Please answer 5-9!! THANK YOU 5. The regression line denotes the between the dep

Question

Please answer 5-9!! THANK YOU

5. The regression line denotes the between the dependent and independent variables. systematic variation c. random variation d. average variation 6. The regression function indicates the a. average value the dependent variable assumes for a given value of the independent variable. b. actual value the independent variable assumes for a given value of the dependent variable c. average value the dependent variable assumes for a given value of the dependent variable d. actual value the dependent variable assumes for a given value of the independent variable 7. The actual value of a dependent variable will generally differ from the regression equation estimate due to a. unaccounted for random variation. b. the inability of the nonlinear Solver to find optimal values. c. not building the regression model with enough data. d. the model R not equal to 1 8. On average, the differences between the actual and predicted values of Y a. are equal to bo. b. sum to an unknown value. c. are distributed uniformly d. sum to zero 9. In the equation Ý-p0-p1 Xii + , 1 is a. the Y intercept b. the slope of the regression line c. the mean of the dependent data. d. the X intercept

Explanation / Answer

5. The regression line denotes the random variation between independent and dependent variable. Regression analysis is generally done to measure causal effect relationship which includes random or error values in them.It is used to determine the degree of relation by calculating the coefficient of correlation. When r=0, that means there is no relation between the variale whereas in regression we calculate the best fit taking error into account.

6. Since regression calculates the best fit it is average value the dependent variable gives for a given value of independent variable.

7. The actual value of dependent variable will generally differ from the regression equation estimate due to unaccounted random variation.

8. The difference between actual and predicted values is equal to the sum of absolute residuals.

9. B1 is the slope of regression line.

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