Question

Interpret the following regression model:             Repairs = 0.33 + 0.312 New Cars + 0.71 Used...

Interpret the following regression model:

            Repairs = 0.33 + 0.312 New Cars + 0.71 Used Cars

                            (1.9)     (1.09)                    (3.1)

                  R2 = 38.4     Adj. R2 = 34.3   Durbin-Watson = 2.0   F(p-value) = 0.03

0 0
Add a comment Improve this question Transcribed image text
Answer #1

The above regression equation is a multi variate regression equation depicting how new and used cars has an impact on the repairs of cars. Thus, independent variable in the above equation are new cars and used cars and the dependent variable in the above equation is repairs done.

There exists a positive relationship between used cars and number of repairs. As the number of new cars increases by 1 unit, the number of repairs increases by 0.312 unit. There also exists a positive relationship between number of used cars and number of repairs. Thus, as the number of used cars increases by 1 unit, number of repairs in the car industry increases by 0.71 units.

Also, the value of r squared of 38.4 shows that around 38 per cent variation in the number of repairs can be explained by variations in the number of new cars and variations in the used cars.

Add a comment
Know the answer?
Add Answer to:
Interpret the following regression model:             Repairs = 0.33 + 0.312 New Cars + 0.71 Used...
Your Answer:

Post as a guest

Your Name:

What's your source?

Earn Coins

Coins can be redeemed for fabulous gifts.

Not the answer you're looking for? Ask your own homework help question. Our experts will answer your question WITHIN MINUTES for Free.
Similar Homework Help Questions
  • Interpret the following regression model: Repairs = 0.33 + 0.312 New Cars + 0.71 Used Cars...

    Interpret the following regression model: Repairs = 0.33 + 0.312 New Cars + 0.71 Used Cars (1.9) (1.09) (3.1)                   R2 = 38.4 Adj. R2 = 34.3 Durbin-Watson = -3.2 F(p-value) = 0.03

  • Build the regression model based on the outputs presented in the following tables Interpret the results of the regression analysis presented in the following tables Should the constant term be in...

    Build the regression model based on the outputs presented in the following tables Interpret the results of the regression analysis presented in the following tables Should the constant term be included in the regression model? R adjusted R Std. Error of the estimate del R Change in statistics R change Fchange Sig. F Durbin- change Watson 2.550 1 .675 455 331 61.3419 .519 99.587 Model Unstandardized Standardized Sig 95% Confidence Interval for B Lower bound bound Coefficients Coefficients Beta Upper...

  • In a simple linear regression model, the intercept of the regression line measures

    QUESTION 1In a simple linear regression model, the intercept of the regression line measuresa.the change in Y per unit change in X.b.the change in X per unit change in Y.c.the expected change in Y per unit change in X.d.the expected change in X per unit change in Y.e.the value of Y when X equals 0.f.the value of X when Y equals 0.g.the average value of Y when X equals 0.h.the average value of X when Y equals 0.QUESTION 2In a...

  • Question 4 (3 points) The statsmodels ols() method is used on a cars dataset to fit...

    Question 4 (3 points) The statsmodels ols() method is used on a cars dataset to fit a multiple regression model using Quality as the response variable. Speed and Angle are used as predictor variables. The general form of this model is: Y = Bo + B. Speed+B Angle If the level of significance, alpha, is 0.10, based on the output shown, is Angle statistically significant in the multiple regression model shown above? Select one. OLS Regression Results ==================================== ========== 0.978...

  • The following output resulted from a regression model where SAGap is seasonally adjusted Gap sales and...

    The following output resulted from a regression model where SAGap is seasonally adjusted Gap sales and dpi is disposable income per capita. Audit Trail -- Coefficient Table (Mulitple Regression Selected) Series Description Included in Model Coefficient Standard Error T-test P-value F-test Elasticity SAGAP Dependent - 2,867,564.78 140,536.33 - 20.40 0.00 416.34 dpi Yes 809.79 25.04 32.33 0.00 1,045.55 2.91 Audit Trail -- Correlation Coefficient Table Series Description Included in Model SAGap dpi SAGap Dependent 1.00 0.97 dpi Yes 0.97 1.00...

  • Classified ads in the local paper offered several used cars of a particular model for sale....

    Classified ads in the local paper offered several used cars of a particular model for sale. The used car ages are measured in years and the advertised selling prices in dollars. Simple linear regression results: Dependent Variable: ad price Independent Variable: age Price_Advertised_($) = 12288.813 - 921.27273 Age_(yr) Sample size: **** R (correlation coefficient) = ****** R-sq = 0.89346768 Estimate of error standard deviation: 1218.3026 Parameter estimates: Parameter Estimate Std. Err. Alternative DF T-Stat P-value Intercept 12288.813 574.62041 ≠ 0...

  • just anw the c part thx Question 1 (100 Marks) The following table is the regression...

    just anw the c part thx Question 1 (100 Marks) The following table is the regression results from the econometric model: LOG(SALES) = B. + B2LOG (PRICE) + BzADVERT + e For a sample of 66 observations. SALES: Monthly Sales of product A ($1000) PRICE: A price Index of product A (SI) ADVERT: Adverting Expenditure on product A (S1000) Dependent Variable: LOGSALES Method: Least Squares Date:03/19/20 Time: 20:04 Included observations: 66 Variable Coefficient Std. Error -Statistic Prob. LOGPRICE ADVERT 5.325153...

  • Domestic Car Sales Consider the following multiple regression model of domestic car sales (DCS) where: DCS...

    Domestic Car Sales Consider the following multiple regression model of domestic car sales (DCS) where: DCS = domestic car sales DCSP = domestic car sales price (in dollars) PR = prime rate as a percent (i.e., 10% would be entered as 10) Q2 = quarter 2 dummy variable Q3 = quarter 3 dummy variable Q4 = quarter 4 dummy variable Multiple Regression — Result Formula DCS = 3,266.66 + ((DCSP) × −0.098297) + ((PR) × −21.17) + ((Q2) × 292.88)...

  • Statistical software was used to fit the model E(y)Pox1 2x2 to n 20 data points. Complete parts a...

    Section 12.3 Multiple Linear Regression: Number ONE: Statistical software was used to fit the model E(y)Pox1 2x2 to n 20 data points. Complete parts a through h EEB Click the icon to see the software output. Data Table The regression equation is Y-1738.93 - 384.54x1 517.39x2 Predictor Constant X1 X2 Coef 1738.93 - 384.54 -517.39 SE Coef 369.06 101.65 - 3.78 0.002 353.04 - 1.47 0.162 4.71 0.000 s-172.003 R-sq-55.0% R-sq(adj):49.0% Analysis of Variance MS Source Regression Residual Error 17...

  • The following multiple regression printout can be used to predict a person's height (in inches) given...

    The following multiple regression printout can be used to predict a person's height (in inches) given his or her shoe size and gender, where gender = 1 for males and 0 for females. Regression Analysis: Height Versus Shoe Size, Gender Coefficients Term Coef SE Coef T-Value P-Value Constant 55.26 1.04 53.13 0.000 Shoe Size 1.166 0.15 0.000 Gender 2.574 0.486 5.30 0.000 Find the value of the test statistic for shoe size. (Round your answer to two decimal places.) t...

ADVERTISEMENT
Free Homework Help App
Download From Google Play
Scan Your Homework
to Get Instant Free Answers
Need Online Homework Help?
Ask a Question
Get Answers For Free
Most questions answered within 3 hours.
ADVERTISEMENT
ADVERTISEMENT
ADVERTISEMENT