Question

We have developed a multiple regression model to predict the number of volunteers for a monthly...

We have developed a multiple regression model to predict the number of volunteers for a monthly charity event. One of the independent variables is Rain: a binary variable equal to 1 if it rains on the day of the event, and 0 otherwise. In the regression output, the coefficient for Rain is -22. What does that coefficient mean, precisely, with regard to the model’s predictions?

Homework Answers

Answer #1

Given that rain =1,if it rans on the day of event and 0 otherwise

coefficient is -22, negative sign shows that there will be 22 less volunteers when binary variable is set to 1

this means that "on an average,there are 22 less number of volunteers for the monthly charity event on rainy day as compared to normal day,when there is no rain"

so, coefficient tells us the numbe of volunteers for the monthly charity event based on the day type, either rain or normal

Know the answer?
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
Similar Questions
A multiple linear regression model based on a sample of 24 weeks is developed to predict...
A multiple linear regression model based on a sample of 24 weeks is developed to predict standby hours based on the total staff present and remote hours. The SSR is 31,193.47 and the SSE is 33,923.99. complete parts a through d. a. Determine whether there is a significant relationship between standby hours and the two independent variables at the .05 level of significance. test statistic pvalue state the conclusion b. Interpret the meaning of the p-value. c. compute the coefficent...
A multiple linear regression model based on a sample of 17 weeks is developed to predict...
A multiple linear regression model based on a sample of 17 weeks is developed to predict standby hours based on the total staff present and remote hours. The SSR is 20,905.02 and the SSE is 25,434.29. (use 0.05 level of significance) H0​: B1 = B2 = 0 H1​: At least one Bj does not equal 0, j = 1, 2 1. Calculate the test statistic. Fstat= _____ 2. Find the p-value. p-value= _____ 3. Compute the coefficient of multiple​ determination,...
1.A real estate analyst has developed a multiple regression line, y = 60 + 0.068 x1...
1.A real estate analyst has developed a multiple regression line, y = 60 + 0.068 x1 – 2.5 x2, to predict y = the market price of a home (in $1,000s), using two independent variables, x1 = the total number of square feet of living space, and x2 = the age of the house in years. With this regression model, the predicted price of a 10-year old home with 2,500 square feet of living area is __________. $205.00 $255,000.00 $200,000.00...
An investigator developed a multiple regression model for employee salaries at a particular company. In this...
An investigator developed a multiple regression model for employee salaries at a particular company. In this multiple regression model, the salaries are in thousands of dollars. For example, a data entry of 35 for the dependent variable indicates a salary of $35,000. The indicator variable for gender is coded as if male and if female. The computer output of this multiple regression model shows that the coefficient for this variable is -4.2. The t test showed that was significant at...
The owner of a movie theater company used multiple regression analysis to predict gross revenue (y)...
The owner of a movie theater company used multiple regression analysis to predict gross revenue (y) as a function of television advertising (x1) and newspaper advertising (x2). The estimated regression equation was ŷ = 82.5 + 2.26x1 + 1.30x2. The computer solution, based on a sample of eight weeks, provided SST = 25.3 and SSR = 23.415. (a)Compute and interpret R2 and Ra2. (Round your answers to three decimal places.) The proportion of the variability in the dependent variable that...
The owner of a movie theater company used multiple regression analysis to predict gross revenue (y)...
The owner of a movie theater company used multiple regression analysis to predict gross revenue (y) as a function of television advertising (x1) and newspaper advertising (x2).The estimated regression equation was ŷ = 83.1 + 2.23x1 + 1.30x2. The computer solution, based on a sample of eight weeks, provided SST = 25.4 and SSR = 23.395. (a) Compute and interpret R2 and Ra2. (Round your answers to three decimal places.) The proportion of the variability in the dependent variable that...
A microcomputer manufacturer has developed a regression model relating his sales (Y in $10,000s) with three...
A microcomputer manufacturer has developed a regression model relating his sales (Y in $10,000s) with three independent variables. The three independent variables are price per unit (Price in $100s), advertising (ADV in $1,000s) and the number of product lines (Lines). Part of the regression results is shown below. Coefficient Standard Error Intercept 1.0211 22.8752 Price (X1) -.1523 -.1411 ADV (X2) .8849 .2886 Lines(X3) -.1463 1.5340 Source D.F. S.S. Regression 3 2708.651 Error 14 2840.51 Total 17 5549.12 (a) What has...
The owner of a movie theater company used multiple regression analysis to predict gross revenue (y)...
The owner of a movie theater company used multiple regression analysis to predict gross revenue (y) as a function of television advertising (x1)  and newspaper advertising (x2).  The estimated regression equation was ŷ = 83.8 + 2.26x1 + 1.50x2. The computer solution, based on a sample of eight weeks, provided SST = 25.8 and SSR = 23.385. (a) Compute and interpret  R2 and Ra2. (Round your answers to three decimal places.) The proportion of the variability in the dependent variable that can be...
A regression analysis was performed and the summary output is shown below. Regression Statistics Multiple R...
A regression analysis was performed and the summary output is shown below. Regression Statistics Multiple R 0.7149844700.714984470 R Square 0.5112027920.511202792 Adjusted R Square 0.4904029110.490402911 Standard Error 8.2079903998.207990399 Observations 5050 ANOVA dfdf SSSS MSMS FF Significance FF Regression 22 3311.5863311.586 1655.7931655.793 24.577224.5772 4.9491E-084.9491E-08 Residual 4747 3166.4423166.442 67.37167.371 Total 4949 6478.0286478.028 Step 1 of 2: How many independent variables are included in the regression model? Step 2 of 2: Which measure is appropriate for determining the proportion of variation in the dependent...
3) Which of the following statements is TRUE about multiple regression? I. We do not want...
3) Which of the following statements is TRUE about multiple regression? I. We do not want our explanatory variables to be interrelated. II. At least one slope should be statistically significant. III. The slopes should not change if one variable is removed. A) I only B) II only C) I and II only D) I and III only 4) A regression model is built using the heights and body fat percentages to predict the weights of 30 volunteers. How many...
ADVERTISEMENT
Need Online Homework Help?

Get Answers For Free
Most questions answered within 1 hours.

Ask a Question
ADVERTISEMENT