14) - Part Ajavascript:WebForm_DoCallback('__Page',H_6.Chapter 7Q Search tA regression model relating x, number of salespersons at a branch office, to`y, annual sales at the office (in thousands of dollars) provided the following regression output. WhereTtotal = 26,ANOVAdfSSMS FSignificance FRegression7,191.1ResidualTotal9,555.8CoefficientsStandard Errort StatP-valueIntercept71.011.707Number of57.05.893Salespersonsa. Write the estimated regression equation (to whole number).b. Compute the F statistic and test the significance of the relationship at a 0.05 level of significance. (to 2 decimals)F-valueНо, we - Select your answer -p-value is - Select your answer -c. Compute the t statistic and test the significance of the relationship at a 0.05 level of significance. (to 2 decimals)t StatV, we, we- Select your answer - v Ho: B, =p-value isSelect your answer-d. Predict the annual sales at the Memphis branch office. This branch employs 11 salespersons.24thousand (to whole number)MacBook Air

The independent variable is number of sales persons. The dependent variable is annual sales. We have…

Answered: A regression model relating x, number…

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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A group of 13 healthy children and adolescents participated in a phycological study designed to analyze the relationship between age and average total sleep time (ATST). To obtain a measure for ATST (in minutes), recordings were taken on each subject on three consecutive nights and then averaged. Results are provided to you in Sleep&Age.xlsx Download Sleep&Age.xlsx file. (2 points) Determine the least-squares regression line for predicting average total sleep time using age. (2 points) Make a scatter plot of the data with ATST on the y-axis (vertical axis) and Age on the x-axis (horizontal axis) with least squares regression line overlaid on the top (i.e.: obtain the fitted line plot). Make sure to attach the plot below. (7 points) Check the assumptions for the simple linear regression. Attach any plots you used check the assumptions and comment on them. (7 points) We want to see if the average sleep time decreases as the children grow older. Write the appropriate null and…
Suppose the regression in Equation is estimated using LoSTR andLoEL in place of HiSTR and HiEL, where LoSTR = 1 - HiSTR is anindicator for a low-class-size district and LoEL = 1 - HiEL is an indicatorfor a district with a low percentage of English learners. What are thevalues of the estimated regression coefficients?
Compute the least-square regression line for predicting the president's age from the first lady's age. Round the slope and -intercept values to at least four decimal places.
A researcher is hired to investigate the relationship between the number of unauthorized days that employees are absent per year and the distance in miles between home and work for the employees. A sample of 10 employees was chosen and the results are listed in the chart below. Use the proper Data Analysis function in Excel to computethe regression equation needed to predict the number of missed days that could be expected foremployees who live certain distances away from the workplace. Distance to Work (miles): 1, 3, 4, 6, 9, 10, 12, 14, 14, 18 Number of Days Absent: 9, 6, 7, 7, 6, 4, 5, 3, 4, 2
Compute the least-squares regression line for predicting U.S. emissions from non-U.S. emissions. Round the slope and y-intercept values to four decimal places.
In an attempt to develop a model of wine quality as judged by wine experts, data was collected from 50 red wine variants of a certain type of wine. A multiple linear regression model to predict wine quality, measured on a scale from 0 (very bad) to 10 (excellent) was developed, based on alcohol content (%), X1, and the amount of chlorides, X2. Perform a multiple regression analysis and determine the VIF for each independent variable in the model. Is there reason to suspect the existence of collinearity? Determine the VIF for each independent variable in the model, with VIF1 and VIF2 being the VIF for X1 and X2, respectively.
The new manager of an Information Technology company collected data for a sampleof 20 computer programmers in the organization to perform a multiple regressionanalysis on the structure of their salaries. The aim of this manager in this exercise is todetermine if the Salary (y) of a hired computer programmer was related to the years ofExperience (??) in the organization and also the Score (??) of the programmers duringtheir first interview aptitude test scores. The years of experience, score on the aptitudetest and the corresponding annual salary (in thousands of Ghana cedis) for a sample ofthe 20 programmers is shown in the Regression statistics table below;Experience (??)(in years)Score (??)(out of 100%)Salary (y)(GH¢ 000)4 78 247 100 431 86 23.75 82 34.38 86 35.810 84 380 75 22.21 80 23.14 | P a g e6 83 306 91 339 88 382 73 26.610 75 36.25 81 31.66 74 298 87 344 79 30.16 94 33.93 70 28.23 89 30Managerial Report 2:Use Multiple Regression Analysis to help management develop a Salary…
Each year, students in an elementary school take a standardized math test at the end of the school year. For a class of fourth-graders, the average score was 55.1 with a standard deviation of 12.3. In the third grade, these same students had an average score of 61.7 with a standard deviation of 14.0. The correlation between the two sets of scores is ?=0.95. Calculate the equation of the least-squares regression line for predicting a fourth-grade score from a third-grade score.
Suppose that a regional express delivery service company wants to estimate the cost of shipping a package (Y) as a function of cargo type, where cargo type includes the following possibilities: fragile, semi-fragile, and durable. Costs for 15 randomly chosen packages of approximately the same weight and same distance shipped, but of different cargo types, are provided in the file P14_16.xlsx. a. Estimate a regression equation using the given sample data, and interpret the estimated regression coefficients. b. According to the estimated regression equation, which cargo type is the most costly to ship? Which cargo type is the least costly to ship? c. How well does the estimated equation fit the given sample data? How might the fit be improved? d. Given the estimated regression equation, predict the cost of shipping a package with semi-fragile cargo.
What is the maximum and mininumber of the range regarding the explanatory variable for which the regression model can make valid predictions?
how to plot the estimated regression line on the scatter diagram in letter c and how to solve for letter d with graph
Suppose there is a significant correlation between variables. describe 2 cases under which it might be inappropriate to use the linear regression equation for prediction

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