A diligent statistics student recorded the length of hisfaithful #2 pencil as he worked away on his homework.He discovered a strong linear relationship between thenumber of hours that he worked and the length of hispencil. Here is the regression analysis for these data.Dependent variable: length (cm)R2 = 92.3, R2 1adj2 = 89.5,coeff se t ratio p valueconstant 17.047 0.128 23.58 60.0001time (hr) -1.914 0.047 35.28 60.0001 a) Write the equation of the least square regressionline.b) Interpret R2 in this context.c) Interpret the equation in this context.d) This student’s girlfriend tried out his model on apencil she had used for 5 hours, and found a residualof -0.88 cm. How long was her pencil at that time?e) Should she have expected this model to describe therate for her pencils? Why or why not?

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=-0.609 b=34.589 r2=0.859329 r=-0.927 Use this to predict the number of sit ups a person who watches 10.5 hours of TV can do, and please round your answer to a whole number.

2. A high school track & field coach wanted to assess the relationship between an athletes height and how far they can jump in the long jump event (both in inches). They collect data on each athletes height and how far they can jump. Let the height in inches of the athlete be the explanatory variable (X) and the distance in inches of the jump be the response (Y). The scatterplot of the data based on 32 athletes is as follows: distance 88 86 84 82 80 78 00 O o 70 Scatter Plot 72 height 00 000 00 00 8 74 8 O ¥75 76 一念 78

intents d Resource Listed below are foot lengths (mm) and heights (mm) of males. Find the regression equation, letting foot length be the predictor (x) variable. Find the best predicted height of a male with a foot length of 273.1 mm. How does the result compare to the actual height of 1776 mm? Foot Length 282.4 277.9 253.4 259.3 279.2 258.2 273.6 262:3 Height 1785.1 1770.9 1676.0 1646.1 1859.0 1710.1 1789.3 1736.9 The regression equation is y=+x. (Round the y-intercept to the nearest integer as needed. Round the slope to two decimal places as needed.) ype here to search Tutoring Help me solve this O i Get more help. C 3¹ ... 99+ 0 LA 6 3 Clear all Check answer 78°F ^®

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=a+bx b=-1.276 a=24.302 r2=0.779689 r=-0.883 Use this to predict the number of situps a person who watches 3 hours of TV can do. Round 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=-0.788 b=22.872 r2=0.388129 r=-0.623 Use this to predict the number of situps a person who watches 10.5 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:ŷ=ax+b a=-1.229 b=35.936 r2=0.508369 r=-0.713 Use this to predict the number of situps a person who watches 3.5 hours of TV can do (to one decimal place)

The accompanying data are the number of wins and the earned run averages (mean number of earned runs allowed per nine innings pitched) for eight baseball pitchers in a recent season. Find the equation of the regression line. Then construct a scatter plot of the data and draw the regression line. Then use the regression equation to predict the value of y for each of the given x-values, if meaningful. If the x-value is not meaningful to predict the value of y, explain why not. (a) x = 5 wins (b) x = 10 wins (c) x = 19 wins (d) x = 15 wins E Click the icon to view the table of numbers of wins and earned run average. The equation of the regression line is y =x+ (Round to two decimal places as needed.) Construct a scatter plot of the data and draw the regression line. Choose the correct graph below. OA. OB. OC. OD. AERA 6- AERA AERA AERA 2- 2- 2- 0- 0- 12 18 24 12 18 24 12 18 24 12 18 24 Wins Wins Wins Wins (a) Predict the ERA for 5 wins, if it is meaningful. Select the correct choice below…

You are studying how a penguin's flipper length (in mm) explains its body mass (in grams) using linear regression. You choose a non-directional alternative to be safe. You calculate an R2=0.7621. What is the interpretation of this value? O76.21% of the variability in body mass is explained by the flipper length. O 76.21% of the variability in flipper length is explained by the body mass. 0 Flipper length and body mass have a positive relationship becase R² is positive O76.21% of the body mass values will be inside the confidence interval

Use the regression line to make the appropriate prediction. A random sample of records of electricity usage of homes in the month of July gives the amount of electricity used and size (in square feet) of 135 homes. A regression was done to predict the amount of eleçtricity used (in kilowatt-hours) (y) from size (x). The residuals plot indicated that a inear model is appropriate. The model is y = 0.2x+ 1271. How much electricity would you predict would be used in a house that is 2471 square feet? 494.2 kilowatt-hours O 6000.00 kilowatt-hours O 1765.2 kilowatt-hours O 776.8 kilowatt-hours 3742.2 kilowatt-hours