ANOVA table for regression n data points, each with an x_i, y_i value. s is the standard deviation of the data around the regression line | | DF | SS | MS | F | |---|---|---|---|---| | Regression | 1 | (Y?_i - Y?)² | SSG/DFG | MSG/MSE | | Error | n-2 | (Y_i - Y?_i)² | SSE/DFE | | | Total | n-1 | SSG+SSE | | |
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Regression: This is the part of the total variation in the dependent variable (Y) that is explained by the regression model. The degrees of freedom (DF) for regression is the number of predictors in the model (in this case, 1). The sum of squares for regression Show more…
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ANOVA df SS MS F Significance F Regression 3 1.9464 0.6488 2.0638 0.1102 Residual 95 29.8650 0.3144 Total 98 31.8114 Coefficients Standard Error t Stat P-value Intercept -1.5746 0.0575 -27.3770 0.0000 x1 -0.1260 0.0567 -2.2232 0.0286 x2 -0.0337 0.0304 -1.1109 0.2694 x3 -0.0095 0.0553 -0.1723 0.8636 The above table shows the regression results when estimating the multiple linear regression model relating response variable y to three predictor variables, x1, x2, and x3. At the 5% significance level, which predictor variable(s) is(are) individually significant? Only x1. Only x2. Only x3. Both x1 and x2.
Adi S.
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