### Multiple Regression Analysis of Used Car PricesConsider the following computer output from a multiple regression analysis, which relates the price of a used car to the variables: age of car, mileage, and safety rating.#### Coefficients Table| | Coefficients | Standard Error | t Stat | P-value ||----------------|--------------|----------------|--------|---------|| **Intercept** | 15068.49 | 1951.90 | 7.720 | 0.0000 || **Age (Year)** | -1917.82 | 250.14 | -7.667 | 0.0000 || **Mileage (in Thousands)** | -224.93 | 16.99 | -13.236| 0.0000 || **Safety Rating** | 620.02 | 69.20 | 8.960 | 0.0000 |#### ExplanationThe table above shows the results of a multiple regression analysis. Each row represents a variable included in the model, while columns display key statistics:- **Coefficients**: The estimated change in the dependent variable (car price) for a one-unit change in the independent variable, holding other variables constant.- **Standard Error**: Measures the standard deviation of the coefficient estimate.- **t Stat**: Tests whether the coefficient is statistically different from zero.- **P-value**: Indicates the probability that the coefficient is different from zero due to random chance. A p-value below 0.05 typically suggests statistical significance.#### Analysis QuestionDoes the sign of the coefficient for the variable age make sense?**Answer Options:**- ○ No, because it is expected that as age increases, the price should also increase.- ○ Yes, because it is expected that as age increases, the price should decrease.- ○ No, because it is expected that as age increases, the price should decrease.- ○ Yes, because it is expected that as age increases, the price should also increase. Please select the option that you believe correctly interprets the relationship between car age and price.

Solution In the model the price of a used car is the dependent variable and this model is multiple…

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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Consider the following table: Source SS DF MS Test Statistic Regression 6 745.77 Error 788.51 Total 12359.72 16 How much of the variation in the dependent variable is explained by the regression? Round your answer to two decimal places, if necessary.
You may need to use the appropriate technology to answer this question. Following is a portion of the computer output for a regression analysis relating y = maintenance expense (dollars per month) to x = usage (hours per week) of a particular brand of computer terminal. Analysis of Variance SOURCE DF Adj SS Adj MS Regression 1 1575.76 1575.76 Error 8 349.14 43.64 Total 9 1924.90 Predictor Coef SE Coef Constant 6.1092 0.9361 X 0.8951 0.1490 Regression Equation Y = 6.1092 + 0.8951 X #1) Write the estimated regression equation. ŷ = #2) Find the value of the test statistic. (Round your answer to two decimal places.)Find the p-value. (Round your answer to three decimal places.) #3)Use the estimated regression equation to predict monthly maintenance expense (in dollars per month) for any terminal that is used 15 hoursper week. (Round your answer to the nearest cent.) $ _____per month
The following is the simple regression results based on the data from Madrigal Corporation: Dependent variable – Machine maintenance costs Independent variable – Machine hours Computed values Intercept P 3,500 Coefficient on independent variable P 3.50 Coefficient of correlation 0.856 R2 0.733 What percentage of the variation in maintenance costs is explained by the independent variable? (use two decimal places, e.g. 82.20%, 55.31% or 0.82, 0.55, these will be counted as correct answers)
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5. For the following set of data: Y 1 10 5 4 4 13 a. Find the regression equation for predicting Y from X. b. Does the regression equation account for a significant portion of the variance in the Y scores? Use a = .05 to evaluate the F-ratio %3D X27 33
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Bluereef real estate agent wants to form a relationship between the prices of houses, how many bedrooms, House size in sq ft and Lot Size in sq ft. The data pertaining to 100 houses were processed using MINITAB and the following is an extract of the output obtained: The regression equation is Price = B + ¢Bedroom + yHouse Size + ALot Size Predictor Сoef SE Coef т P Constant 37718 14177 2.66 ** Bedrooms 2306 6994 0.33 0.742 House Size 74.3 52.98 0.164 Lot Size -4.36 17.02 -0.26 0.798 S= 25023 R-Sq=56.0% R-Sq(adj)=54.6% Source DF MS F P Regression 3 76501718347 25500572782 Residual Error 96 60109046053 626135896 Total 99 • Is y significantly different from -0.5? Perform the F test at the 1% level, making sure to state the null and alternative hypotheses. Give an interpretation to the term “R-sq" and comment on its value.
Below are the results of Multiple Regression Analysis. Write down the interpretation of the following tables: R Model R Square .560a .314 Change Statistics F Change df1df2 1 .314 7.210 9 142 a. Predictors: (Constant), Horsepower, Wheelbase, Fuel efficiency, Width, Price in thousands, Fuel capacity, Length, Engine size, Curb weight b. Dependent Variable: Sales in thousands Adjusted R Square Model Summaryb .270 Std. Error of the Estimate 58.895390 R Square Change Sig. F Change .000
An econometric model is a multiple linear regression model if Question 10Select one: a. it explains the average value of y as a linear function of several explanatory variables b. it explains the sample mean of y as a function, linear in the parameters , of several x c. it explains y as a linear function of several x , the explanatory variables d. it explains the average of y as a function of several x e. none of the answers is correct
Listed below are paired data consisting of movie budget amounts and the amounts that the movies grossed. Find the regression equation, letting the budget be the predictor (x) variable. Find the best predicted amount that a movie will gross if its budget is $135 million. Use a significance level of alpha equals 0.05 . Budget left parenthesis $ right parenthesis in Millions 45 24 115 74 72 48 117 67 4 60 124 24 6 152 8 Gross left parenthesis $ right parenthesis in Millions 115 10 95 69 127 112 93 101 50 102 223 26 18 282 56 The regression equation is ŷ = __ + __x. (Round to one decimal place as needed.)

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