4. :1 mark: Whloh of the following statement is correct? The correlation coefflclent r answers the question: how close do these points in the scatter plot come to falling on a stralght line. The best fitted line is the one with the smallest sum of residuals. an. The intercept measures the average value of the Yvalue when the Xvariable equals to 1. If there is a statistically significant linear relationship, than we can conclude that a change in the lit-variable leads to changes In the Y-varlabla. 'L.._,e._m.__._..__._._ 2 1 mar Which of the followin statement is correct? 3 The sign {+ or ] of the corretstion coefficient r is not necessarily the same as the slope of the least squares regression tine For a least squares regression, the distribution of the residuals depends on the X-variable. An outlier affects the position of the least squares regression line but it does not affect the correlation coefficient r For a least squares regression line. the sum of all the residuals is zero. 3 __(1 mark] Which of the following statement is correct? Scatter plots can be used to check the ncrrnallty assumption of the residuals Even if we see patterns in the residuals versus the Xvariable pint, we can interpret the regression results The correlation coefficient r and the slope of a least squares regression line measure the same thing and should always have the same value. The regression equation describes the average relationship between two numeric variables. 1 mark Which of the followin statement is correct? The outliers on Y axis do not impact on the slope of the regression line. The outliers are not important for simple linear regression. The outliers on X or Y axes impact on the slope of the regression line. The outliers on X axis do not impact on the slope of the regression line. 6. 1 mark Which of the followin statement is correct? __ J Normality assumption for linear regression refers to normality of the relationship. OP? Normality assumption for linear regression refers to normality of Y variable. ont' Normality assumption for linear regression refers to normality of residuals. Normality assumption for linear regression refers to normality of X variable. 1__._(1 mark] Which of the following statement is correct? The intercept of a regression line describes the average change in the Yvariable for each 1-uni'l: change in the X-variable. For the least squares regressions, the average pattern seen in the scatter plot must be a straight llne. In regression, we can use the Yvariahle to predict values for the Xvariehla. In regression, the X-axis and the Yaxis play the same role