The following scatter plot presents data on an individual's Body Mass Index and their percent of body fat. Scatterplot of %Fat vs BM] 15 Ill 15 3a 35 BM From the scatterplot, what must the correlation coefficient be? 0 a r=0.'|9 O b r=0.74 O c =o.19 0 d r=-0.74 A study gathers data on the brain function of teenagers and routine exercise. The number of hours each individual exercised over the course of a week and their score on a standard cognitive test were recorded. Call the hours of exercise x and the score on the testy. The least-squares regression line for predictingyfrom x is f=64+5.2:: What does the number 64 represent in the equation? 0 a The avearge score on the test for all individuals. 0 b Predicted score on test when the individual exercises 5.2 hours per week. 0 c It's the yvintercept of the line, but it has no practical purpose in the context of the problem. 0 d Predicted score on test when the individual exercises 0 hours per week. The following scatter plot presents data on an individual's Body Mass Index and their percent of body fat. Scatterplot of %Fat vs BMI Based on the scatterplot, the least-squares line would predict that a person with a body mass index of 20 would have which of the following body fat percentage? 0 a 15% O b 24% O c 30% 0 d 18% The following scatter plot presents data on an individual's Body Mass Index and their percent of body fat. Scatterplot of %Fat vs BMI In the scatterplot, the point indicated by the open triangle 0 a has a positive value for the residual. O b has a negative value for the residual. O c has a zero value for the residual. 0 d does not have a residual. A study gathers data on the brain function of teenagers and routine exercise. The number of hours each individual exercised over the course of a week and their score on a standard cognitive test were recorded. Call the hours of exercise X and the score on the testy. The least-squares regression line for predictingy from x is =64+5.2x The standard deviation of the residuals for the data was found to be 0.85. What does this tell us? 0 a 85% of the variation in test scores is accounted for by the LSRL. O b The size of the typical prediction error from the LSRL is about 0.85. O c There is a strong, positive correlation between the variables. 0 d Each data point is 0.85 away from the LSRL. The time it takes to successfully navigate through a cornmaze was studied in relation to the amount of time the subjects were allowed to study the map before beginning. A computer regression analysis of time with the map (x in minutes) versus time to complete the maze (y in minutes) is shown below. Predictor Coef SE Coef T P Constant 129.092 5.996 21.53 0.000 Time -5.196 1.146 -4.54 0.001 s = 13.1089 RSq = 69.6% RSqtadj) = 66.2% The equation of the least-squares regression line is O a :129.0925.196x O b )'3=1.146+5.996x O c )'5=5.196+129.092x 0 d f=5.996+1_146)( The time it takes to successfully navigate through a cornmaze was studied in relation to the amount of time the subjects were allowed to study the map before beginning. A computer regression analysis of time with the map (x in minutes) versus time to complete the maze (y in minutes) is shown below. Predictor Coef SE: Coef T P Constant 129.092 5.996 21.53 0.000 Time -5.196 1.146 -4.54 0.001 s = 13.1089 99; = 69.6% RSq(adj) = 66.2% On average, how far are the predicted yvalues from the actualyvalues? O a 5.996 O b 0.696 O c 13.1089 0 d 1.146 1-..--.u\" .... '. r\"... .t, The time it takes to successfully navigate through a cornmaze was studied in relation to the amount of time the subjects were allowed to study the map before beginning. A computer regression analysis of time with the map (x in minutes) versus time to complete the maze (y in minutes) is shown below. Predictor Coef SE Coef T P Constant 129.092 5.996 21.53 0.000 Time -5.196 1.146 4.54 0.001 s = 13.1059 RSq = 59.5% R5q(adj] = 55.2% Which statement is an accurate interpretation for the data from the analysis? 0 a r = 0.834 proves a strong positive correlation O b 69.6% ofthe variation in minutes with the map can be accounted for by the linear regression. O c 69.6% ofthe variation in minutes to compelte the maze can be accounted for by the linear regression. Q d r = 0.696 proves a moderate positive correlation The time it takes to successfully navigate through a cornmaze was studied in relation to the amount of time the subjects were allowed to study the map before beginning. A computer regression analysis of time with the map (x in minutes) versus time to complete the maze (y in minutes) is shown below. Predictor Coef SE Coef T P Constant 129. 092 5.996 21.53 0. 000 Time -5. 196 1. 146 -4. 54 0. 001 S = 13. 1089 R-Sq = 69.68 R-Sq (adj ) = 66.28 Calculate and interpret the correlation coefficient. a r = -0.834 proves a strong negative correlation Ob r = 0.834 proves a strong positive correlation O c r = 0.696 proves a moderate positive correlation OWhich of the following residual plots shows a data set for which a linear model would be the best fit? O a Ob O c 2 10 Od Inderaiders