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Regression Analysis: Understanding R-Squared and Standard Error

Learn about calculating R-squared value and standard error in a regression analysis to assess the model's goodness of fit and prediction accuracy. Understand the significance of sample size, parameters estimation, residual variance, and degrees of freedom. Explore simple linear regression models and their estimation methods.

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Regression Analysis: Understanding R-Squared and Standard Error

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  1. DRQ #11 – October 22, 2013 (4 pts) (1/2 pt) (1/2 pt) (1/2 pt) (1/2 pt) (1/2 pt) (1/2 pt) (1/2 pt) (1/2 pt) If the SST = 1,000 and SSE = 200, then SSR = ____800_____ Calculate the R2 associated with question 1. R2 = SSR/SST = .8 3. In a particular regression analysis, the sample size is 128, the number of parameters to be estimated is 8, and SSE = 1,600. a.) Degrees of freedom = n-p = 128-8 = 120 b.) Residual variance = SSE/n-p = 1,600/120 = 13.33 c.) Standard error of the regression = ___3.6515___ Suppose we wish to estimate the simple linear regression model of personal consumption expenditure (PCE) as a function of disposable income (DPI): PCE = b0 + b1DPIt + ut True or False. b) c) B1 = 900/1,000 = .9

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