1. The following model is a simplified version of the multiple regression model used by Biddle andHamermesh (1990) to study the tradeoff between time spent sleeping and working and to lookat other factors affecting sleep:sleep = Bo + B1totwrk + Bzeduc + B3age + u,where sleep and totwrk (total work) are measured in minutes per week and educ and age aremeasured in years.(a) If adults trade off sleep for work, what is the sign of 31?(b) What signs do you think 32 and B3 will have?(c) Using the data in SLEEP75.csv, the estimated equation issleep = 3638.25 – 0.148totwrk – 11.13educ + 2.20age,n = 706R = 0.113.If someone works five more hours per week, by how many minutes is sleep predicted to fall?Is this a large tradeoff?(d) Discuss the sign and magnitude of the estimated coefficient on educ.(e) Would you say totwrk, educ, and age explain much of the variation in sleep? What otherfactors might affect the time spent sleeping? Are these likely to be correlated with totwrk?2. Using the same data of previous problem (SLEEP75.csv), we obtain the estimated equationsleep = 3840.83 – 0.163totwrk – 11.71educ - 8.70age +0.128age? + 87.75male,(235.11)(.018)(5.86)(11.21)(.134)(34.33)n = 706,R2 = 0.123,R = 0.117The variable sleep is total minutes per week spent sleeping at night, totwrk is total weekly minutesspent working, educ and age are measured in years, and male is a gender dummy.(a) All other factors being equal, is there evidence that men sleep more than women? Howstrong is the evidence?(b) Is there a statistically significant tradeoff between working and sleeping? What is theestimated tradeoff?(c) What other regression do you need to run to test the null hypothesis that, holding otherfactors fixed, age has no effect on sleeping?1

Hi! Thank you for the question, As per the honor code, we are allowed to answer one question at a…

1. The following model is a simplified version of the multiple regression model used by Biddle and Hamermesh (1990) to study the tradeoff between time spent sleeping and working and to look at other factors affecting sleep: sleep = Bo + B1totwrk+ B2educ + B3age + u, where sleep and totwrk (total work) are measured in minutes per week and educ and age are measured in years. (a) If adults trade off sleep for work, what is the sign of B1? (b) What signs do you think 32 and B3 will have? (c) Using the data in SLEEP75.csv, the estimated equation is sleep = 3638.25 - 0.148totwrk - 11.13educ+2.20age, n = 706 R = 0.113. If someone works five more hours per week, by how many minutes is sleep predicted to fall? Is this a large tradeoff?

1. The following model is a simplified version of the multiple regression model used by Biddle and Hamermesh (1990) to study the tradeoff between time spent sleeping and working and to look at other factors affecting sleep: sleep = Bo + B1totwrk+ B2educ + B3age + u, where sleep and totwrk (total work) are measured in minutes per week and educ and age are measured in years. (a) If adults trade off sleep for work, what is the sign of B1? (b) What signs do you think 32 and B3 will have? (c) Using the data in SLEEP75.csv, the estimated equation is sleep = 3638.25 - 0.148totwrk - 11.13educ+2.20age, n = 706 R = 0.113. If someone works five more hours per week, by how many minutes is sleep predicted to fall? Is this a large tradeoff?

