Summary measures Multiple R 0.8440 R-Square 0.7123 Adj...

Summary measures Multiple R 0.8440 R-Square 0.7123 Adj R-Square 0.6644 StErr of Estimate 0.5598 ANOVA Table Source df SS MS F p-value Explained 3 13.9682 4.6561 14.8564 0.0000 Unexplained 18 5.6413 0.3134 Regression coefficients Coefficient Std Err t-value p-value Constant 1.683 1.1696 1.4389 0.1674 Age -0.0498 0.0199 -2.5018 0.0222 Education 0.2135 0.0503 4.2426 0.0005 Family Size 0.0405 0.0784 0.5168 0.6116 (C) Identify and interpret the coefficient of determination (R²) for the model in (A).

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Transcript

00:01 Once again, welcome to a new problem.

00:02 This time we're dealing with regression.

00:06 We're dealing with regression and regression has two components.

00:10 Regression has correlation, which deals with the strength of relationships, the strength of relationships between dependent and independent variables.

00:41 So there's a strength of relationships between dependent and independent variable.

00:46 And then we also have prediction, which is connected to the regression equation.

00:54 So we're looking at the regression equation.

00:57 And under the regression equation, we have two types of regression equations.

01:03 So simple linear regression and multiple linear regression.

01:11 Looking at a multiple linear regression, we have y hat equals to beta 0 plus beta 1 x1 plus beta 2 x2, all the way up until beta n xn.

01:25 Beta 0 is the intercept.

01:30 Y hat is the dependent variable.

01:35 And of course, the x's happen to be the independent variables.

01:44 So the x's are the independent variables.

01:51 We have a new problem.

01:52 And in this particular problem, we're given regression coefficients involving predictions of amounts of television hours that people watch.

02:23 So that's the y variable.

02:26 And of course, we also have multiple x variables, the age, x1, the education, x2, and the family size, x3...

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