A sales manager collected the following data on x = years of experience and y = annual sales ($1,000s). The estimated regression equation for these data is ŷ= 80 + 4xX.Years ofAnnual SalesSalespersonExperience($1,000s)118023973492441026.10381117101198101239111171013136(a) Compute SST, SSR, and SSE.SST =SSR =SSE =(b) Compute the coefficient of determination r. (Round your answer to three decimal places.)p? =%3D (c) What is the value of the sample correlation coefficient? (Round your answer to three decimal places.)

Answered: A sales manager collected the following…

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. run a simple regression- dependent variable is Weeks, independent variable is Age. B. run a multiple regression with dependent variable weeks and independent variable-age, married, head, manager and sales. C. Create the regular and standardized residual plots for both. Please show the tables when entering values of the regression for both the outputs and the scatter plots.
The tables show the amount of revenue (y) in billions for different companies x years after 2005. Match the table with the correct description of its regression model and correlation. Negative Linear Regression 1. a0 1 2 3 4 5 6 78 9 10 Strong Negative Correlation y 12 12 13 14 15 16 18 19 21 22 25 Positive Linear Regression * 0 1 2 3 4 5 6 7 89 10 y 20 18 17 15 13 14 16 18 21 25 28 2. Strong Positive Correlation Quadratic Regression * 0123 4 5 6 7 8 9 10 3. Strong Positive Correlation y 5679 13 15 18 24 25 28 30 Increasing Exponential Regression 4. x0 1 2 3 4 5 6 789 10 Strong Positive Correlation y 20 20 19 18 16 15 12 8765 Decreasing Exponential Regression z0 1 2 3 4 5 6 7 8 9 10 5. Strong Negative Correlation y 20 19 20 18 16 15 13 11 10 11 10
Use the data given below to answer a -c. Heigh (IN) 71 70.5 71 72 70 66.5 70 71 Weight (lb) 125 119 128 128 119 127 123 105 Find the regression equation letting the first variable (height) to be the independent variable (x). Find the best-predicted weight for a person whose height is 69in. What percentage of the variation in weight can be explained by the linear relationship between height and weight.
A sales manager collected the following data on x = years of experience and y = annual sales ($1,000s). The estimated regression equation for these data is ý = 80 + 4x. Years of Annual Sales Salesperson Experience ($1,000s) 1 1 80 3 97 3 4 92 4 4 102 103 6 8 111 7 10 119 8 10 123 9 11 117 10 13 136 (a) Compute the residuals. Years of Annual Sales Residuals Experience ($1,000s) 1 80 97 4 92 4 102 6 103 8 111 10 119 10 123 11 117 13 136
The following table shows the reported net sales by Nokia in 2008, 2009, and 2010. Year 2008 2009 2010x 0 1 2Sales ($ millions) 20 50 60 Find the Equation of the Linear Regression Line. Rounds decimal values to the nearest tenths.
Use the scatterplot of Vehicle Registrations below to answer the questions Vehicle Registrations in the United States, 1925- 2011 Vehicles millions 300 y = 3.0161x - 5819.5 R? = 0.9695 250 200 150 100 50 1920 -50 1940 1960 1980 2000 2020 Year Write a sentence explaining the value of the slope for this regression line. For every increase in year, the number of vehicle registrations in the US increases by 3.0161 million. For every increase in year, the number of vehicle registrations in the US increases by 5819.5. For every increase in vehicle registrations in the US, the number of years increases by 5819.5. For every increase in vehicle registrations in the US, the number of years increases by 3.0161 million. Registrations (in millions)
Find the equation of the regression line for the following data: X 23 43 29 32 46 22 37 y 56 8 44 35 13 48 20
1.
A sales manager collected the following data on x = years of experience and y = annual sales ($1,000s). The estimated regression equation for these data is ŷ = 81 + 4x. Salesperson Years ofExperience Annual Sales($1,000s) 1 1 80 2 3 97 3 4 92 4 4 107 5 6 103 6 8 111 7 10 119 8 10 128 9 11 117 10 13 136 (a) Compute the residuals. Years ofExperience Annual Sales($1,000s) Residuals 1 80 3 97 4 92 4 107 6 103 8 111 10 119 10 128 11 117 13 136 Construct a residual plot. A residual plot has 10 points plotted on it. The horizontal axis ranges from 0 to 14 and is labeled: Years of Experience. The vertical axis ranges from −16 to 16 and is labeled: Residuals. There is a horizontal line that spans the graph at 0 on the vertical axis. There are 6 points below the line and 4 points above the line. The points are between 1 to 13 on the horizontal axis and appear to vary randomly between −8 to 10 on the vertical axis.…
A sales manager collected the following data on x = years of experience and y = annual sales ($1,000s). The estimated regression equation for these data is ŷ = 80 + 4x. Salesperson Years ofExperience Annual Sales($1,000s) 1 1 80 2 3 97 3 4 92 4 4 102 5 6 103 6 8 111 7 10 119 8 10 123 9 11 117 10 13 136 (a) Compute SST, SSR, and SSE. SST=SSR=SSE= (b) Compute the coefficient of determination r2. (Round your answer to three decimal places.) r2 = Comment on the goodness of fit. (For purposes of this exercise, consider a proportion large if it is at least 0.55.) The least squares line provided a good fit as a small proportion of the variability in y has been explained by the least squares line.The least squares line provided a good fit as a large proportion of the variability in y has been explained by the least squares line. The least squares line did not provide a good fit as a large proportion of the variability in y has been explained by the…
Develop a scatterplot and explore the correlation between customer age and net sales by each type of customer (regular/promotion). Use the horizontal axis for the customer age to graph. Find the linear regression line that models the data by each type of customer. Round the rate of changes (slopes) to two decimal places and interpret them in terms of the relation between the change in age and the change in net sales. What can you conclude? Hint: Rate of Change = Vertical Change / Horizontal Change = Change in y / Change in x
Explain how to find se

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