Attached below is the SPSS data file, USAHealthStats.sav. This data set contains

Attached below is the SPSS data file, USAHealthStats.sav. This data set contains all 50 USA states with 6 different health statistics from 2017, 2 matching health statistics from 2005, the Smoking Rate from 2021, 2 rates related to Covid-19, the categorization of each state according to their geographic region, and the categorization of each state according to how they voted in the 2020 Election (either Red State or Blue State). You can get a better description of each data variable by going to the Variable View tab — be sure to look at the Label for each variable. Using this data set, do the following: 1. For the health statistics from 2017, create a Correlation matrix, computing the correlation coefficient and corresponding p-value for the Diabetes rate, Cancer rate, Smoking rate, and Infant mortality rate. (a) Which correlation is strongest (give its r-value and p-value)? (b) Which correlation is weakest (r-value, p-value)? Do these two correlations make sense? Write a 1- or 2-sentence interpretation of these two correlations’ results. 2. Use Stepwise Regression with your Dependent variable being the Infant Mortality Rate, and your Independent variables being the other 5 health variables from 2017. Use the default alpha levels (no need to change these.) Report the final model that SPSS finds, and write a 1- or 2-sentence interpretation of this model. 3. Look up (in this data set) the Indiana rates for the 2021 Smoking Rate and the 2022 Covid Vaccination Rate. Do 1-Sample t-tests for each of these variables to determine if the USA states’ average is significantly different from the Indiana rates. Write a 1- or 2-sentence interpretation for each of these t-tests’ results. 4. Using the 2-sample Independent t-test, compare the Red States to the Blue States on the Homicide Death Rate and the Covid Vaccination Rate. Write a 1- or 2-sentence interpretation of each of these two t-tests’ results. 5. To determine if the rates have changed from 2005 to 2017, use the Paired t-test to compare the Diabetes Death Rate (2005 vs 2017) and the Cancer Death Rate (2005 vs 2017). Write a 1- to 2-sentence interpretation of each of these two paired t-tests’ results. 6. Use Analysis of Variance (One-way ANOVA) to compare the 4 geographical regions on the 2017 Diabetes Death Rate and 2017 Cancer Death Rate. Be sure to use Options/Descriptive Stats to report the means for each of the 4 regions. Write a 1- to 2-sentence interpretation of your ANOVA results. 7. Do a Chi-square test to test if there was a relationship between the geographical regions and how they voted in the 2020 Election. Be sure to use Cells/Row Percentages to report the percents within each region that voted Red and voted Blue. Write a 1- to 2-sentence interpretation of your Chi-square test results. For all of the above SPSS statistical analyses, give your key SPSS output, but clean up your output so you don’t report unnecessary results.

Attached below is the SPSS data file, USAHealthStats.sav. This data set contains

Attached below is the SPSS data file, USAHealthStats.sav. This data set contains all 50 USA states with 6 different health statistics from 2017, 2 matching health statistics from 2005, the Smoking Rate from 2021, 2 rates related to Covid-19, the categorization of each state according to their geographic region, and the categorization of each state according to how they voted in the 2020 Election (either Red State or Blue State). You can get a better description of each data variable by going to the Variable View tab — be sure to look at the Label for each variable. Using this data set, do the following: 1. For the health statistics from 2017, create a Correlation matrix, computing the correlation coefficient and corresponding p-value for the Diabetes rate, Cancer rate, Smoking rate, and Infant mortality rate. (a) Which correlation is strongest (give its r-value and p-value)? (b) Which correlation is weakest (r-value, p-value)? Do these two correlations make sense? Write a 1- or 2-sentence interpretation of these two correlations’ results. 2. Use Stepwise Regression with your Dependent variable being the Infant Mortality Rate, and your Independent variables being the other 5 health variables from 2017. Use the default alpha levels (no need to change these.) Report the final model that SPSS finds, and write a 1- or 2-sentence interpretation of this model. 3. Look up (in this data set) the Indiana rates for the 2021 Smoking Rate and the 2022 Covid Vaccination Rate. Do 1-Sample t-tests for each of these variables to determine if the USA states’ average is significantly different from the Indiana rates. Write a 1- or 2-sentence interpretation for each of these t-tests’ results. 4. Using the 2-sample Independent t-test, compare the Red States to the Blue States on the Homicide Death Rate and the Covid Vaccination Rate. Write a 1- or 2-sentence interpretation of each of these two t-tests’ results. 5. To determine if the rates have changed from 2005 to 2017, use the Paired t-test to compare the Diabetes Death Rate (2005 vs 2017) and the Cancer Death Rate (2005 vs 2017). Write a 1- to 2-sentence interpretation of each of these two paired t-tests’ results. 6. Use Analysis of Variance (One-way ANOVA) to compare the 4 geographical regions on the 2017 Diabetes Death Rate and 2017 Cancer Death Rate. Be sure to use Options/Descriptive Stats to report the means for each of the 4 regions. Write a 1- to 2-sentence interpretation of your ANOVA results. 7. Do a Chi-square test to test if there was a relationship between the geographical regions and how they voted in the 2020 Election. Be sure to use Cells/Row Percentages to report the percents within each region that voted Red and voted Blue. Write a 1- to 2-sentence interpretation of your Chi-square test results. For all of the above SPSS statistical analyses, give your key SPSS output, but clean up your output so you don’t report unnecessary results.