The table below gives the number of absences and the overall grade in the class for seven randomly selected students. Based on this data, consider the equation of the regression line, yˆ=b0+b1x , for using the number of absences to predict a student's overall grade in the class. Keep in mind, the correlation coefficient may or may not be statistically significant for the data given. Remember, in practice, it would not be appropriate to use the regression line to make a prediction if the correlation coefficient is not statistically significant. Number of Absences    Grade 1    3.7 2    3.3 3    3.1 4    2.9 6    2.4 7    2.2 8    1.9 According to the estimated linear model, if the value of the independent variable is increased by one unit, then the change in the dependent variable yˆ is given by? a. b0  b. b1 c.  x d. y

The table below gives the number of hours spent unsupervised each day as well as the overall grade averages for seven randomly selected middle school students. Using this data, consider the equation of the regression line, bo + b₁x, for predicting the overall grade average for a middle school student based on the number of hours spent unsupervised each day. Keep in mind, the correlation coefficient may or may not be statistically significant for the data given. Remember, In practice, it would not be appropriate to use the regression line to make a prediction if the correlation coefficient is not statistically significant. a Answer How to enter your answer (opens in new window) Step 6 of 6: Find the value of the coefficient of determination. Round your answer to three decimal places. 9 S e $ A A Hours Unsupervised Overall Grades V 96 5 t 8 b Oll 0 0.5 1 3.5 4 5 5.5 95 92 85 83 81 73 63 h → U İ 8 i k N 9 O alt I * Р ctri Tables Copy Data Keypad Keyboard Shortcuts Previous step answers…

The table below gives the list price and the number of bids received for five randomly selected items sold through online auctions. Using this data, consider the equation of the regression line, yˆ=b0+b1x, for predicting the number of bids an item will receive based on the list price. Keep in mind, the correlation coefficient may or may not be statistically significant for the data given. Remember, in practice, it would not be appropriate to use the regression line to make a prediction if the correlation coefficient is not statistically significant. Price in Dollars 124 143 158 160 196 Number of Bids 12 13 15 16 20 Table   Step 2 of 6 :  Find the estimated y-intercept. Round your answer to three decimal places.

The table below gives the age and bone density for five randomly selected women. Using this data, consider the equation of the regression line, yˆ=b0+b1^x, for predicting a woman's bone density based on her age. Keep in mind, the correlation coefficient may or may not be statistically significant for the data given. Remember, in practice, it would not be appropriate to use the regression line to make a prediction if the correlation coefficient is not statistically significant. Age    Bone Density35    35043    34053    33954    32155    310 Step 3 of 6 :   Determine if the statement "Not all points predicted by the linear model fall on the same line" is true or false.

The table below gives the completion percentage and interception percentage for five randomly selected NFL quarterbacks. Based on this data, consider the equation of the regression line, yˆ=b0+b1xy^=b0+b1x, for using the completion percentage to predict the interception percentage for an NFL quarterback. Keep in mind, the correlation coefficient may or may not be statistically significant for the data given. Remember, in practice, it would not be appropriate to use the regression line to make a prediction if the correlation coefficient is not statistically significant. completion percentage 57 58 60 63 64  interception percentage 4.5 4 3 2.5 1  Find the estimated value of y when x=57x=57. Round your answer to three decimal places.

The table below gives the age and bone density for five randomly selected women. Using this data, consider the equation of the regression line, yˆ=b0+b1x, for predicting a woman's bone density based on her age. Keep in mind, the correlation coefficient may or may not be statistically significant for the data given. Remember, in practice, it would not be appropriate to use the regression line to make a prediction if the correlation coefficient is not statistically significant. Age 39 43 46 61 64 Bone Density 352 346 321 314 312 Table   Step 1 of 6 :  Find the estimated slope. Round your answer to three decimal places

The table below gives the completion percentage and interception percentage for five randomly selected NFL quarterbacks. Based on this data, consider the equation of the regression line, yˆ=b0+b1xy^=b0+b1x, for using the completion percentage to predict the interception percentage for an NFL quarterback. Keep in mind, the correlation coefficient may or may not be statistically significant for the data given. Remember, in practice, it would not be appropriate to use the regression line to make a prediction if the correlation coefficient is not statistically significant. completion 55 59 60 64 66 interception 4.5 3.5 3 2.5 1.5    Substitute the values you found in steps 1 and 2 into the equation for the regression line to find the estimated linear model. According to this model, if the value of the independent variable is increased by one unit, then find the change in the dependent variable y^.

The table below gives the age and bone density for five randomly selected women. Using this data, consider the equation of the regression line, yˆ=b0+b1x, for predicting a woman's bone density based on her age. Keep in mind, the correlation coefficient may or may not be statistically significant for the data given. Remember, in practice, it would not be appropriate to use the regression line to make a prediction if the correlation coefficient is not statistically significant. Age 50 59 60 64 68 Bone Density 331 326 325 320 315 Table   Step 3 of 6 :  Substitute the values you found in steps 1 and 2 into the equation for the regression line to find the estimated linear model. According to this model, if the value of the independent variable is increased by one unit, then find the change in the dependent variable yˆ.

The table below gives the age and bone density for five randomly selected women. Using this data, consider the equation of the regression line, yˆ=b0+b1^x, for predicting a woman's bone density based on her age. Keep in mind, the correlation coefficient may or may not be statistically significant for the data given. Remember, in practice, it would not be appropriate to use the regression line to make a prediction if the correlation coefficient is not statistically significant. Age    Bone Density35    35043    34053    33954    32155    310 Step 4 of 6 :   Determine the value of the dependent variable yˆ at x=0.

The table below gives the number of weeks of gestation and the birth weight (in pounds) for a sample of five randomly selected babies. Using this data, consider the equation of the regression line, y = bo + b1x, for predicting the birth weight of a baby based on the number of weeks of gestation. Keep in mind, the correlation coefficient may or may not be statistically significant for the data given. Remember, in practice, it would not be appropriate to use the regression line to make a prediction if the correlation coefficient is not statistically significant. Weeks of Gestation 33 34 36 38 41 Weight (in pounds) 6 6.1 6.8 7.3 7.9 Table Copy Data Step 4 of 6: Find the estimated value of y when x = 36. Round your answer to three decimal places.