Table 1 regress size hhinc Source df MS Number of obs 5,407 %3D F(1, 5405) 1613.34 %3D Model 263141566 1 263141566 Prob > F 0.0000 %3D Residual R-squared %3D Adj R-squared Root MSE %3D Total 1.1447e+09 5,406 211749.457 size Coefficient Std. err. P>|t| [95% conf. interval] hhinc .0082545 .0002055 40.17 0.000 .0078516 .0086574 _cons 800.9835 9.404917 85.17 0.000 782.5461 819.4209 Compute the Sum of Squared Residuals (SSR), R-squared (R^2), and Root Mean Squared Error from Table 1 (Root MSE), Interpret the R-squared.

The data show systolic and diastolic blood pressure of certain people. Find the regression equation, letting the systolic reading be the independent (x) variable. If one of these people has a systolic blood pressure of 125 mm Hg, what is the best predicted diastolic blood pressure? Systolic Diastolic Click the icon to view the critical values of the Pearson correlation coefficient r. What is the regression equation? ŷ-+x (Round to two decimal places as needed.) What is the best predicted diastolic blood pressure? y=(Round to one decimal place as needed.) C 148 115 82 83 82 136 115 127 128 140 97 60 65 93 145 101 108

Find the equation of the regression line for the given data. Then construct a scatter plot of the data and draw the regression line. (The pair of variables have a significant correlation.) Then use the regression equation to predict the value of y for each of the given x-values, if meaningful. The table below shows the heights (in feet) and the number of stories of six notable buildings in a city. Height, x Stories, y 519 (a) x = 500 feet (c) x = 810 feet (b) x = 649 feet (d) x = 732 feet 775 619 508 491 474 36 53 47 44 43 37 Find the regression equation. y=x+ O (Round the slope to three decimal places as needed. Round the y-intercept to two decimal places as needed.) Choose the correct graph below. OA. 60- 60- 60- 60- 800 Height (feet) 800 Height (feet) 800 Height (feet) Height (feet) (a) Predict the value of y for x = 500. Choose the correct answer below. A. 52 В. 40 C. 48 D. not meaningful (b) Predict the value of y for x = 649. Choose the correct answer below. A. 56 В. 48 O C. 40…

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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…

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 E Click the icon to view the table of numbers of wins and earned run average. (b) x = 10 wins (c) x = 19 wins (d) x= 15 wins ..... The equation of the regression line is y = x+O (Round to two decimal places as needed.) Wins and ERA Earned run Wins, x average, y 20 2.71 18 3.19 17 2.69 16 3.68 14 3.94 12 4.25 11 3.86 9 5.18 Print Done

Researchers are interested in predicting the height of a child based on the heights of their mother and father. Data were collected, which included height of the child ( height), height of the mother ( mothersheight ), and height of the father (fathersheight ). The initial analysis used the heights of the parents to predict the height of the child (all units are inches). The results of the analysis, a multiple regression, are presented below. . regress height mothersheight fathersheight Source Model Residual Total height mothersheight fathersheight _cons SS 208.008457 314.295372 522.303829 df 2 104.004228 8.49446952 37 MS 39 13.3924059 Coef. Std. Err. .6579529 .1474763 .2003584 .1382237 9.804327 12.39987 t P>|t| 4.46 0.000 C 0.156 0.79 0.434 Number of obs = F( 2, 37) = Prob > F R-squared Adj R-squared = Root MSE = = .3591375 -.0797093 -15.32021 = 40 12.24 0.0001 0.3983 0.3657 2.9145 [95% Conf. Interval] .9567683 .4804261 34.92886 What are the null and alternative hypotheses…

Find the equation of the regression line for the given data. Then construct a scatter plot of the data and draw the regression line.​ (The pair of variables have a significant​ correlation.) Then use the regression equation to predict the value of y for each of the given​ x-values, if meaningful. The table below shows the heights​ (in feet) and the number of stories of six notable buildings in a city. height 775 619 519 508 491 474  ​(a) x=503 feet ​(b) x=644 feet ​Stories, y 53 47 44 42 39 38   ​(c) x=798 feet ​(d) x=734 feet

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 Click the icon to view the table of numbers of wins and earned run average. (b) x = 10 wins (c) x = 21 wins (d) x = 15 wins ERA 6- ERA 6- AERA 6- ERA 6- 4- 4- 4- 4- 2- 2- 2- 2- 0+ 6 0- 0- 0- 12 18 24 6. 12 18 24 12 18 24 6 12 18 24 Wins Wins Wins Wins (a) Predict the ERA for 5 wins, if it is meaningful. Select the correct choice below and, if necessary, fill in the answer box within your choice. A. ŷ= (Round to two decimal places as needed.) B. It is not meaningful to predict this value of y because…

The file Galton on D2L contains the 928 observations Francis Galton used in 1885 to estimate the relationship between the heights of parents and the heights of their children. The column Children refers to the height (in inches) of a child, and the column Mid-Parents refers to the average height (in inches) of the mother and father of that child. You can download this file into Excel and Minitab. a. Calculate the regression Height of Children = a +b (Height of Mid-Parents). b. Calculate the average for Height of Children, and calculate the average Height of Mid-Parents. c. Create a new variable in Minitab which is the Height of Children measured in terms of deviations from its mean. Call this new variable y. Also, create a new variable in Minitab with is the Height of Mid-Parents measured in terms of deviations from its mean. Call this new variable x. Calculate the regression y = a + bx. You can create the new y and x variables in Excel of Minitab, whichever you find more convenient.…