SUMMARY OUTPUT Regression Statistics Multiple R 0.3736 R square 0.1396 Adjusted R Square 0.1217 Standard Error 5824.9199
Question:
SUMMARY OUTPUT
Regression Statistics | |
Multiple R | 0.3736 |
R square | 0.1396 |
Adjusted R Square | 0.1217 |
Standard Error | 5824.9199 |
Observations | 50 |
ANOVA
| df | SS | MS | F | Significance F |
Regression | 1 | 264233586.4 | 264233586.41 | 7.79 | 0.008 |
Residual | 48 | 1628625194 | 33929691.54 | | |
Total | 49 | 1892858780 | | | |
| | | | | |
| Coefficients | Standard Error | t-stat | p-value | |
Intercept | 55536.010004 | 4824.391094 | -2.791 | 0.008 | |
A public health researcher is interested in examining the economic effects of smoking. Specifically, she has data on two variables measured at the state level: the percentage of residents who smoke (PSMOKE) and annual average wages per capita (ANWAGES). Her hypothesis is that as the percentage of residents who smoke increases, average annual wages decrease. After running a regression (where PSMOKE is the independent variable), she gets the following Excel results attached in the PDF file below.
(1) specifying the regression equation, (2) interpreting the intercept and slope of the equation, (3) explaining the coefficient of determination ( R 2), and (4) testing the statistical significance of the slope of PSMOKE. Based on these information, is there support for the researcher’s hypothesis?
Business Statistics A First Course
ISBN: 978-0321979018
7th edition
Authors: David M. Levine, Kathryn A. Szabat, David F. Stephan