### Understanding the Relationship Between Commute Time and Well-Being ScoreThe data below represent commute times (in minutes) and scores on a well-being survey.| Commute Time (minutes), x | 5 | 15 | 30 | 35 | 60 | 72 | 105 ||---------------------------|-----|-----|-----|-----|-----|-----|-----|| Well-Being Index Score, y | 69.0 | 67.7 | 66.1 | 65.7 | 63.5 | 63.2 | 59.6 |Using this data, we find the least-squares regression line treating the commute time, \( x \), as the explanatory variable and the index score, \( y \), as the response variable:\[ y = -0.089x + (69.06) \]#### Steps to Interpret the Regression Equation:1. **Slope Interpretation:** - The slope of the regression line is -0.089. This means that for every unit increase in commute time (in minutes), the well-being index score decreases by 0.089 units, on average. - **Correct Choice:** - **A.** For every unit increase in index score, the commute time falls by 0.090, on average. - **Note:** This interpretation is reversed; it needs to be corrected as: For every unit increase in commute time, the index score falls by 0.089, on average. - **B.** For a commute time of zero minutes, the index score is predicted to be [fill-in required]. - **C.** For an index score of zero, the commute time is predicted to be [fill-in required]. - **D.** For every unit increase in commute time, the index score falls by [fill-in required], on average. - **E.** It is not appropriate to interpret the slope.2. **Y-Intercept Interpretation:** - The y-intercept of the regression line is 69.06. This indicates that when the commute time is zero (i.e., if someone has no commute time), the predicted well-being index score would be 69.06. - **Correct Choice:** - **B.** For a commute time of zero minutes, the index score is predicted to be **69.06

Use EXCEL to determine the regression equation. EXCEL procedure: Go to EXCEL Go to Data>Data…

