Questions: 1) The file attached (Excel: Mortality Data) contains mortality information from a nursing home during the year 2015. The variable “died” indicates that if the patient died before the end of the year. Given this data, develop a logistic regression model, which predicts probability of death of a guest during any year by the end of that year, given the age and the dummy variable “gender.” -Use the tool (Logistic Regression Calculator) on this link to solve question 1: https://stats.blue/index.html 2) The personnel director of a firm has developed two tests to help determine whether potential employees would perform successfully in a particular position. To help estimate the usefulness of the tests, the director gives both tests to 43 employees that currently hold the position. Table 5 gives the scores of each employee on both tests and indicates whether the employee is currently performing successfully or unsuccessfully in the position. If the employee is performing successfully, we set the dummy variable Group is set equal to 1; if the employee is performing unsuccessfully, we set Group equal to 0. Let x1 and x2 denote the scores of a potential employee on tests 1 and 2. Perform a discriminant analysis on the data and interpret the result, including the confusion matrix. Include all required steps in assessing the final model. By trial and error, find the threshold, which minimizes prediction relative error. -Use the file attached (Question2) as reference for question2.

Question 2 What is the crude mortality rate?Age-GroupPopulationNumber of Deaths<3015,0002030-6517,00055>656,000155a.230b.6.1 per 1,000c.8.6 per 1,000d.6.1 per 10,000Question 3 The age-specific death rate for the over-65 age group isAge-GroupPopulationNumber of Deaths<3015,0002030-6517,00055>656,000155a.155b.25.8 per 1,000c.1.55 per 10,000d.25.8 per 10,000Question 4 Calculate the relative risk of stroke of male smokers to male nonsmokersStrokeSmokersYesNoTotalYes1713,2643,435No1174,3204,437Total2887,5847,872a.1.54b.1.88c.2.08d.None of the above is correctQuestion 5 Calculate the odds ratio of having a stroke in men who smoke to those who do not smokeStrokeSmokersYesNoTotalYes1713,2643,435No1174,3204,437Total2887,5847,872a.1.93b.1.88c.1.78d.1.34Question 6 Is the following interpretation of the odds ratio true or false?The odds of having a stoke are 1.93 times higher in men who smoke than in men who do not smokeStrokeSmokersYesNoTotalYes1713,2643,435No1174,3204,437Total2887,5847,872 True FalseQuestion 7 A new type of test, Generation A, was given to 500 individuals with suspected diabetes, of whom 320 were actually found to have diabetes. The results of the examination are presented in the following table:Generation A ResultDiabetesTest ResultPresentAbsentPositive30050Negative20130Compute the sensitivity and specificity of the findings shown for Test A.a.Sensitivity = 93.7%, Specificity = 72.2%b.Sensitivity = 96.7%, Specificity = 70.2%c.Sensitivity = 95.7%, Specificity = 76.2%d.Sensitivity = 91.7%, Specificity = 78.2%Question 8 A new type of test, Generation A, was given to 500 individuals with suspected diabetes, of whom 320 were actually found to have diabetes. The results of the examination are presented in the following table:Generation A ResultDiabetesTest ResultPresentAbsentPositive30050Negative20130Compute the positive and negative predictive values of the findings shown for the test.a.Positive Predictive value = 82.3% , Negative Predictive Value = 87.5%b.Positive Predictive value = 85.7% , Negative Predictive Value = 86.7%c.Positive Predictive value = 80.1% , Negative Predictive Value = 82.2%d.Positive Predictive value = 77.3% , Negative Predictive Value = 79.3%Question 9 From the following scatter plot, we can say that between y and x there is _______a.Perfect positive correlationb.Virtually no correlationc.Positive correlationd.Negative correlation7 points Question 10 A Director of Human Resources is exploring employee absenteeism at the INCOVA Hospital. A multiple linear regression analysis was performed using the following variables. The results are presented below.VariableDescriptionYnumber of days absent