Run a regression analysis on the following bivariate set of data with y as the response variable. x y 28.8 51.5 11.8 54.7 2.9 45.5 18.5 45.3 18.5 52.4 29 62.9 2.9 56.8 24.9 51.3 Find the correlation coefficient and report it accurate to three decimal places. What proportion of the variation in y can be explained by the variation in the values of x? Report answer as a percentage accurate to one decimal place.  (If the answer is 0.84471, then it would be 84.5%...you would enter 84.5 without the percent symbol.)%Based on the data, calculate the regression line (each value to three decimal places)ˆyy^ = x + Predict what value (on average) for the response variable will be obtained from a value of 14.8 as the explanatory variable. Use a significance level of α=0.05α=0.05 to assess the strength of the linear correlation.What is the predicted response value?  (Report answer accurate to one decimal place.)ˆyy^ =

Here is a bivariate data set. x y 73.1 75.1 63 76.7 75.3 78.6 62.9 71.2 72.3 76.2 60.4 74.7 Find the correlation coefficient and report it accurate to three decimal places. r = What proportion of the variation in y can be explained by the variation in the values of x? Report answer as a percentage accurate to one decimal place.r² = %

Run a regression analysis on the following bivariate set of data with y as the response variable. x y 58.3 81.3 77.7 48.5 49.3 67.2 68.5 58.9 46.4 93.1 65.5 75.8 48.9 90.5 65.2 66.9 63.2 79.4 56.5 89.3 65.6 75.5 40.4 96.2 Find the correlation coefficient and report it accurate to three decimal places. r = What proportion of the variation in y can be explained by the variation in the values of x? Report answer as a percentage accurate to one decimal place.  (If the answer is 0.84471, then it would be 84.5%...you would enter 84.5 without the percent symbol.)r² = %Based on the data, calculate the regression line (each value to three decimal places)y = x + Predict what value (on average) for the response variable will be obtained from a value of 77.8 as the explanatory variable. Use a significance level of α=0.05 to assess the strength of the linear correlation.What is the predicted response value?  (Report answer accurate to one decimal place.)y =

Run a regression analysis on the following bivariate set of data with y as the response variable. X y 9.4 24.2 36.9 66.5 42.6 140.4 -16 -26.1 27.4 52 27.5 71.9 24.8 66.8 21 41.2 33.1 2.4 23.9 34.4 69.2 34.3 71.9 66 Find the correlation coefficient and report it accurate to three decimal places. r = What proportion of the variation in y can be explained by the variation in the values of x? Report answer as a percentage accurate to one decimal place. (If the answer is 0.84471, then it would be 84.5%...you would enter 84.5 without the percent symbol.) r² = % Based on the data, calculate the regression line (each value to three decimal places) y = X + Predict what value (on average) for the response variable will be obtained from a value of -13.4 as the explanatory variable. Use a significance level of a = 0.05 to assess the strength of the linear correlation. What is the predicted response value? (Report answer accurate to one decimal place.) y =

Here is a bivariate data set. x y 33.1 67.2 34.8 73.7 20.9 93.2 26.1 74.2 31.3 97.5 1.3 110.9 30.4 59.6 18.8 91.8 40.9 43.1 Find the correlation coefficient and report it accurate to three decimal places. r = What proportion of the variation in y can be explained by the variation in the values of x? Report answer as a percentage accurate to one decimal place.r² =            %

Run a regression analysis on the following bivariate set of data with y as the response variable. y 62.8 75.2 60 70.4 59.8 63 59.3 68.5 59 70.3 52.4 50.8 63.4 61.9 70.3 68.1 65.6 69.3 56.8 60.9 67.5 77.5 55.5 57.2 Find the correlation coefficient and report it accurate to three decimal places. What proportion of the variation in y can be explained by the variation in the values of x? Report answer as a percentage accurate to one decimal place. (If the answer is 0.84471, then it would be 84.5%...you would enter 84.5 without the percent symbol.) r2 = Based on the data, calculate the regression line (each value to three decimal places) y = Predict what value (on average) for the response variable will be obtained from a value of 52.5 as the explanatory variable. Use a significance level of a = 0.05 to assess the strength of the linear correlation. What is the predicted response value? (Report answer accurate to one decimal place.) y = Submit Question IO 01 100 O Details

Here is a bivariate data set. x y 36 52 25 52.7 29.5 51.3 21.4 54.7 28 51.2 26.8 50.9 22 53.3 33.7 50.4 26.1 53.8 23.1 53.5 Find the correlation coefficient and report it accurate to three decimal places.r = What proportion of the variation in y can be explained by the variation in the values of x? Report answer as a percentage accurate to one decimal place.R² = %

Here is a bivariate data set. x y 61.6 77.9 86.4 55.6 75.6 62.4 60.1 69.2 76.4 60.4 74 60.1 59.7 66.7 Find the correlation coefficient and report it accurate to three decimal places.r = __?__ What proportion of the variation in y can be explained by the variation in the values of x? Report answer as a percentage accurate to one decimal place.R² = __?__%

Run a regression analysis on the following bivariate set of data with y as the response variable. x y 57.9 -34.4 16.6 92 40.1 89 55.8 54.5 57.9 27.4 17.3 164.1 65.2 62.4 48.3 51.9 66.2 14.5 70.9 -39.2 57 -90.1 58.5 63 Find the correlation coefficient and report it accurate to three decimal places.r = What proportion of the variation in y can be explained by the variation in the values of x? Report answer as a percentage accurate to one decimal place.  (If the answer is 0.84471, then it would be 84.5%...you would enter 84.5 without the percent symbol.)r² = %

6. Which of the following are true? Select all that apply. a. The coefficient of determination is the proportion of variation in the response variable that is explained by the regression line. b. We often use the symbol r to represent the coefficient of determination. c. In simple linear regression, the coefficient of determination equals the square of the linear correlation coefficient r. d. The closer the regression line comes to the data points, r should go to eleven.