monday, February 10, 2020
Cultural Exploration - Term Paper Example These cultural patterns, which include beliefs, values, norms and social practices, do affect the quality of communication that takes place as the people from these different cultures interact. The result is either an enhanced quality of communication or a total breakdown in communication. To avoid this breakdown in communication, and to enhance the quality of communication between the different parties, it is very important for the two people who are communicating to be aware of the various attributes of the other culture. This way, mistakes that can be avoided and that can cause a breakdown in communication are identified. The cultural patterns of the two cultures might either lead to a competent intercultural communication or problems as far as the communication is concerned. This paper is going to examine the cultural patterns of two cultures and how the interplay between these patterns affects the intercultural communication of people from these cultures. The first culture is that of the writer, Southeastern Georgia Caucasian, and that of Chinese. Throughout this paper, the writer will be guided by one major objective. This will be the exploration of the various potential effects of the cultural patterns of Southeastern Georgia Caucasian and Chinese on intercultural communication between the two. To achieve the major objective, the writer will be guided by several specific objectives. It is through the address of these specific objectives that the writer will have effectively dealt with the major one. These specific ones are as follows: 1. An analysis of cultural value orientations of southeastern Georgia Caucasian and Chinese using the globe taxonomy approach 2. An analysis of verbal communication norms of the two cultures 3. An analysis of non-verbal communication norms of the two cultures 4. An analysis of relational communication norms of the two cultures The writer will not merely describe the cultural patterns of the two cultures picked. Rather, this will be a comprehensive analysis of the effects that the interplay between this set of pattern has on intercultural communication. Does it make it more productive or does it make it less productive and problematic 1: Cultural Value Orientations of Georgian and Chinese Cultures This analysis will be conducted using the GLOBE cultural taxonomy approach. This approach identifies nine dimensions of culture that are regarded important or ideal in a particular culture. These nine dimensions are a description of what people actually do, or what Millet (1) refers to as cultural practices. They also identify cultural values or what is regarded as ideal practice or conduct in the society under examination (Millet: 1). Power Distance Dimension Power distance dimension describes the degrees to which members of the society that are less powerful both "expect and accept that power is distributed unequally" (Leadlay & Jomy: 38). It is a fact that in any one society, there are differences in the distribution of power, resulting in powerful individuals and less powerful ones. But the differences between the societies occur because, to some, as much as the power