An expert witness in a case of alleged racial discrimination in a state university school of nursing
introduced a regression of the determinants of Salary of each professor for each year during an
8-year period (n = 423) with the following results, with dependent variable Year (year in which
the salary was observed) and predictors YearHire (year when the individual was hired), Race (1 if
individual is black, 0 otherwise), and Rank (1 if individual is an assistant professor, 0 otherwise).
Interpret these results.
Unsolved 2012-03-19 14:36:54 In the following regression, X = total assets ($ billions), Y = total revenue ($ billions), and n = 64
large banks. (a) Write the fitted regression equation. (b) State the degrees of freedom for a two tailed
test for zero slope, and use Appendix D to find the critical value at á = .05. (c) What is your
conclusion about the slope? (d) Interpret the 95 percent confidence limits for the slope. (e) Verify
that F = t2 for the slope. (f) In your own words, describe the fit of this regression
I'm sorry, but it seems like you have provided two different questions. Can you please clarify which question you would like me to answer?
To interpret the regression results in the case of alleged racial discrimination in a state university school of nursing, we would need to examine the coefficients, p-values, and other relevant statistics provided in the regression output. Unfortunately, the results of the regression equation and other necessary information are not provided in your question. However, I can guide you on how to interpret the results once you have access to the necessary information.
Here is a general guide on how to interpret regression results:
1. Coefficients: The coefficients indicate the average change in the dependent variable (salary in this case) for a one-unit change in the corresponding predictor variable, holding other variables constant. For example, a positive coefficient for the "YearHire" variable would indicate that, on average, as the year of hire increases, the salary also increases.
2. p-values: The p-values associated with each coefficient indicate the statistical significance of the relationship between the predictor variable and the dependent variable. A low p-value (typically less than 0.05) indicates that the relationship is statistically significant.
3. R-squared: R-squared measures the proportion of the variance in the dependent variable that is explained by the predictor variables. A higher R-squared value (closer to 1.0) indicates that the predictors collectively explain a larger proportion of the variability in the dependent variable.
4. Confidence intervals: Confidence intervals provide a range of values within which the true population value of the coefficient is likely to fall. For example, a 95% confidence interval for the coefficient of the "Race" variable would indicate a range of values within which the true effect of race on salary is likely to lie.
Without the specific regression results and information on the statistical significance of the coefficients, it is not possible to provide a specific interpretation of the results for the alleged racial discrimination case. I recommend reviewing the regression output or consulting with a statistician to obtain the necessary information for interpretation.