The results of the simple linear regression are provided below. y = 4.25 + 2.75x, SSy = 5.15, SSxx = 1.87 SSyy = 15.17, SSE = 1.0075 Calculate the value of r2, the coefficient of determination. O 0.872 0.339 O 0.934 O 0.661

A regression was run to determine if there is a relationship between hours of TV watched per day (x) and number of situps a person can do (y). The results of the regression were: y=ax+b a=-1.226 b=33.622 r²-0.4489 r=-0.67 Use this to predict the number of situps a person who watches 3 hours of TV can do (to one decimal place)

A regression analysis was performed to determine if there is a relationship between hours of TV watched per day (xx) and number of sit ups a person can do (yy). The results of the regression were: y=ax+b a=-1.152 b=30.418 r2=0.703921 r=-0.839      1. Use this to predict the number of sit ups a person who watches 2.5 hours of TV can do, and please round your answer to a whole number

The data show the chest size and weight of several bears. Find the regression​ equation, letting chest size be the independent​ (x) variable. Then find the best predicted weight of a bear with a chest size of 4141 inches. Is the result close to the actual weight of 273273 ​pounds? Use a significance level of 0.05.   Chest size​ (inches) 4040 5353 3838 4343 4444 5858   Weight​ (pounds) 227227 360360 153153 206206 234234 414414   LOADING... Click the icon to view the critical values of the Pearson correlation coefficient r.       Question content area bottom Part 1 What is the regression​ equation?   ModifyingAbove y with caretyequals=enter your response hereplus+enter your response herex ​ (Round to one decimal place as​ needed.) Part 2 What is the best predicted weight of a bear with a chest size of 4141 ​inches?   The best predicted weight for a bear with a chest size of 4141 inches is enter your response here pounds. ​(Round to one…

Draw the regression line for the following equation: Y = -3 + (1) X Show your calculation for a low and high value on the predictor value.

A regression analysis was performed to determine if there is a relationship between hours of TV watched per day () and number of sit ups a person can do (y). The results of the regression were: y=ax+b a3D-0.838 b=28.783 es 72-0.784996 r=-0.886 rences borations Use this to predict the number of sit ups a person who watches 1 hours of TV can do, and please round your answer to a whole number. gle Drive ce 365 Submit Question adent Course aluations ere to search Chp ins prt sc delete f12 f9 f10 fa f5 f6 +1 f4 backspa & $4 8. 6 3 O E R T II 5

A regression analysis was performed to determine if there is a relationship between hours of TV watched per day (xx) and number of sit ups a person can do (yy). The results of the regression were: y=ax+b a=-1.013 b=27.906 r2=0.498436 r=-0.706 Use this to predict the number of sit ups a person who watches 12 hours of TV can do, and please round your answer to a whole number.

A regression was run to determine if there is a relationship between hours of TV watched per day (x) and number of situps a person can do (y).The results of the regression were:y=ax+b a=-0.926 b=22.103 r2=0.481636 r=-0.694 Use this to predict the number of situps a person who watches 7.5 hours of TV can do (to one decimal place)

A regression was run to determine if there is a relationship between hours of TV watched per day (x) and number of situps a person can do (y). The results of the regression were: y=ax+b a=-0.845 b=38.464 r2=0.632025 r=-0.795 Use this to predict the number of situps a person who watches 11.5 hours of TV can do (to one decimal place) Submit Question hp Ce

A regression was run to determine if there is a relationship between hours of TV watched per day (x) and number of situps a person can do (y). The results of the regression were: y=ax+b a=-1.307 b=32.392 r²=0.675684 r=-0.822 Use this to predict the number of situps a person who watches 12.5 hours of TV can do (to one decimal place)