The attached results are for a multiple regression study of smartphone addiction (SSA-SV) proneness in relation to 1) Gender 2)Age 3) Anxiety (GAD-7). I just want to clarify what the F-statistic means and the effects of AGE on the F-statistic; given that AGE is statistically significant compared to the other predictor variables.

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The attached results are for a multiple regression study of smartphone addiction (SSA-SV) proneness in relation to 1) Gender 2)Age 3) Anxiety (GAD-7). I just want to clarify what the F-statistic means and the effects of AGE on the F-statistic; given that AGE is statistically significant compared to the other predictor variables. 

## Linear Regression Analysis

### Model Summary – SAS-SV Score

| Model | R    | R²   | Adjusted R² | RMSE  |
|-------|------|------|-------------|-------|
| H₀    | 0.000 | 0.000 | 0.000       | 10.874 |
| H₁    | 0.311 | 0.097 | 0.066       | 10.510 |

### ANOVA (Analysis of Variance)

| Model | Sum of Squares | df | Mean Square | F     | p    |
|-------|----------------|----|-------------|-------|------|
| Regression | 1032.000        | 3  | 344.000      | 3.114 | 0.030 |
| Residual | 9609.824        | 87 | 110.458      |       |      |
| Total   | 10641.824       | 90 |             |       |      |

*Note: The intercept model is omitted, as no meaningful information can be shown.*

### Coefficients

| Model     | Unstandardized | Standard Error | Standardized | t      | p     | 95% CI Lower | 95% CI Upper |
|-----------|----------------|----------------|--------------|--------|-------|--------------|--------------|
| **H₀**        |                |                |              |        |       |              |              |
| (Intercept)   | 29.813         | 1.140          |              | 26.154 | <.001 | 27.549       | 32.078       |
| **H₁**        |                |                |              |        |       |              |              |
| (Intercept)   | 38.385         | 3.505          |              | 10.952 | <.001 | 31.419       | 45.351       |
| GENDER        | -1.772         | 2.080          | -0.088       | -0.852 | 0.396 | -5.907       | 2.362        |
| AGE           | -0.270         | 0.097          | -0.287       | -2.795 | 0.006 | -0.462       | -0.078       |
| G
Transcribed Image Text:## Linear Regression Analysis ### Model Summary – SAS-SV Score | Model | R | R² | Adjusted R² | RMSE | |-------|------|------|-------------|-------| | H₀ | 0.000 | 0.000 | 0.000 | 10.874 | | H₁ | 0.311 | 0.097 | 0.066 | 10.510 | ### ANOVA (Analysis of Variance) | Model | Sum of Squares | df | Mean Square | F | p | |-------|----------------|----|-------------|-------|------| | Regression | 1032.000 | 3 | 344.000 | 3.114 | 0.030 | | Residual | 9609.824 | 87 | 110.458 | | | | Total | 10641.824 | 90 | | | | *Note: The intercept model is omitted, as no meaningful information can be shown.* ### Coefficients | Model | Unstandardized | Standard Error | Standardized | t | p | 95% CI Lower | 95% CI Upper | |-----------|----------------|----------------|--------------|--------|-------|--------------|--------------| | **H₀** | | | | | | | | | (Intercept) | 29.813 | 1.140 | | 26.154 | <.001 | 27.549 | 32.078 | | **H₁** | | | | | | | | | (Intercept) | 38.385 | 3.505 | | 10.952 | <.001 | 31.419 | 45.351 | | GENDER | -1.772 | 2.080 | -0.088 | -0.852 | 0.396 | -5.907 | 2.362 | | AGE | -0.270 | 0.097 | -0.287 | -2.795 | 0.006 | -0.462 | -0.078 | | G
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