The same media consultants decided to continue their investigation of the focal relationship between income and TV viewing habits. In order to confirm the existence of the focal relationship, they decided to control for immigration status by adding a variable measuring the number of each survey respondent's grandparents who were born outside the U.S. The researchers wonder if immigration background could influence both income and TV watching (but they hope this is not true, because it could invalidate the focal relationship). The researchers conduct a multivariate regression by adding a measure of number of grandparents born outside the U.S. to the regression equation presented in Equation 1 of Table 1. The results of the second regression equation are presented in Equation 2 of Table 1. Table 1. OLS Regression Coefficients Representing Influence of Income and Control Variables on Number of Hours of TV Watched per Day Equation 1 Equation 2 Equation 3 Annual Income of respondent (x 1000) -.116 (.003) -.115 (.004) -.063 (.103) Number of respondent's grandparents born outside US -.067 (.123) -.048 (.254) Average weekly hours of employment -.131 (.000) Y-intercept (Constant) 8.69 8.07 10.32 R2 .034 .038 .070 (Significance level in parentheses) Equation 1 can also be written as: Y^ = 8.69 -.116 (X) Equation 2 can also be written as: Y^ = 8.07 -.115 (X) - .067 (Z1) Question 11. What conclusion should the researcher draw about the focal relationship between annual income and TV watching when comparing Equation 2 to Equation 1? Question 2. (IMPORTANT) You should clearly reference the specific numbers that you are using to draw your
The same media consultants decided to continue their investigation of the focal relationship between income and TV viewing habits. In order to confirm the existence of the focal relationship, they decided to control for immigration status by adding a variable measuring the number of each survey respondent's grandparents who were born outside the U.S. The researchers wonder if immigration background could influence both income and TV watching (but they hope this is not true, because it could invalidate the focal relationship). The researchers conduct a multivariate regression by adding a measure of number of grandparents born outside the U.S. to the regression equation presented in Equation 1 of Table 1. The results of the second regression equation are presented in Equation 2 of Table 1.
Table 1. OLS Regression Coefficients Representing Influence of Income and Control Variables on Number of Hours of TV Watched per Day
|
Equation 1 |
Equation 2 |
Equation 3 |
|
|
Annual Income of respondent (x 1000) |
-.116 (.003) |
-.115 (.004) |
-.063 (.103) |
|
Number of respondent's grandparents born outside US |
-.067 (.123) |
-.048 (.254) |
|
|
Average weekly hours of employment |
-.131 (.000) |
||
|
Y-intercept (Constant) |
8.69 |
8.07 |
10.32 |
|
R2 |
.034 |
.038 |
.070 |
(Significance level in parentheses)
Equation 1 can also be written as: Y^ = 8.69 -.116 (X)
Equation 2 can also be written as: Y^ = 8.07 -.115 (X) - .067 (Z1)
Question 11. What conclusion should the researcher draw about the focal relationship between annual income and TV watching when comparing Equation 2 to Equation 1?
Question 2. (IMPORTANT) You should clearly reference the specific numbers that you are using to draw your
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