Description
Instructions: Students should submit their reports on Canvas. The report needs to clearly state what question is being solved, step-by-step walk-through solutions, and final answers clearly indicated. Please solve by hand where appropriate.
Please submit two files: (1) a R Markdown file (.Rmd extension) and (2) a PDF document generated using knitr for the .Rmd file submitted in (1) where appropriate. Please, use RStudio Cloud for your solutions.
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Refer to the CDI data set. Using R2 as the criterion, which predictor variable accounts for the largest reduction in the variability in the number of active physicians?
- Refer to the CDI data set in Appendix C.2 and Project l.44. Obtain a separate interval estimate of β1, for each region. Use a 90 percent confidence coefficient in each case. Do the regression lines for the different regions appear to have similar slopes?
- Refer to GPA data:
- Set up the ANOVA table.
- What is estimated by MSR in your ANOVA table? by MSE? Under what condition do MSR and MSE estimate the same quantity?
- Conduct an F test of whether or not β1 = 0. Control the α risk at .01. State the alternatives, decision rule, and conclusion.
- What is the absolute magnitude of the reduction in the variation of Y when X is introduced into the regression model? What is the relative reduction? What is the name of the latter measure?
- Obtain r and attach the appropriate sign.
- Which measure, R2 or r, has the more clear-cut operational interpretation? Explain.
- Refer to Crime rate data.
- Compute the Pearson product-moment correlation coefficient r12.
- Test whether crime rate and percentage of high school graduates are statistically independent in the population; use a α = .01. State the alternatives, decision rule, and conclusion.
- Compute the Spearman rank correlation coefficient rs.
- Test by means of the Spearman rank correlation coefficient whether an association exists between crime rate and percentage of high school graduates. State the alternatives, decision rule, and conclusion.