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##### Biostatistic Problems SPSS

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"Biostatistics Answer 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 Number AGE 1 3 2 11 3 15 4 46 5 14 6 35 7 46 8 35 9 40 10 29 11 22 12 16 2) 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 t tests: 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 . sav and 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 Crosstabulation Alcohol consumption * Bladder Cancer Crosstabulation Alcohol consumption * Bladder Cancer Crosstabulation Alcohol consumption * Bladder Cancer Crosstabulation Alcohol consumption * Bladder Cancer Crosstabulation Count Count Count Count Count Bladder Cancer Bladder Cancer Total No Yes Total Alcohol consumption ""Less than 1 drink per week"" 30 54 84 Alcohol consumption 4 or more drinks per month 22 115 137 Total Total 52 169 221 Calculate 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 for Homogeneity 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 r 2 ). 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 Regression Researchers 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 Summary c Model Summary c Model Summary c Model Summary c Model Summary c Model R R Square Adjusted R Square Std. Error of the Estimate 1 .796 a .634 .608 5.443 2 .792 b .627 .607 5.445 a. Predictors: (Constant), Diabetes, Age, Gender, BMI b. Predictors: (Constant), Age, Gender, BMI c. Dependent Variable: SBP a. Predictors: (Constant), Diabetes, Age, Gender, BMI b. Predictors: (Constant), Age, Gender, BMI c. Dependent Variable: SBP a. Predictors: (Constant), Diabetes, Age, Gender, BMI b. Predictors: (Constant), Age, Gender, BMI c. Dependent Variable: SBP a. Predictors: (Constant), Diabetes, Age, Gender, BMI b. Predictors: (Constant), Age, Gender, BMI c. Dependent Variable: SBP a. Predictors: (Constant), Diabetes, Age, Gender, BMI b. Predictors: (Constant), Age, Gender, BMI c. Dependent Variable: SBP ANOVA c ANOVA c ANOVA c ANOVA c ANOVA c ANOVA c ANOVA c ANOVA c ANOVA c ANOVA c ANOVA c ANOVA c Model Model Model Sum of Squares Sum of Squares df df Mean Square Mean Square F F Sig. 1 Regression Regression 2824.968 2824.968 4 4 706.242 706.242 23.839 23.839 .000 a 1 Residual Residual 1629.408 1629.408 55 55 29.626 29.626 1 Total Total 4454.376 4454.376 59 59 2 Regression Regression 2794.222 2794.222 3 3 931.407 931.407 31.418 31.418 .000 b 2 Residual Residual 1660.155 1660.155 56 56 29.646 29.646 2 Total Total 4454.376 4454.376 59 59 a. Predictors: (Constant), Diabetes, Age, Gender, BMI b. Predictors: (Constant), Age, Gender, BMI c. Dependent Variable: SBP a. Predictors: (Constant), Diabetes, Age, Gender, BMI b. Predictors: (Constant), Age, Gender, BMI c. Dependent Variable: SBP a. Predictors: (Constant), Diabetes, Age, Gender, BMI b. Predictors: (Constant), Age, Gender, BMI c. Dependent Variable: SBP a. Predictors: (Constant), Diabetes, Age, Gender, BMI b. Predictors: (Constant), Age, Gender, BMI c. Dependent Variable: SBP a. Predictors: (Constant), Diabetes, Age, Gender, BMI b. Predictors: (Constant), Age, Gender, BMI c. Dependent Variable: SBP a. Predictors: (Constant), Diabetes, Age, Gender, BMI b. Predictors: (Constant), Age, Gender, BMI c. Dependent Variable: SBP a. Predictors: (Constant), Diabetes, Age, Gender, BMI b. Predictors: (Constant), Age, Gender, BMI c. Dependent Variable: SBP a. Predictors: (Constant), Diabetes, Age, Gender, BMI b. Predictors: (Constant), Age, Gender, BMI c. Dependent Variable: SBP a. Predictors: (Constant), Diabetes, Age, Gender, BMI b. Predictors: (Constant), Age, Gender, BMI c. Dependent Variable: SBP a. Predictors: (Constant), Diabetes, Age, Gender, BMI b. Predictors: (Constant), Age, Gender, BMI c. Dependent Variable: SBP a. Predictors: (Constant), Diabetes, Age, Gender, BMI b. Predictors: (Constant), Age, Gender, BMI c. Dependent Variable: SBP a. Predictors: (Constant), Diabetes, Age, Gender, BMI b. Predictors: (Constant), Age, Gender, BMI c. Dependent Variable: SBP Coefficients a Coefficients a Coefficients a Coefficients a Coefficients a Coefficients a Coefficients a Coefficients a Coefficients a Coefficients a Coefficients a Coefficients a Coefficients a Model Model Standardized Coefficients Standardized Coefficients t t Sig. Sig. 95.0% Confidence Interval for B 95.0% Confidence Interval for B 95.0% Confidence Interval for B 95.0% Confidence Interval for B 95.0% Confidence Interval for B Model Model Beta Beta t t Sig. Sig. Lower Bound Lower Bound Upper Bound Upper Bound Upper Bound 1 (Constant) 8.092 8.092 .000 .000 57.471 57.471 95.309 95.309 95.309 1 Gender -.189 -.189 -2.100 -2.100 .040 .040 -6.381 -6.381 -.149 -.149 -.149 1 BMI .557 .557 6.130 6.130 .000 .000 1.213 1.213 2.392 2.392 2.392 1 Age .507 .507 6.067 6.067 .000 .000 .426 .426 .847 .847 .847 1 Diabetes -.089 -.089 -1.019 -1.019 .313 .313 -4.752 -4.752 1.549 1.549 1.549 2 (Constant) 8.885 8.885 .000 .000 55.243 55.243 87.407 87.407 87.407 2 Gender -.173 -.173 -1.950 -1.950 .056 .056 -6.054 -6.054 .081 .081 .081 2 BMI .574 .574 6.413 6.413 .000 .000 1.276 1.276 2.436 2.436 2.436 2 Age .517 .517 6.243 6.243 .000 .000 .441 .441 .859 .859 .859 a. Dependent Variable: SBP a. Dependent Variable: SBP a. Dependent Variable: SBP a. Dependent Variable: SBP a. Dependent Variable: SBP a. Dependent Variable: SBP a. Dependent Variable: SBP a. Dependent Variable: SBP a. Dependent Variable: SBP a. Dependent Variable: SBP a. Dependent Variable: SBP a. Dependent Variable: SBP a. Dependent Variable: SBP a. Dependent Variable: SBP 1) 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 Regression The 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 Information Model Fitting Information Model Fitting Information Model Fitting Information Model Fitting Information Model Model Fitting Criteria Likelihood Ratio Tests Likelihood Ratio Tests Likelihood Ratio Tests Model -2 Log Likelihood Chi-Square df Sig. Intercept Only 74.995 Final 16.398 58.598 5 .000 Pseudo R-Square Pseudo R-Square Pseudo R-Square Pseudo R-Square Cox and Snell Cox and Snell .623 .623 Nagelkerke Nagelkerke .866 .866 McFadden McFadden .767 .767 Parameter Estimates Parameter Estimates Parameter Estimates Parameter Estimates Parameter Estimates Parameter Estimates Parameter Estimates Parameter Estimates Parameter Estimates Parameter Estimates Heart Attack a Heart Attack a Heart Attack a B B Std. Error Wald df Sig. Exp(B) No Intercept Intercept 115.037 115.037 43.679 6.936 1 .008 No BMI BMI -1.400 -1.400 .572 5.995 1 .014 .247 No Age Age .037 .037 .116 .099 1 .753 1.037 No Diabetes Diabetes .811 .811 1.471 .304 1 .581 2.251 No SBP SBP -.469 -.469 .213 4.849 1 .028 .626 No [Gender=1] [Gender=1] -11.866 -11.866 4.695 6.389 1 .011 7.025E-6 No [Gender=2] [Gender=2] 0 b 0 b . . 0 . . Parameter Estimates Parameter Estimates Parameter Estimates Parameter Estimates Heart Attack a Heart Attack a 95% Confidence Interval for Exp(B) 95% Confidence Interval for Exp(B) Heart Attack a Heart Attack a Lower Bound Upper Bound No Intercept No BMI .080 .756 No Age .826 1.303 No Diabetes .126 40.193 No SBP .412 .950 No [Gender=1] 7.088E-10 .070 No [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? Aug 16 2012 11:51 AM"

Solution ID:608890 | This paper was updated on 26-Nov-2015

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