Question
Simple linear regression results: Dependent Variable: var9 Independent Variable: var8 var9 = 5.9351585 - 0.0057636888 var8 Sample size: 20 R (correlation coefficient) = -0.010243565 R-sq
Simple linear regression results:
Dependent Variable: var9 Independent Variable: var8 var9 = 5.9351585 - 0.0057636888 var8 Sample size: 20 R (correlation coefficient) = -0.010243565 R-sq = 0.00010493061 Estimate of error standard deviation: 2.9229348
Parameter estimates:
| Parameter | Estimate | Std. Err. | Alternative | DF | T-Stat | P-value |
|---|---|---|---|---|---|---|
| Intercept | 5.9351585 | 1.039987 | 0 | 18 | 5.7069542 | <0.0001 |
| Slope | -0.0057636888 | 0.1326143 | 0 | 18 | -0.043462044 | 0.9658 |
Analysis of variance table for regression model:
| Source | DF | SS | MS | F-stat | P-value |
|---|---|---|---|---|---|
| Model | 1 | 0.016138329 | 0.016138329 | 0.0018889493 | 0.9658 |
| Error | 18 | 153.78386 | 8.5435479 | ||
| Total | 19 | 153.8 |
Make sure that you report whether or not your findings are significant. Discuss what your findings mean. If your correlation was significant does it mean that one variable is causing the other to act a certain way? Why/why not?
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