please help with part IV and V 3. The following model is a simplified version of the multiple regression model used by BESTEconometrics Group to study the trade off between time spent sleeping and working to look at otherfactors affecting sleep:sleep %3D Bo + Вitotwrk + Bzeduc + Bзадe + M,where sleep and totwork (total work) are measure in minutes per week and educ and age are measuredin years.(i) If adults trade sleep for work, what is the sign of B,?(ii) What signs do you think B2 and B3 wilI have?(iii) Using the data in SLEEP75.RAW, the estimated equation issleep— 3, 638.25—.148totwrk — 11.13educ — 2.20аge, п %3706, R? =. 113-If someone works five hours per week, by how manu minutes is sleep predicte d to fall? Is thisa large tradeoff?(iv) Discuss the sign and magnitude of the estimated coefficient on educ.(v) Would you say totwrk, educ, and age explain much of the variation in sleep? What other factorsmight affect the time spent sleeping? Are these likely to be correlated with totwrk?

* ANSWER :- From the given information the answer is provided as below

3. The following model is a simplified version of the multiple regression model used by BEST Econometrics Group to study the trade off between time spent sleeping and working to look at other factors affecting sleep: sleep %3D Bo + Вitotwrk + Bzeduc + Bзадe + Ma where sleep and totwork (total work) are measure in minutes per week and educ and age are measured in years. (i) If adults trade sleep for work, what is the sign of B1? (ii) What signs do you think B2 and ß3 will have? (iii) Using the data in SLEEP75.RAW, the estimated equation is sleep — 3, 638.25—.148totwrk — 11.13educ — 2.20аge, п %3 706, R? =. 113 If someone works five hours per week, by how manu minutes is sleep predicte d to fall? Is this a large tradeoff? (iv) Discuss the sign and magnitude of the estimated coefficient on educ. (v) Would you say totwrk, educ, and age explain much of the variation in sleep? What other factors might affect the time spent sleeping? Are these likely to be correlated with totwrk?

3. The following model is a simplified version of the multiple regression model used by BEST Econometrics Group to study the trade off between time spent sleeping and working to look at other factors affecting sleep: sleep %3D Bo + Вitotwrk + Bzeduc + Bзадe + Ma where sleep and totwork (total work) are measure in minutes per week and educ and age are measured in years. (i) If adults trade sleep for work, what is the sign of B1? (ii) What signs do you think B2 and ß3 will have? (iii) Using the data in SLEEP75.RAW, the estimated equation is sleep — 3, 638.25—.148totwrk — 11.13educ — 2.20аge, п %3 706, R? =. 113 If someone works five hours per week, by how manu minutes is sleep predicte d to fall? Is this a large tradeoff? (iv) Discuss the sign and magnitude of the estimated coefficient on educ. (v) Would you say totwrk, educ, and age explain much of the variation in sleep? What other factors might affect the time spent sleeping? Are these likely to be correlated with totwrk?

Algebra and Trigonometry (MindTap Course List)

