A regression analysis was performed and the summary output is shown below.
Multiple R | 0.7149844700.714984470 |
---|---|
R Square | 0.5112027920.511202792 |
Adjusted R Square | 0.4904029110.490402911 |
Standard Error | 8.2079903998.207990399 |
Observations | 5050 |
dfdf | SSSS | MSMS | FF | Significance FF | |
---|---|---|---|---|---|
Regression | 22 | 3311.5863311.586 | 1655.7931655.793 | 24.577224.5772 | 4.9491E-084.9491E-08 |
Residual | 4747 | 3166.4423166.442 | 67.37167.371 | ||
Total | 4949 | 6478.0286478.028 |
Step 1 of 2:
How many independent variables are included in the regression model?
Step 2 of 2:
Which measure is appropriate for determining the proportion of variation in the dependent variable explained by the set of independent variable(s) in this model?
(1) Degree of freedom for regression = 2
and we know that number of independent variable =df(regression) + 1
independent variables = 2 + 1 = 3
So, we have 3 independent variables in regression model.
(2) R square is appropriate for determining the proportion of variation in the dependent variable explained by the set of independent variable(s) in this model
Given that R square = 0.5112
this means that 51.12% of variation in the dependent variable explained by the set of independent variable(s) in this model
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