#1) Question Use the two-way analysis of variance to test the following hypotheses. Summarize your findings for each.

1. Question: Average years of education (variable educ ) are the same for people employed full time and people employed part time (codes 1 and 2 for variable wrkstat ).
2. Question: Average years of education are the same for males and females.
3. Question: The relationship between education and work status (variable wrkstat ) is the same for males and females.

#5) Consider the relationship between the number of hours a person works (hrs1), the happiness of their marriage (variable hapmar), and the person's gender. Use two-way analysis of variance to analyze the data. Indicate what hypothesis you are testing and what your conclusions are about each of them.

#6) Money doesn't buy happiness we're told. Test this adage by looking at the relationship between total family income for last year (incomdol), gender (sex), and happiness of one's marriage (hapmar). What do you conclude? Is the relationship between total family income and marital happiness the same for men and women?

#1) Using the Graphs menu, make a scatterplot of husband's education against wife's education (variables husbeduc and wifeduc). Edit the chart to draw the regression line and print R ² .

1. Does there appear to be a linear relationship between the two variables?
2. Would you characterize the relationship as positive or negative?
3. From the plot, estimate the slope and the intercept. (It's easier to estimate the intercept if you edit the x axis to start at 0.)
4. What's the value for the correlation coefficient?
5. Describe the points that are far from the regression line.
   #3) Run the Regression procedure to obtain the least-squares line that predicts a wife's education from her husband's education.
   1. Write the regression equation. Is it the same as that for predicting a husband's education from his wife's education?
   2. What proportion of the variability in wives' education can be explained by husbands' education? Compare this to the proportion of variability in husbands' education that can be explained by wives' education.

#5) Now let's look at the relationship between a husband’s education and his wife’s education separately for couples in which the wife is employed full time (variable wifeft equals 1) and in which the wife is not employed full time (wifeft equals 0).

a. Use the Graphs menu to make the appropriate scatterplot using wifeft as the Set Markers By variable.

b. Edit the chart so that separate regression lines are drawn for the two types of couples. Print separate R2 values as well.

c. Does there seem to be a linear relationship between a husband's and wife's education for both groups of couples? What is the correlation coefficient between a husband's and wife's education for couples in which the wife is employed full time? For couples in which the wife is not employed full time?

CHAPTER 17

#5) See if the relationship between belief in life after death and highest degree earned is the same for males and for females. What can you conclude?

#17) Test the null hypothesis that job classification (variable jobcat) and gender/race roup are independent.

a. What problem do you see with the chi-square test for the jobcat by sexrace table?

b. What can you do to the table so the chi-square test is appropriate?

c. Based on the new table, what can you conclude about the null hypothesis?

CHAPTER 18

#1) Use a nonparametric test to see if the median difference in years of education between husbands and wives is 0 (variables husbeduc and wifeduc). What do you conclude? Compare your results to those from a paired-samples t test. If the assumptions are met for the paired t test, which test should you use?

#16) Use a nonparametric test to test the whether cigarette consumption (variable cgt58) is related to education (variable educcat). What can you conclude?

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