The statistical method of regression maximizes the parameters/coefficients in th

Question

The statistical method of regression maximizes the parameters/coefficients in the model. minimizes the variance of the observed values. minimizes the sum of the squares of the differences between the observed values and the predicted values. minimizes the sum of the absolute values of the differences between predicted values and the observed values. For the same values of alpha, variance, n, and data, the prediction interval for the response variable, y at x = x_0 is Larger than the confidence interval for E(y), at x = x_0 smaller than the confidence interval for E(y), at x = x_0 Equal to the confidence interval for E(y), at x = x_0 Sometimes smaller than and sometimes larger than the confidence interval for E(y), at x = x_0, depending upon the variance, sigma^2 Let SSE be the Sum of Squares due to Error and R be the correlation coefficient. Which one of the following statements is true? SSE depends upon R only when the slope of the fitted line = 0 SSE does not depend upon R SSE depends upon R None of these The range of the value of the correlation coefficient R is Between 0 and +1 Between - infinity and + infinity Between - 1 and + 1 Between 0 and + infinity Let SSE be the Sum of Squares due to Error and SSR be the Sum of Squares due to Regression. Which one of the following statements is true? None of these SSR is always greater than SSE SSR is always less than SSE SSR is always equal to SSE

Explanation / Answer

Q1) Option C is Correct. It minimizes the sum of squared differences

Q2) Option A is Correct. It is larger than the confidence interval

Q3) Option C is Correct. SSE depends on R

SSE = SST-SSR

Q4) Option C is Correct. It is between -1 to 1

Q5)Option A is Correct

SSR +SSE = SST

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