Fit a linear least square regression equation of production rating odevises a manual dexterity test for job applicants to predict their productionrating in the assembly department. In order to do this he selects a random sampleof 10 applicants. They are given the text and later assigned a production rating-Results are as follows:The personnel manager of an electronic manufacturing companyWorkerc| DABE FGHIJTest Score5336888486Production6445486939Rating45438979846649484376on test score.

Solution is given amd i hope the solution is helpful for you. Introduction:

Answered: Fit a linear least square regression…

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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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…
An educator wants to see the number of absences(X) a student in her class has affects the Student’s final GPA(Y)a) Find Correlation coefficient b) Draw a Scatter plot c) Find the Regression equation of Y on X d) Predict the student’s GPA when X = 6 x 4 5 2 8 3 10 7 y 2.5 2.3 4 2.1 3.3 1.7 2.3
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.
Mike's Stables has two different possible cost drivers (trainer hours and number of horses) for budgeting the cost of horses training. The most recent results of the two regression analysis are as follows: Trainer's hours: SUMMARY OUTPUT – Regression Results Regression Statistics R Square 0.56 Coefficients t Stat Intercept 1,004.65 4.61 X Variable 1 $22.99 7.11 Number of horses: SUMMARY OUTPUT – Regression Results Regression Statistics R Square 0.63 Coefficients t Stat Intercept $5,240.95 4.44 X Variable 1 $951.48 3.50 What is the estimated total training horses cost for the coming year if 15,000 trainer hours are incurred and the stable has 350 horses to be trained, based upon the best cost driver?
An article gave a scatter plot along with the least squares line of x = rainfall volume (m³) and y = runoff volume (m³) for a particular location. The accompanying values were read from the plot. X 4 12 14 20 23 30 40 50 55 67 72 83 96 112 127 y 4 10 13 14 15 25 27 45 38 46 53 75 82 99 104 USE SALT (a) Does a scatter plot of the data support the use of the simple linear regression model? o Yes, the scatterplot shows a reasonable linear relationship. Yes, the scatterplot shows a random scattering with no pattern. No, the scatterplot shows a reasonable linear relationship. No, the scatterplot shows a random scattering with no pattern. (b) Calculate point estimates of the slope and intercept of the population regression line. (Round your answers to four decimal places.) slope X X .84210 intercept -1.86631 (c) Calculate a point estimate of the true average runoff volume when rainfall volume is 45. (Round your answer to four decimal places.) 40.2387 X m 3 (d) Calculate a point estimate of…
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Report the equation of the regression line and interpret it in the context of the problem
Using data from 50 workers, a researcher estimates Wage = β0 + β1Education + β2Experience + β3Age + ε, where Wage is the hourly wage rate and Education, Experience, and Age are the years of higher education, the years of experience, and the age of the worker, respectively. The regression results are shown in the following table. Coefficients Standard Error t Stat p-Value Intercept 8.93 3.96 2.26 0.0289 Education 1.34 0.38 3.53 0.0010 Experience 0.48 0.20 2.40 0.0205 Age −0.04 0.05 −0.80 0.4278 a-1. Interpret the point estimate for β1. multiple choice 1 As Education increases by 1 year, Wage is predicted to increase by 1.34/hour. As Education increases by 1 year, Wage is predicted to increase by 0.48/hour. As Education increases by 1 year, Wage is predicted to increase by 1.34/hour, holding Age and Experience constant. As Education increases by 1 year, Wage is predicted to increase by 0.48/hour, holding Age and Experience constant. a-2. Interpret the point estimate for β2. multiple…
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Using data from 50 workers, a researcher estimates Wage Be + B₁Education + B2Experience + B3Age +, where Wage is the hourly wage rate and Education, Experience, and Age are the years of higher education, the years of experience, and the age of the worker, respectively. A portion of the regression results is shown in the following table. Coefficients Standard Error t Stat p-Value Intercept 7.45 3.79 1.97 0.0554 Education 1.06 0.37 2.86 0.0063 Experience 0.37 0.18 2.06 0.0455 Age -0.02 0.06 -0.33 0.7404 a-1. Interpret the point estimate for ẞ1. As Education increases by 1 year, Wage is predicted to increase by 1.06/hour. As Education increases by 1 year, Wage is predicted to increase by 0.37/hour. As Education increases by 1 year, Wage is predicted to increase by 1.06/hour, holding Age and Experience constant. As Education increases by 1 year, Wage is predicted to increase by 0.37/hour, holding Age and Experience constant. a-2. Interpret the point estimate for ẞ2. ○ As Experience…
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