This question uses the same 2018 GSS data. We now add a second predictor: realrinc: the respondent’s real (inflation-adjusted) personal income, in dollars. male: a binary variable scored 1 if the respondent is male, and 0 if female. college: a binary variable scored 1 if the respondent holds a bachelor’sdegree or higher, and 0 otherwise. Refer to the regression output below. a) Is college education a statistically significant predictor of income in this model? You do not need to conduct a formal test; explain how you can tell from the output, and briefly interpret what the college coefficient means. b) Report the R-squared value for this model and compare it to the R-squared from the model in Question 1. How much did it change, and what does this comparison suggest about the relative importance of education versus sex in explaining income?
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This question uses data from the 2018 General Social Survey…
This question uses data from the 2018 General Social Survey (GSS), a nationally representative survey of adults in the United States. We analyze the following variables: realrinc: the respondent’s real (inflation-adjusted) personal income, measured in dollars. male: a binary variable scored 1 if the respondent is male, and 0 if female. The analysis uses the 1,363 respondents with complete data on these variables. Refer to the regression output below to answer the prompts. a) Conduct a formal hypothesis test at the 0.05 significance level to assess whether sex predicts income. State your null and alternative hypotheses, identify the test statistic and p-value from the table, and state your final conclusion. b) What is the R-squared value in this regression output? Explain exactly what this number means in the context of these two variables. Based on this value, how would you describe the strength of the relationship between sex and income?
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