last fiscal yearx1commuting distance (in miles)x2employee's age (in years)x3length of employment at PPP (in years)CoefficientsStandard Errort Statisticp-valueIntercept6.5941463.2730052.0147070.047671x1-0.180190.141949-1.269390.208391x20.2681560.2606431.0288280.307005x3-2.310680.962056-2.401820.018896R=0.498191R2=0.248194Adj R2=0.192089se = 3.553858n = 73What is the regression equation based on this analysis?a.Y = 0.18 x1 + 0.27 x2 –0.51 x3 b.Y = 6.59 – 0.18 x1 + 0.27 x2 c.Y = 6.59 – 0.18 x1 + 0.27 x2 – 2.31x3 d.None of the aboveQuestion 11 A Director of Human Resources is exploring employee absenteeism at the INCOVA Hospital. A multiple linear regression analysis was performed using the following variables. The results are presented below.VariableDescriptionYnumber of days absent last fiscal yearx1commuting distance (in miles)x2employee's age (in years)x3length of employment at PPP (in years)CoefficientsStandard Errort Statisticp-valueIntercept6.5941463.2730052.0147070.047671x1-0.180190.141949-1.269390.208391x20.2681560.2606431.0288280.307005x3-2.310680.962056-2.401820.018896R=0.498191R2=0.248194Adj R2=0.192089se = 3.553858n = 73Which of the following interpretations is correct?a.For every additional year in the employee's age, the average number of absent days in the last year significantly (p-value<0.05) increases by 0.27 days. b.For every additional year in employee’s length of employment, the average number of absent days in the last year significantly (p-value<0.05) decreases by 0.51 days. c.None of the above is correct.6 points Question 12 A Director of Human Resources is exploring employee absenteeism at the INCOVA Hospital. A multiple linear regression analysis was performed using the following variables. The results are presented below.VariableDescriptionYnumber of days absent last fiscal yearx1commuting distance (in miles)x2employee's age (in years)x3length of employment at PPP (in years)CoefficientsStandard Errort Statisticp-valueIntercept6.5941463.2730052.0147070.047671x1-0.180190.141949-1.269390.208391x20.2681560.2606431.0288280.307005x3-2.310680.962056-2.401820.018896R=0.498191R2=0.248194Adj R2=0.192089se = 3.553858n = 73Which of the following statements is correct about the R2?a.The adjusted R2 value is 0.25. This means that the model explains around 25% of the variation in the average number of days absent in the last year. b.The adjusted R2 value is approximately 0.19. This means that the model explains around 19% of the variation in the average number of days absent in the last year. c.The adjusted R2 value is 0.50. This means that the model explains around 50% of the variation in the average number of days absent in the last year. d.None of the above is correct.Question 13 The following graph of a time-series data suggests a _______________ trend.a.linearb.quadraticc.cosined.tangential7 points Question 14 Fitting a linear trend to 36 monthly data points (January 2000 = 1, February 2000 =2, March 2000 = 3, etc.) produced the following tables.CoefficientsStandard Errort Statisticp-valueIntercept222.37967.358243.3014380.002221x9.0090663.174712.837760.00751dfSSMSFp-valueRegression1315319.3315319.38.0528850.007607Residual34133130639156.07Total351646626The projected trend value for January 2003 is ________.a.231.39b.555.71c.339.50d.447.766 points Question 15 Using a three-month moving average, the forecast value for November in the following time series is ____________.July5Aug11Sept13Oct6a.11.60b.10.00c.9.67d.8.606 points Question 16 When forecasting with exponential smoothing, data from previous periods is _________.a.given equal importanceb.given exponentially increasing importancec.ignoredd.given exponentially decreasing importanceQuestion 17A time series with forecast values and error terms is presented in the following table. The mean absolute deviation (MAD) for this forecast is ___________.

In what way might an average be misused? Alternatively, how might an average be misinterpreted? For instance, how can a misinterpreted average pertain to stereotyping? How can we avoid misuse or misinterpretation of averages? Provide a specific example to illustrate your explanation.