Answered: Bata Below represent commute times (in…

MATLAB: An Introduction with Applications

6th Edition

ISBN:9781119256830

Author:Amos Gilat

Publisher:Amos Gilat

Chapter1: Starting With Matlab

Section: Chapter Questions

Problem 1P

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The data below represent commute times (in minutes) and scores on a well-being survey. Complete parts (a) through (d) below. Commute Time (minutes), x Well-Being Index Score, y 20 30 35 60 84 105 O 69 1 67.8 66.8 66.6 65.1 64.4 62.5 (a) Hind the least-squares regression line treating the commute time, x, as the explanatory vaiable and the index score, y, as the response va %3D y3-0 0.061 x + (68.980) (Round to three decimal places as needed.) (b) Interpret the slope and y-intercept, if appropriate. First interpret the slope. Select the correct choice below and, if necessary, fill in the answer box to complete your choice minutes A. For an index score of zero, the commute time is predicted to be (Round to three decimal places as needed.) D For a commute time of zero minutes, the index score is predicted to bel (Round to three decimal places as needed) on average. For every unit increase in index score, the commute time falls by (Round to three decimal places as needed.) on averagel For…
show how to do it on a TI-84 please and thank you From the data, find the least-squares regression line. What type of correlation is there? Study Hours Test Score 0 20 1 35 2 50 4 47.5 4 65 5 72.5 5 75 6 67.5 6 92.5 7 85 7 90 8 95 5 70 y=9.051+24.765x.y=9.051+24.765x. There is positive linear correlation. y=24.765−9.051x.y=24.765-9.051x. There is negative linear correlation. y=24.765+9.051x.y=24.765+9.051x. There is positive linear correlation. y=−24.765+9.051x.y=-24.765+9.051x. There is negative linear correlation y=9.051−24.765x.y=9.051-24.765x. There is negative linear correlation.
Is It Getting Harder to Win a Hot Dog Eating Contest?Every Fourth of July, Nathan’s Famous in New York City holds a hot dog eating contest. The table below shows the winning number of hot dogs and buns eaten every year from 2002 to 2015, and the data are also available in HotDogs. The figure below shows the scatterplot with the regression line. Year Hot Dogs 2015 62 2014 61 2013 69 2012 68 2011 62 2010 54 2009 68 2008 59 2007 66 2006 54 2005 49 2004 54 2003 45 2002 50 Winning number of hot dogs in the hot dog eating contest Winning number of hot dogs and buns Click here for the dataset associated with this question. (a) Is the trend in the data mostly positive or negative? Positive Negative (b) Using the figure provided, is the residual larger in 2007 or 2008?Choose the answer from the menu in accordance to item (b) of the question statement 20072008 Is the residual positive or…
In baseball, two statistics, the ERA (Earned Run Average) and the WHIP (Walks and Hits per Inning Pitched), are used to measure the quality of pitchers. For both measures, smaller values indicate higher quality. The following computer output gives the results from predicting ERA by using WHIP in a least-squares regression for the 2017 baseball season. Variable DF Estimate SE T Intercept 1 -5.0 0.26 - 19.3 WHIP 1 6.8 0.14 47.4 Which of the following statements is the best interpretation of the value 6.8 shown in the output? ERA is predicted to increase by 6.8 units for each 1 unit increase of WHIP. WHIP is predicted to increase by 6.8 units for each 1 unit increase of ERA. For a pitcher with 0 units of WHIP, the ERA is predicted to be approximately 6.8 units. For a pitcher with 0 units of ERA, the WHIP is predicted to be approximately 6.8 units. Approximately 6.8% of the variability in ERA is due to its linear relationship with WHIP.
The data below represents commute times (in minutes) and scores on a well-being survey. Complete parts (a) through (d) below. Commute time (in minutes), x 5, 20, 25, 40, 60, 84, 105 Well-being index score, y 69.1, 67.8, 67.1, 66.3, 66.0, 64.3, 62.3 Find the least-squares regression line treating the commute time, x, as the explanatory variable and the index score, y, as the response variable. y=___x + ____ (round to three decimal places as needed).
Statistical software is generally used to test the significance of a regression model. The table below shows software output for a least squares regression line. Fill in the blanks with the correct answers, using the table above. Assume a significance level of 0.05. The variable "unemp" is the_______variable. The value of the slope of the regression line is_______. The slope of the regression line is not significantly different from______because the p-value is______ which is larger than 0.05.
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Hi I need help with the second question "What is the best model using the t-statistic criteria? Select the best answer below and fill in the answer boxes to complete your choice." Please let me know what option to choose from A-G and what numbers to put in the boxes.
The data below represent commute times (in minutes) and scores on a well-being survey. Complete parts (a) through (d) below. Commute Time (minutes), x 15 25 35 60 84 105 O Well-Being Index Score, y 69.0 67.7 66.5 65.6 63.4 62.0 59.4 (a) Find the least-squares regression line treating the commute time, x, as the explanatory variable and the index score, y, as the response variable. (Round to three decimal places as needed.)
The data below represent commute times (in minutes) and scores on a well-being survey. Complete parts (a) through (d) below. 20 30 35 60 84 105 O Commute Time (minutes), x Well-Being Index Score, y 69.1 67.8 66.8 66.6 65.1 64.4 62.5 (a) Find the least-squares regression line treating the commute time, x, as the explanatory variable and the index score, y, as the respont (Round to three decimal places as needed.)
Using the sample data from the accompanying table, complete parts (a) and (b). E Click the icon to view the data table (a) Explain why it does not make sense to construct confidence or prediction intervals based on the least-squares regression equation. Choose the correct answer below. O A. It does not make sense to construct confidence or prediction intervals based on the least-squares regression equation because there is a linear relationship between sugar content and calories in high-protein and moderate protein energy bars. O B. It does not make sense to construct confidence or prediction intervals based on the least-squares regression equation because there is no linear relationship between sugar content and calories in high-protein and moderate protein energy bars. O C. It does not make sense to construct confidence or prediction intervals based on the least-squares regression equation because the residuals are not normally distributed. (b) Construct a 95% confidence interval for…
The data below represent the number of days absent, x, and the final grade, y, for a sample of college students at a large university. Complete parts (a) through (e) below. No. of absences, x Final grade, y 1 2 3 4 7 8 9 89.0 86.2 83.2 80.8 77.8 73.4 63.8 68.3 65.4 62.6 (a) Find the least-squares regression line treating the number of absences, x, as the explanatory variable and the final grade, y, as the response variable. y = - 3.110 x + ( 89.045 ) (Round to three decimal places as needed.) (b) Interpret the slope and y-intercept, if appropriate. Interpret the slope. Select the correct choice below and, if necessary, fill in the answer box to complete your choice. (Round to three decimal places as needed.) O A. For every unit change in the final grade, the number of days absent falls by days, on average. O B. For a final score of zero, the number of days absent is predicted to be days. O C. For every day absent, the final grade falls by on average. O D. For zero days absent, the…

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