MATLAB: An Introduction with Applications

6th Edition

ISBN:9781119256830

Author:Amos Gilat

Publisher:Amos Gilat

Chapter1: Starting With Matlab

Section: Chapter Questions

Problem 1P

See similar textbooks

Similar questions

3
Sometimes, the pattern of coefficients on the year dummy variables is itself of interest. For example, a demographer may be interested in the following question: After controlling for education, has the pattern of fertility among women over age 35 changed between 1972 and 1984? The following table illustrates how this question is simply answered by using multiple regression analysis with year dummy variables. The table below presents the results from a study carried out on the total number of kids born to a woman. The factors that are controlled for are years of education, age, race, region of the country where living at age 16, and living environment at age 16. Interpret the results of the estimated model provided above. and why can we not use first differences when we have independent cross sections in two years (as opposed to panel data)?
You have been assigned the task of comparing the investment performance of five different investment managers. After gathering 60 months of excess returns (i.e. returns in excess of monthly risk-free rate) on each fund as well as the monthly excess returns on the entire stock market, you perform the regression of the form (Rp - RFR)t = a + B(RM - RFR)t + &t. You have prepared the following summary of the data, with the standard errors for each of the coefficients listed in parenthesis. Regression Data (Rp - RFR) Portfolio Alpha Beta R2 Mean Sigma 0.192 1.048 XMN 94.1% 1.022% 1.193% (0.11) (0.10) -0.053 0.662 |РЕК 91.6% 0.473 0.764 (0.19) (0.09) 0.463 0.594 PVG 68.6% 0.935 0.793 (0.19) (0.07) 0.355 0.757 SZX 64.1% 0.955 1.044 (0.22) (0.08) 0.296 0.785 CAN 94.8% 0.890 0.890 (0.14) (0.12) 1. Which portfolio do you think had the highest degree of diversification over the sample period? Explain. 2. Rank these portfolios' performance according to the Sharpe, Treynor, and Jensen measures. 3.…
4. The following regression is fitted using variables identified that could be related to tuition charges ($) of a university. TUITION = a+ B ACCEPT + y MSAT + 1 VSAT Where ACCEPT = the percentage of applicants that was accepted by the university, MSAT = Median Math SAT score for the freshman class and VSAT = Median English SAT score for the freshman class. The data was processed using MNITAB and the following is an extract of the output obtained: Predictor Coef StDev T P Constant -26780 6115 -4.38 0.000 ACCEPT 116.00 37.17 * 0.003 MSAT -4.21 14.12 VSAT 70.85 15.77 -0.30 4.49 0.767 ** S = 2685 R-Sq = 69.6% Analysis of Variance R-Sq (adj) = 67.7% Source DF SS MS F P Regression 3 808139371 Residual Error 49 353193051 269379790 7208021 37.37 0.000 Total 52 1161332421 a) Write out the regression equation. b) State the dependent and independent variable(s) c) Fill in the blanks identified by ** and ****. d) Is B significant, at the 10% level of significance? e) State one limitation of using…
The age (in weeks) and the number of hours slept in a day were recorded for arandom sample of 8 infants.Age (in weeks) 5 10 15 22 26 36 42 47Hours slept ina day14.9 14.5 14.7 14.2 13.4 14.1 13.4 13.7(a) Find the regression line that predicts the number of hours an infant sleeps ina day based on its age (in weeks).(b) How well does the regression line fit the data? Use words and statistics tosupport your answer.(c) Predict the number of hours that a 30-week-old infant would sleep in a day.Use words to state your answer.
Suppose the following data were collected from a sample of 1515 CEOs relating annual salary to years of experience and the economic sector their company belongs to. Use statistical software to find the following regression equation: SALARYi=b0+b1EXPERIENCEi+b2SERVICEi+b3INDUSTRIALi+eiSALARY�=�0+�1EXPERIENCE�+�2SERVICE�+�3INDUSTRIAL�+��. Is there enough evidence to support the claim that on average, CEOs in the service sector have lower salaries than CEOs in the financial sector at the 0.010.01 level of significance? If yes, write the regression equation in the spaces provided with answers rounded to two decimal places. Else, select "There is not enough evidence." Copy Data CEO Salaries Salary Experience Service (1 if service sector, 0 otherwise) Industrial (1 if industrial sector, 0 otherwise) Financial (1 if financial sector, 0 otherwise) 144225144225 1010 11 00 00 187765187765 2020 00 00 11 142500142500 66 11 00 00 169650169650 2828 11 00 00 167250167250 3131 00…
According to an article, one may be able to predict an individual's level of support for ecology based on demographic and ideological characteristics. The multiple regression model proposed by the authors was the following. y = 3.60-.01.x₁ +.01.x2-.07x3+.12x4+.02xs-.04x6-.01x7.04x8-.02xg+e The variables are defined as follows. y = ecology score (higher values indicate a greater concern for ecology) x₁ = age times 10 x₂ = income (in thousands of dollars) x3 = gender (1 = male, 0 = female) X4 = race (1 = white, 0 = nonwhite) X5 = education (in years) x6 = ideology (4 = conservative, 3 = right of center, 2 = middle of the road, 1 = left of center, and 0 = liberal) X7 = social class (4 = upper, 3 = upper middle, 2 = middle, 1 = lower middle, 0 = lower) x8 = postmaterialist (1 if postmaterialist, 0 otherwise) x9 = materialist (1 if materialist, O otherwise) (a) Suppose you knew a person with the following characteristics: a 30 year old, white female with a college degree (20 years of…
3. Wine Participant magazine has collected average price per bottle for the prestigious Chateau Le Thundebird bordeaux for different vintages (years). The data appears in the table below. year of bottling price a) draw the scatter diagram showing how wine price varies by vintage year b) use the most appropriate regression equation to determine the relationship between year of bottling (age) and price. c) what is the explanatory power (RSQ) of that equation d) determine the predicted price of a bottle of this wine for the 2017 vintage. 2009 36 2010 40 2011 51 2012 60 2013 68 2014 72 2015 70 2016 65 2018 51 2019 44 2020 39