A football coach is looking for a way to identify players that are "under weight". The coach decides to get data for each player's height (æ, in inches) and weight (y, in pounds), then does a linear regression. The results are: y = - 57 + 3.2a, r = 0.87 and the standard error is S. = 11.4. Since there is a strong linear correlation the coach, who also majored in Statistics, decides to identify all "outliers" in the data. Obviously, any player whose weight is above the regression line is not "under weight". So the only outliers the coach is interested in are those that are below the regression line. What is the lowest weight possible for a 71 inch player to not be considered "under weight"? Do not round.

A football coach is looking for a way to identify players that are "under weight". The coach decides to get data for each player's height (æ, in inches) and weight (y, in pounds), then does a linear regression. The results are: y = - 57 + 3.2a, r = 0.87 and the standard error is S. = 11.4. Since there is a strong linear correlation the coach, who also majored in Statistics, decides to identify all "outliers" in the data. Obviously, any player whose weight is above the regression line is not "under weight". So the only outliers the coach is interested in are those that are below the regression line. What is the lowest weight possible for a 71 inch player to not be considered "under weight"? Do not round.

MATLAB: An Introduction with Applications

6th Edition

ISBN:9781119256830

Author:Amos Gilat

Publisher:Amos Gilat

Chapter1: Starting With Matlab

Section: Chapter Questions

Problem 1P

See similar textbooks

Similar questions

What does the square of the linear correlation coefficient measure (rxr)?
The table below gives the age and bone density for five randomly selected women. Using this data, consider the equation of the regression line, y = bo + bjx, 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 47 49 50 51 58 Bone Density 360 353 336 333 310 Table Copy Data Step 1 of 6: Find the estimated slope. Round your answer to three decimal places.
The table below gives the number of hours seven randomly selected students spent studying and their corresponding midterm exam grades. Using this data, consider the equation of the regression line, yˆ=b0+b1x, for predicting the midterm exam grade that a student will earn based on the number of hours spent studying. 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. Hours Studying 0.5 1 1.5 2 3 3.5 4.5 Midterm Grades 63 66 68 72 74 93 94 Table Step 4 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 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 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. bo + bjx, for predicting the birth weight of a baby Weeks of Gestation 33 35 37 39 40 Weight (in pounds) 5 6.8 7.9 8.5 9.3 Table Copy Data Step 5 of 6: Find the error prediction when x = 39. 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, y = bo + 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 58 60 61 62 65 Interception Percentage 5 4.5 4 3.5 3 Table Copy Data Step 5 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 ŷ. Tables Keypad Keyboard Shortcuts Next
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 Bone Density48 35151 32056 31860 31169 310 Step 6 of 6: Find the value of the coefficient of determination. Round your answer to three decimal places.
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, yˆ=b0+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. Hours Unsupervised 0.5 1 2.5 3 4 5 5.5 Overall Grades 98 95 90 79 75 69 66 Step 1 of 6: Find the estimated slope. Round your answer to three decimal places. Step 2 of 6: Find the estimated y-intercept. Round your answer to three decimal places. Step 3 of 6: Determine if the statement "Not all points predicted by the linear model fall on the…
The table below gives the age and bone density for five randomly selected women. Using this data, consider the equation of the regression line, ý = bo + bjx, 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 38 43 53 59 63 Bone Density 357 351 326 317 313 Table Copy Data Step 2 of 6: Find the estimated y-intercept. Round your answer to three decimal places. O Tables E Keypad Answer How to enter your answer Keyboard Shortcuts Previous step answers Submit Answer O 2020 Hawkes Learning MacBook Air DO 888 SC 80 F3 F6 FS FI & %23 2$ 6 7 8 9 2 3 Y P Q W E D F G H J K A S > C V N M command option option command ンレ 5
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 35 43 53 54 55 Bone Density 350 340 339 321 310 Table Step 6 of 6 : Find the value of the coefficient of determination. Round your answer
The table below gives the age and bone density for five randomly selected women. Using this data, consider the equation of the regression line, y = bo + bjx, 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 35 50 54 61 66 Bone Density 354 353 350 334 332 Tab Copy Data Step 6 of 6: Find the value of the coefficient of determination. Round your answer to three decimal places. 田 Tables 國 Key Answer Keyboard Sho How to enter your answer (opens in new window) Previous step ar Submit An © 2022 Hawkes Learning tv APR 24 MacBook Pro
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 Bone Density34 35745 34148 33160 32965 325 Step 3 of 6: Determine the value of the dependent variable yˆ at x=0.
The table below gives the number of hours seven randomly selected students spent studying and their corresponding midterm exam grades. Using this data, consider the equation of the regression line, yˆ=b0+b1x, for predicting the midterm exam grade that a student will earn based on the number of hours spent studying. 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. Hours Studying 2 2.5 3 3.5 4 5 5.5 Midterm Grades 63 67 76 78 84 85 90 Table Step 6 of 6 : Find the value of the coefficient of determination. Round your answer to three decimal places.