A simple regression model was fitted to estimate the credit score of customers based on their income. The histogram of the residuals of the regression model is shownbelow.Frequency20151050ToBased on this plot, what can you say about the validity of the regression model?18-150 -100 -50 0 50 100 150 Based on this plot, what can you say about the validity of the regression model?AnswerTablesKeypadKeyboard ShortcutsO The histogram of the residuals resembles a normal distribution, so the normality assumption of the regression model is satisfied.O The histogram of the residuals resembles a normal distribution, so the normality assumption of the regression model is not satisfied.O The histogram of the residuals does not resemble a normal distribution, so the normality assumption of the regression model is satisfied.The histogram of the residuals does not resemble a normal distribution, so the normality assumption of the regression model is not satisfied.

From the above histogram we can see that, the tail of the histogram is on right side. So the data is…

Answered: A simple regression model was fitted to…

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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On-base percentage plus slugging (OPS) is a statistic used in baseball to measure a team's batting success. The number of runs scored and OPS for 30 baseball teams was used to conduct a linear regression analysis. The scatterplot and computer output for the regression analysis is shown. 900- 850- 800- Number of 750- Runs Scored 700- 650- 600- 0.650 0.675 0.700 0.725 0.750 0.775 0.800 OPS Term Coef SE Coef Constant -838.40 77.99 OPS 2144.3 107.1 T-Value -10.75 20.01 P-Value <0.0001 < 0.0001 S = 19.516 R-Sq = 93.47% R-Sq(adj) 93.23% Which of the following is the most appropriate interpretation of the statistic 93.47% in the regression output? (A) There is a strong, positive, linear relationship between number of runs scored and OPS. (B) The typical deviation between observed and predicted number of runs scored is 0.9347. (C) For each one-unit increase in OPS, the regression model predicts an increase of 93.47 runs scored. (D) 93.47% of the observed number of runs scored are close to the…
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What is the regression line
Prices and mileage of 48 Dodge 2500 Pickup trucks with Cummins diesel engines. Prices ranged from $28,925 to $68,650. Mileage ranged from 315 to 186,466. While I did not share the trucks were 2013 to 2020 models. Please show all work. Interpret b0 in the context of this problem. Interpret b1 in the context of this problem. What percent of total variation in prices is explained by the regression model? Provide evidence to support your answer. Price Mileage 52293 34450 50987 23372 62988 21738 37500 81176 66000 23118 47601 71143 47750 18214 42000 32303 50987 23372 28925 143737 42050 58561 51738 9691 51500 23969 44988 48781 29500 114809 46975 16918 63999 15507 46975 16918 43222 50459 38386 87192 42000 68722 62250 14042 62998 21738 32130 103644 52293 34450 52900 68883 53172 3656 53494 315 39594 88441 63246 23631 58250 34974 59391 18383 50650 42737 54976 84200 68650 14401 51114 47433 43900 74106 37450…
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
What is the regression equation? (Round to decimal one) The best predicted weight of a bear with a chest size 43 inches is how many pounds???? Round to decimal one)
A physician wishes to know whether there is a relationship between a father’s weight (in pounds) and his new born sons’ weight (in pounds). The data are given here Fathers Weight 176 145 187 210 196 142 205 215 152 145 Sons Weight 6.6 8.2 9.2 7.1 8.8 9.3 7.1 8.6 7.8 6.0 Develop the regression equation for the sample Predict the sons weight based on the father’s weight of 300, 250 and 400.
Using the data, run a regression where you control for “Promotion,” and test the effect of “Wins” on “Attendance”. Does “Promotion” have a significant effect on “Attendance”? Wins Promotion Attendance 4 29500 36300 6 55700 40100 6 71300 41200 8 87000 53000 6 75000 44000 7 72000 45600 5 55300 39000 7 81600 47500
Use the value of the linear correlation coefficient to calculate the coefficient of determination. What does this tell you about the explained variation of the data about the regression line? About the unexplained variation? r= -0.438 please dont copy paste the content from other sites
statistic please answer
Using the data in the Excel spreadsheet "auto.xlsx", estimate a regression to answer the following question. A 1 inch increase in LENGTH corresponds to a _________________ increase in MPG (miles per gallon). Use negative values if it results in a decreased price. Round your answers to two decimals. make price mpg rep78 headroom trunk weight length turn displacement gear_ratio foreign AMC Concord 4,099 22 3 3 11 2,930 186 40 121 3.58 Domestic AMC Pacer 4,749 17 3 3 11 3,350 173 40 258 2.53 Domestic AMC Spirit 3,799 22 3 12 2,640 168 35 121 3.08 Domestic Buick Century 4,816 20 3 5 16 3,250 196 40 196 2.93 Domestic Buick Electra 7,827 15 4 4 20 4,080 222 43 350 2.41 Domestic Buick LeSabre 5,788 18 3 4 21 3,670 218 43 231 2.73 Domestic Buick Opel 4,453 26 3 10 2,230 170 34 304 2.87 Domestic Buick Regal 5,189 20 3 2 16 3,280 200 42 196 2.93 Domestic Buick Riviera 10,372 16 3 4 17 3,880 207 43 231 2.93 Domestic Buick Skylark 4,082 19 3 4 13 3,400 200 42 231…
What is the explanatory variable, what is the response variable. What is the regression equation for this data. Plot the regression line in your calculator and draw it on your graph. Interpret in context the slope. Interpret in context the y-intercept. What is the residual at 45 years of age. Plot the residual plot on the axis to the right

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