Share
Explore BrainMass

# Statistic problems-Chapters 12, 13, 14

Problem 1:
(a) How does correlation analysis differ from regression analysis? (b) What does a correlation
coefficient reveal? (c) State the quick rule for a significant correlation and explain its
limitations. (d) What sums are needed to calculate a correlation coefficient? (e) What
are the two ways of testing a correlation coefficient for significance?
Problem 2:
In the following regression, X = weekly pay, Y = income tax withheld, and n = 35 McDonald's
employees. (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 &#945;
= .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.
Problem 3:
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 &#945; = .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.
Problem 4:
A researcher used stepwise regression to create regression models to predict
BirthRate (births per 1,000) using five predictors: LifeExp (life expectancy in
years), InfMort (infant mortality rate), Density (population density per square
kilometer), GDPCap (Gross Domestic Product per capita), and Literate (literacy
percent). Interpret these results
Problem 5:
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.
Problem 6:
(a) Plot the data on U.S. general aviation shipments. (b) Describe the pattern and
discuss possible causes. (c) Would a fitted trend be helpful? Explain. (d) Make a
similar graph for 1992-2003 only. Would a fitted trend be helpful in making a
prediction for 2004? (e) Fit a trend model of your choice to the 1992-2003 data.
(f) Make a forecast for 2004, using either the fitted trend model or a judgment
forecast. Why is it best to ignore earlier years in this data set?
(See attachment). I need help with these statistic problems. Please.

#### Solution Summary

Complete, Neat and Step-by-step Solutions are provided in the attached file.

\$2.19