BiostatisticsAnswer the following questions. Copy and paste any required data charts or summaries into this Word document.Include the file naming convention.I.Descriptive Statistics:Download the data set Final_1.sav. Complete the following:1) List the level of measurement for the variables AGE SEX AGEGRP SBP1 in the data set and describe the appropriate numerical and descriptive statistics based on these.Record NumberAGE132113154465146357468359401029112212162) Calculate (by hand) the mean and standard deviation for the first 12 records for age in the data set.3) Generate numerical and graphical descriptive statistics for each of the variables namely AGE SEX AGEGRP and SBP1.4) Interpret the output you generated in part 2 for each of the variables in the data set.I.Paired and Independent ttests:Download the data set Final_2.sav and use SPSS to complete the following calculations:1) Use the 5-step approach to hypothesis testing and the calculation of the 95% confidence intervals to answer the following research question: Was a significant difference in Systolic Blood Pressure (SBP) observed over the course of the study?2) Use the 5-step approach to hypothesis testing and the calculation of the 95% confidence intervals to answer the following research question: Is there a difference in SBP1 based on HIV status? (Hint: Assign Y as group 1 and N as group 2)II.Cross-Tabulation:III. Download the data set Final_3.savand use SPSS to complete the following calculations.1) Use the 5-step approach to hypothesis testing to answer the following research question:2) In the sample provided in Final_3.sav are the variables income and Bladder Cancer independent of each other? (Note: The question could also be asked: Is there an association between the variables because the lack of independence implies an association)?2) Answer the following based on the cross-tabulation of alcohol consumption and Bladder Cancer:Alcohol consumption * Bladder Cancer CrosstabulationCountBladder CancerTotalNoYesAlcohol consumption”Less than 1 drink per week”3054844 or more drinks per month22115137Total52169221Calculate the odds ratio.Describe how the odds ratio differs from the relative risk or risk ratio and why you would chose it here.Interpret the odds ratio and how it might impact the practice of public health practitioners.If you wanted to know whether this relationship was statistically significant what test(s) could you use?IV.ANOVA:Download the data set Final_4.sav and use SPSS to complete the following calculations.1) Produce box plots of income for each region of the US in the data set and interpret them. Based on the box plots do you expect to find a difference between any of the groups?2) Create descriptive statistics for each region using the variable income.Include skewness and kurtosis in the output.Create a histogram for each group.3) Run the ANOVA for income based on region. Include the ANOVA table and the test forHomogeneity of Variance. Interpret the results.5) Conduct post hoc analysis using Bonferroni and LSD methods to control for multiple testing.Provide the output.Interpret your results.Why do you need to use methods like Bonferroni and LSD with the ANOVA?V.Regression:VI. Download the data set Final_5.sav and use SPSS to complete the following calculations.1) Use an independent t test and simple linear regression to identify whether a relationship exists between gender and BMI.Run the appropriate t test in SPSS report the significance of the difference in means and the confidence interval and interpret the results.Run the simple linear regression in SPSS report the significance of the variable gender and the overall fit of the model (using r2). Interpret the results.How are these two approaches different?Are your conclusions the same using both tests?2) Answer the questions using the provided output:Multiple Linear RegressionResearchers looked at the Emergency Department Records of 60 adults ages 22 to 46 years who arrived in the ED complaining of chest pain during a 6 month period of time. They did not use a random sample as they wanted 30 males and 30 females in the study. They collected information on BMI (a measure of overweight/obesity) Age SBP (Systolic Blood Pressure) and the diagnosis of Diabetes. Their first hypothesis (alternative) was that the dependent variable SBP is associated with BMI Age Diabetes and Gender. They conducted a multiple linear regression to test their hypothesis. Here are the results (note that they had two models and chose to use the second one):Model SummarycModelRR SquareAdjusted R SquareStd. Error of the Estimate1.796a.634.6085.4432.792b.627.6075.445a. Predictors: (Constant) Diabetes Age Gender BMIb. Predictors: (Constant) Age Gender BMIc. Dependent Variable: SBPANOVAcModelSum of SquaresdfMean SquareFSig.1Regression2824.9684706.24223.839.000aResidual1629.4085529.626Total4454.376592Regression2794.2223931.40731.418.000bResidual1660.1555629.646Total4454.37659a. Predictors: (Constant) Diabetes Age Gender BMIb. Predictors: (Constant) Age Gender BMIc. Dependent Variable: SBPCoefficientsaModelStandardized CoefficientstSig.95.0% Confidence Interval for BBetaLower BoundUpper Bound1(Constant)8.092.00057.47195.309Gender-.189-2.100.040-6.381-.149BMI.5576.130.0001.2132.392Age.5076.067.000.426.847Diabetes-.089-1.019.313-4.7521.5492(Constant)8.885.00055.24387.407Gender-.173-1.950.056-6.054.081BMI.5746.413.0001.2762.436Age.5176.243.000.441.859a. Dependent Variable: SBP1) Which variables in model 1 are significant?2) Which variables in model 2 are significant?3) Why did they choose model 2?4) What is the “fit” of model 2 (the one they chose to use)?5) Is this a good model why or why not?Multiple Logistic RegressionThe Emergency Department Researchers selected another 60 adults and again looked at Age SBP BMI Gender and Diabetes. This time however they also collected information on whether the chest pain was diagnosed as an MI (aka Heart Attack) or something else. Now their alternative hypothesis was that gender was related to the diagnosis of an MI after controlling for Age SBP BMI and Diabetes. They used multiple logistic regression to test their hypothesis and these are their results (note that there are multiple models and they chose to use the final one):Model Fitting InformationModelModel Fitting CriteriaLikelihood Ratio Tests-2 Log LikelihoodChi-SquaredfSig.Intercept Only74.995Final16.39858.5985.000Pseudo R-SquareCox and Snell.623Nagelkerke.866McFadden.767Parameter EstimatesHeart AttackaBStd. ErrorWalddfSig.Exp(B)NoIntercept115.03743.6796.9361.008BMI-1.400.5725.9951.014.247Age.037.116.0991.7531.037Diabetes.8111.471.3041.5812.251SBP-.469.2134.8491.028.626[Gender=1]-11.8664.6956.3891.0117.025E-6[Gender=2]0b..0..Parameter EstimatesHeart Attacka95% Confidence Interval for Exp(B)Lower BoundUpper BoundNoInterceptBMI.080.756Age.8261.303Diabetes.12640.193SBP.412.950[Gender=1]7.088E-10.070[Gender=2]..1) Is the final model significant?2) What are the odds ratios for each of the significant variables and what do they mean? 3) Will this model help the researchers why or why not?
