Consider the following computer output from a multiple regression analysis relating the cost of car insurance to the variables: number of car accidents, driver's creditscore, and safety rating of the car.CoefficientsCoefficientsStandard Errort StatP-valueIntercept95697.239.8320.0000Car Accidents172.0818.249.4340.0000(In last 3 years)Credit ScoreSafety Rating105.16201.030.5230.6030-207.8120.46- 10.1570.0000Does the sign of the coefficient for the variable credit score make sense?AnswerNo, because it is expected that as the credit score increases then the cost should decrease.○ No, because it is expected that as the credit score increases then the cost should also increase.○ Yes, because it is expected that as the credit score increases then the cost should decrease.○ Yes, because it is expected that as the credit score increases then the cost should also increase.TablesKeypadKeyboard Shortcuts

The objective of the question is to understand if the sign of the coefficient for the variable…

Answered: Consider the following computer output…

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 Cadet is a popular model of sport utility vehicle, known for its relatively high resale value. The bivariate data given below were taken from a sample of sixteen Cadets, each bought new two years ago, and each sold used within the past month. For each Cadet in the sample, we have listed both the mileage x (in thousands of miles) that the Cadet had on its odometer at the time it was sold used and the price y (in thousands of dollars) at which the Cadet was sold used. With the aim of predicting the used selling price from the number of miles driven, we might examine the least-squares regression line, y=41.57 – 0.49.x. This line is shown in the scatter plot in Figure 1. Used selling price, Mileage, x (in thousands) (in thousands of dollars) 25.9 26.1 28.1 26.2 40- 21.1 31.4 24.0 27.5 35 27.2 30.9 38.7 21.4 30. 34.6 25.5 37.2 23.5 15.6 34.0 25- 23.8 28.0 20.9 30.9 20. 23.1 32.7 28.0 30.3 40 29.2 28.1 Figure 1 24.0 29.6 23.0 31.5 Send data to Excel
Consider the following computer output from a multiple regression analysis relating the cost of car insurance to the variables: number of car accidents, driver's credit score, and safety rating of the car. Answer Coefficients Coefficients Standard Error 680 Intercept Car Accidents (In last 3 years) Credit Score -74.80 Safety Rating - 104.25 122.40 Does the sign of the coefficient for the variable number of car accidents make sense? 79.71 14.75 8.89 11.46 t Stat P-value 8.531 0.0000 0.0000 8.298 -8.414 -9.097 0.0000 0.0000 O Yes, because it is expected that as the number of car accidents increases then the cost should also increase. O No, because it is expected that as the number of car accidents increases then the cost should also increase. O Yes, because it is expected that as the number of car accidents increases then the cost should decrease. O No, because it is expected that as the number of car accidents increases then the cost should decrease. Keyp- Keyboard Short Tables
Consider the following computer output from a multiple regression analysis relating the price of a used car to the variables: age of car, mileage, and safety rating. Intercept Age (Year) Mileage (in Thousands) Safety Rating Does the sign of the coefficient for the variable age make sense? Answer Coefficients 23287.65 7671.28 -551.27 Coefficients 894.16 Standard Error 3327.39 8976.75 45.10 96.36 t Stat 6.999 0.855 - 12.223 9.280 P-value 0.0000 0.3965 0.0000 0.0000 O No, because it is expected that as age increases then the price should decrease. O Yes, because it is expected that as age increases then the price should also increase. O No, because it is expected that as age increases then the price should also increase. O Yes, because it is expected that as age increases then the price should decrease. Tables Key Keyboard Shor
Please see attached image. In analyzing the effects of an after-school reading program, you run a regression analysis with program participation as the independent variable (0 = control group; 1 = intervention group) and scores on a reading comprehension exam after the program as the dependent variable. Is the effect of the after-school reading program statistically significant? How can you tell, and what does this mean?
Please help me with question d, e, f, g, h, and I. Thank you.
Consider a linear regression model that relates school expenditures and family background to student performance in Massachusetts using 224 school districts. The response variable is the mean score on the MCAS (Massachusetts Comprehensive Assessment System) exam given in May 1998 to 10th-graders. Four explanatory variables are used: (1) STR is the student-to-teacher ratio, (2) TSAL is the average teacher’s salary, (3) INC is the median household income, and (4) SGL is the percentage of single family households. The Excel Regression output for the sample regression equation is given below. (a) What proportion of the variation in MCAS score is explained by the explanatory variables? (b) At the 5% level, are the explanatory variables jointly significant in explaining MCAS score? Explain briefly. (c) At the 5% level, which variables are individually significant at predicting MCAS score? Explain briefly. (d) Suppose a second regression model (Model 2) was generated using only…
The following data gives the number of employees at the bookstore and the number of minutes students wait in line to buy books at the beginning of the term. The independent variable is the number of employees and the dependent variable is the number of minutes. What is the y intercept? SSxx = 56.857; SS=2095.714; SSxy=-322.571 SUMMARY OUTPUT Regression Statistics Multiple R R Square Adjusted R Square XA356899 0.96 0.93 0.91 y 67 54 47 33 31 25 12
Write the formula for the estimated regression line and interpret the slope of the estimated regression line, the intercept of the estimated regression line- Is it meaningful?, and the estimated R2. Based on the fitted regression model, what is the predicted ATST for a child who is 7 years old? What is the correlation between AGE and ATST? Does the residual plot suggest that the fitted regression line is inappropriate for these data? Explain why or why not. Suppose that a new subject is added to the study data and that subject is 12.5 years old with an ATST of 580 minutes. If the regression model were to be refit with this additional data point, would the new slope be greater than or less than -14.041? Justify your response.
Please answer... I'm needed only 1 hours time to dd .... Thank u
Consider the following computer output from a multiple regression analysis relating the cost of car insurance to the variables: number of car accidents, driver's credit score, and safety rating of the car. Intercept Car Accidents (In last 3 years) Credit Score Safety Rating Coefficients 1186 213.48 Coefficients - 130.46 294.11 Standard Error Does the sign of the coefficient for the variable safety rating make sense? 123.87 21.89 14.26 356.37 t Stat P-value 9.575 0.0000 9.752 0.0000 -9.149 0.0000 0.825 0.4128
The local utility company surveys 12 randomly selected customers. For each survey participant, the company collects the following: annual electric bill (in dollars) and home size (in square feet). Output from a regression analysis appears below: Bill 13.45 + 4.39*Size Coefficients Estimate Std. Error (Intercept) 13.45 Size 4.39 0.54 0.2 We are 90% confident that the mean annual electric bill increases by between dollars and dollars for every additional square foot in home size. Round your answers to three decimal places and enter in increasing order.
Consider the following computer output from a multiple regression analysis relating the cost of car insurance to the variables: number of car accidents, driver's credit score, and safety rating of the car. Intercept Car Accidents (In last 3 years) Credit Score Safety Rating Answer Coefficients 933 167.94 - 102.63 -199.18 Does the sign of the coefficient for the variable credit score make sense? Coefficients Standard Error 95.65 17.99 10.89 19.98 t Stat P-value 9.754 0.0000 9.335 0.0000 -9.424 0.0000 -9.969 0.0000 O Yes, because it is expected that as the credit score increases then the cost should decrease. O No, because it is expected that as the credit score increases then the cost should decrease. O Yes, because it is expected that as the credit score increases then the cost should also increase. O No, because it is expected that as the credit score increases then the cost should also increase. Tables Keypad Keyboard Shortcuts

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