![]() Note that in some cases the variable contains the value of 0 so we cannot directly transform them to the log-form. To interpret the slope coefficient we divide it by 100(13.4382/100=0.134382). This tells us that a 1% increase in income increases the dependent variable prestige score by about 0.13 points. The interpretation of the log-transformed variable is a bit different. The other interpretation is similar to the previous model. Residual standard error: 7.093 on 98 degrees of freedom You can either transform the variable beforehand or do so in the equation. It's a useful practice to transform the variable to it's log form when doing the regression. ![]() This provides a more honest association between X and Y. When the # of variables is small and the # of cases is very large then Adj R2 is closer to R2. ![]() In this case average education level explains 72.28% of the variance in prestige scores. Adj R2(72%) shows the same as R2 but adjusted by the # of cases and # of variables. To be specific, one way of interpreting the result is: One unit in crease in average education years is related to around 5.361 points increase of the prestige score. R-square shows the amount of variance of Y explained by X. Multiple R-squared: 0.7228, Adjusted R-squared: 0.72į-statistic: 260.8 on 1 and 100 DF, p-value: < 2.2e-16įrom the result of this univariate analysis, we can see that average education years has a significant (small p-value, general rule of thumb <0.05) and positive relationship (positive coefficient) with the prestige score. Residual standard error: 9.103 on 100 degrees of freedom ![]() In the simple linear regression example we have only one dependent variable(prestige) and one independent variable(education). ![]()
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