Question

1. A sociologist examines the relationship between the poverty rate and several socioeconomic factors. For the...

1. A sociologist examines the relationship between the poverty rate and several socioeconomic factors. For the 50 states and the District of Columbia (n = 51), he collects data on the poverty rate (y, in %), the percent of the population with at least a high school education (x1), median income (x2, in $1000s), and the mortality rate per 1,000 residents (x3). He estimates the following model as y = β0 + β1 Education + β2 Income + β3 Mortality + ε. The following ANOVA table shows a portion of the regression results.

df SS MS F
Regression 3 417.3 139.1 94.6
Residual 47 69.1 1.47
Total 50 486.4
Coefficients Standard Error t-stat p-value
Intercept 60.3 4.8 12.47 1.65E-16
Education −0.43 0.05 −7.78 5.45E-10
Income −0.20 0.03 −7.75 6.02E-10
Morality 0.08 0.17 0.47 0.6438

The coefficient of determination indicates that ________.

2. A marketing analyst wants to examine the relationship between sales (in $1,000s) and advertising (in $100s) for firms in the food and beverage industry and collects monthly data for 25 firms. He estimates the model:

Sales = β0 +β1Advertising + ε. The following ANOVA table shows a portion of the regression results.

df SS MS F
Regression 1 78.53 78.53 3.58
Residual 23 504.02 21.91
Total 24 582.55
Coefficients Standard Error t-stat p-value
Intercept 40.10 14.08 2.848 0.0052
Advertising 2.88 1.52 -1.895 0.0608

Which of the following is true?

Multiple Choice

  • If Sales go up by $100, then we predict Advertising to go up by $2,880.

  • If Advertising goes up by $100, then we predict Sales to go up by $2,880.

  • If Sales go up by $100, then we predict Advertising to go up by $4,298.

  • If Advertising goes up by $100, then we predict Sales to go up by $4,298.

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Answer #1

Answer

(1) R square = SS(regression)/SS(total)

setitng the values from the given table, we get

R square = 417.3/486.4= 0.8579 or 85.79%

R square suggest that 85.79% variation in the poverty rate can be explained by the regression line

(2) Independent variable is advertising with slope coefficient $2.88(in thousands) or $2880

slope is positive, this means that the every unit in advertising will increase the sales by $2880

option B is correct

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