Use the value of the linear correlation coefficient r to find the coefficient of determination and the percentage of the total variation that can be explained by the linear relationship between the two variables. r= 0.712 What is the value of the coefficient of determination? (Round four decimal places as needed.)

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Use the value of the linear correlation coefficient r to find the coefficient of determination and the percentage of the total variation that can be explained by the linear relationship between the two variables.
r= 0.712
What is the value of the coefficient of determination?
(Round to four decimal places as needed.)
Transcribed Image Text:Use the value of the linear correlation coefficient r to find the coefficient of determination and the percentage of the total variation that can be explained by the linear relationship between the two variables. r= 0.712 What is the value of the coefficient of determination? (Round to four decimal places as needed.)
Listed below are the numbers of cricket chirps in 1 minute and the corresponding temperatures in °F. Find the regression equation, letting chirps in 1 minute be the independent (x) variable. Find the best predicted temperature at a time when a
cricket chirps 3000 times in 1 minute, using the regression equation. What is wrong with this predicted temperature?
Chirps in 1 min
1109 1115
772
911
804
1163
1191 1189 O
Temperature ("F)
67
66
91.2 86.8
84.7 83.5
80.3
88.5
The regression equation is y =+ (Ox.
(Round the y-intercept to one decimal place as needed. Round the slope to four decimal places as needed.)
Transcribed Image Text:Listed below are the numbers of cricket chirps in 1 minute and the corresponding temperatures in °F. Find the regression equation, letting chirps in 1 minute be the independent (x) variable. Find the best predicted temperature at a time when a cricket chirps 3000 times in 1 minute, using the regression equation. What is wrong with this predicted temperature? Chirps in 1 min 1109 1115 772 911 804 1163 1191 1189 O Temperature ("F) 67 66 91.2 86.8 84.7 83.5 80.3 88.5 The regression equation is y =+ (Ox. (Round the y-intercept to one decimal place as needed. Round the slope to four decimal places as needed.)
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