4th Edition

ISBN:9781305071742

Author:James Stewart, Lothar Redlin, Saleem Watson

Publisher:James Stewart, Lothar Redlin, Saleem Watson

Chapter1: Equations And Graphs

Section1.FOM: Focus On Modeling: Fitting Lines To Data

Problem 10P

See similar textbooks

Similar questions

If your graphing calculator is capable of computing a least-squares sinusoidal regression model, use it to find a second model for the data. Graph this new equation along with your first model. How do they compare?
Find the equation of the regression line for the following data set. x 1 2 3 y 0 3 4
Show that an interaction term of a dummy variable and a regressor changes the slope of a regression line..
when a regression is used as a method of predicting dependent variables from one or more independent variables. How are the independent variables different from each other yet related to the dependent variable?
Online clothes II For the online clothing retailer dis-cussed in the previous problem, the scatterplot of Total Yearly Purchases by Income showsThe correlation between Total Yearly Purchases and Incomeis 0.722. Summary statistics for the two variables are: a) What is the linear regression equation for predictingTotal Yearly Purchase from Income? b) Do the assumptions and conditions for regression ap-pear to be met? c) What is the predicted average Total Yearly Purchasefor someone with a yearly Income of $20,000? Forsomeone with an annual Income of $80,000?d) What percent of the variability in Total YearlyPurchases is accounted for by this model?e) Do you think the regression might be a useful one forthe company? Comment.
XYZ company is interested in quantifying the impact of consumer promotions on the sales of its packaged food product. XYZ has historical data on the following variables for 38 weeks:• Sales: Weekly sales volume in thousands of units.• Prom: Weekly spending on consumer promotions in thousands of Dollars""A regression analysis was applied to XYZ historical dataset. The dependent variable is weekly Sales and the independent variables are weekly Prom and weekly Lagged Prom (i.e., last week Prom). This is a summary of the regression output:Sales = 0.80 + 1.20*Prom - 0.40*Lag(Prom)• R-squared=0.85• F-Statistic=23.83• p-value=0.001 (for the overall regression)•All regression coefficients are statistically significant at the 5% level."A. What is the percent of variance in sales that is explained by Prom and Lag(Prom)? B. Is the overall regression equation statistically significant at the 5% level? Give reasons for the same? C. What is the net volume impact of an incremental $1000 spending per…
XYZ company is interested in quantifying the impact of consumer promotions on the sales of its packaged food product. XYZ has historical data on the following variables for 38 weeks: • Sales: Weekly sales volume in thousands of units.• Prom: Weekly spending on consumer promotions in thousands of Dollars" "A regression analysis was applied to XYZ historical dataset. The dependent variable is weekly Sales and the independent variables are weekly Prom and weekly Lagged Prom (i.e., last week Prom). This is a summary of the regression output:Sales = 0.80 + 1.20*Prom - 0.40*Lag(Prom) • R-squared=0.85• F-Statistic=23.83• p-value=0.001 (for the overall regression)•All regression coefficients are statistically significant at the 5% level." A. What is the percent of variance in sales that is explained by Prom and Lag(Prom)?B. Is the overall regression equation statistically significant at the 5% level? Give reasons for the same? C. What is the net volume impact of an incremental $1000 spending…
7. Battery life has a strong, negative, linear relationship with temperature. If the least-squares regression line using xx = temperature explains 90% of the variation in battery life, which of the following must be the correlation, rr, between battery life and temperature? (A) -0.90 (B) 0.90 (C) -0.95 (D) 0.95 (E) Cannot be determined without the original data. Submit Answer
The administration of a midwestern university commissioned a salary equity study to help establish benchmarks for faculty salaries. The administration utilized the following regression model for annual salary, y : ?(?) β0+β1x ,where ?=0 if lecturer, 1 if assistant professor, 2 if associate professor, and 3 if full professor. The administration wanted to use the model to compare the mean salaries of professors in the different ranks. a) Explain the flaw in the model. b)Propose an alternative model that will achieve the administration’s objective. c) If the global F-test for the model you proposed in 2 is conducted, what would be the value of the numerator degrees of freedom?
The marketing manager wants to test if the effect of the MBA program on salary depends on the work experience while controlling for the other factors. Which regression model is the MOST appropriate? O a. Salary = B_0+B_1 MBA+B_2 Work +B_3 Age +3_4 Gender + 8 Ob. Salary = B_0+ B_1 MBAx Work + B_2 Age + B 3 Gender & Oc. Salary = B_0+ B_1 MBA + B_2 MBA x Work + B_3 Age + B_4 Gender + e Od. Salary = B_0+ B_1 MBA+B_2 Work + B_3 MBA x Work + 3_4 Age + B_5 Gender + &
A study was conducted on 64 female college athletes. The researcher collected data on a number of variables including percent body fat, total body weight, height, and age of athlete. The researcher wondered if % body fat (%BF), height (HGT), and/or age are significant predictors of total body weight. All conditions have been checked and are met and no transformations were needed. The technology output from the multiple regression analysis is given below. What percent of the variation in total body weight is being explained by the regression model with these three explanatory variables?
Suppose you are estimating a wage regression, where salary is the dependent variable and age, years of education and a dummy variable for male are your independent variables. You are interested in measuring how salary differs between those who have at least a college education with those who have less than a college education. If a person is considered as having a college education when she has more than 12 years of education, how can you measure the difference in salary between college and non-college educated individuals? Select one: a. Multiply coefficient for years of education in original regression by 12 O b. Re-estimate model replacing years of education with a dummy variable for college c. Re-estimate model replacing years of education with a dummy variable for college and one for no college O d. Re-estimate model interacting years of education with a dummy variable for college e. Calculate the difference in predicted salary between an individual with 14 years of education and…