How 15. many degrees of freedom are there for a test of significance for the slo

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How 15. many degrees of freedom are there for a test of significance for the slope of a simple using the t-test method? -B, n-2 o E. Can't tell, not enough information given. 16. By what criteria is an ordinary (simple) linear regression considered best fit? A. A straight line is drawn between all of the points B. The slope is calculated from an equation that maximizes the sums of squares for the regression C. The slope is calculated from an equation that minimizes the sums of squares for the regression D. The slope is calculated from an equation that maximizes the sums of squares for the error (residuals) o E. The slope is calculated from an equation that minimizes the sums of squares for the error (residuals) 17. Which is the single biggest limitation of multiple linear regression? A. If the independent variables are not linearly related to the dependent variable, the regression slopes cannot be tested and interpreted. tested and interpreted. interpreted interpreted. B. If the dependent variable is not correlated with any of the independent variables, the regression slopes cannot be C. If the independent variables are not correlated with each other, the regression slopes cannot be tested and D. If the independent variables are correlated with each other, the regression slopes cannot be tested and a E. None of the above 18. How many degrees of freedom are there for a test of significance for the regression of Y on X using the ANOVA F-test method? A. F numerator df-1 and denominator df-n-1 B. F numerator df= l and denominator df= n-2 C. F numerator df= n-1 and denominator df-i D. F numerator df-n-2 and denominator df-1 E. None of the above 19. True or false. The value of X at the Y-intercept is always equal to zero in a simple linear regression. A, True B. False 20. True of false. The simple linear regression is called a "best fit" line because it maximizes the squared deviations for the difference between observed and predicted Y values. A, True B. False 21. True of false. Regression analysis results in a model of the cause-effect relationship between a dependent and one (simple linear) or more (multiple) predictor variables. The equation can be used to predict new observations of the dependent variable. A, True 22. If the regression equation of a predictor variable does not explain the variation in the dependent variable (eg. R2, the coefficient of determination, is close to zero), what can be concluded about the relationship between the predictor and the response variables? B. False A. The independent variable has no causal association with the dependent variable. B. The independent variable has no linear causal association with the dependent variable. C. There will be a trend in the plot of the residuals against the predictor variable. D. There will be no trend in the plot of the residuals against the predictor variable. E. None of the above

Explanation / Answer

Q15

Answer is B) n-2

Degrees of freedom. For simple linear regression (one independent and one dependent variable), the degrees of freedom (DF) is equal to:

DF = n - 2

where n is the number of observations in the sample.

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