h) ii). Consider the results in Table 5. After running the regression, we test whether the coefficients of the variables hhsize1 and hhsize2 are equal at 5% significance level. The value of the F-statistic is 26.32. Note that the sample size is 1,593. What is the outcome of the test? i). Do you think the regression in Table 5 suffers from omitted variable bias? If yes, which additional variables would you include to control for omitted variable bias? Table 5. regress size hhinc ownerhhsizel hhsize2 hhsize3, robust noconstantLinear regressionNumber of obs1,593F(5, 1588)3895.70Prob > F0.0000R-squared0.9255%3DRoot MSE353.16RobustsizeCoefficientstd. err.P>|t|[95% conf. interval]hhinc.004567.00067176.800.000.0032495.0058845owner404.7925442.6217hhsize1675.808624.2422227.880.000628.2585723.3587hhsize2788.660636.4762821.620.000717.1138860.2073hhsize3993.660958.6329416.950.000878.65481108.667

Answered: Image /qna-images/answer/2362ef74-9cde-45e8-90ba-0c5fb782eb04.jpg

Consider the results in Table 5. After running the regression, we test whether the coefficients of the variables hhsize1 and hhsize2 are equal at 5% significance level. The value of the F-statistic is 26.32. Note that the sample size is 1,593. What is the outcome of the test? i). Do you think the regression in Table 5 suffers from omitted variable bias? If yes, which additional variables would you include to control for omitted variable bias

Consider the results in Table 5. After running the regression, we test whether the coefficients of the variables hhsize1 and hhsize2 are equal at 5% significance level. The value of the F-statistic is 26.32. Note that the sample size is 1,593. What is the outcome of the test? i). Do you think the regression in Table 5 suffers from omitted variable bias? If yes, which additional variables would you include to control for omitted variable bias

College Algebra

1st Edition

ISBN:9781938168383

Author:Jay Abramson

Publisher:Jay Abramson

Chapter4: Linear Functions

Section: Chapter Questions

Problem 8PT: Does Table 1 represent a linear function? If so, finda linear equation that models the data.

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?
Does Table 1 represent a linear function? If so, finda linear equation that models the data.
Water is being poured into a large cone shape cistern. The volume of water measured and centimeters cubed is reported at different time intervals measured in seconds. A regression analysis was completed and is displayed in the computer output. what is the equation of the lease squared regression line?
Find the equation of the regression line for the data based on time spend forstudying and current CGPA
Find the least-squares regression line treating square footage as the explanatory variable. y = (Round the slope to three decimal places as needed. Round the intercept to one decimal place as needed.)
https://1drv.ms/x/s!Av-KKmo42J4EggRqEfmUbMJgjKt3 The file Assignment 2 data.xlsx contains hypothetical starting salaries for MBA students directly after graduation. The file also lists their years of experience prior to the MBA program and their class rank in the MBA program (on a 0-100 scale- this not a categorical Variable) Estimate the regression equation with Salary as the dependent variable and Experience and Class Rank as the explanatory (independent) variables. What does this equation imply? What does the standard error of estimate tell you? What about ? Repeat part a, but now include the interaction term Experience*ClassRank in the equation as well as Experience and Class Rank individually. Answer the same questions as in part a. What evidence in there that this extra variable (the interaction variable) is worth including? Why might you expect the interaction to be present in real data of this type? Please submit the excel file of solution including results…
The following table gives the data for the grades on the midterm exam and the grades on the final exam. Determine the equation of the regression line, yˆ=b0+b1x�^=�0+�1�. Round the slope and y-intercept to the nearest thousandth. Grades on Midterm and Final Exams Grades on Midterm 7171 6262 7878 9494 8383 8181 8080 9494 8585 6262 Grades on Final 8888 7979 8888 9191 8080 7070 7171 9393 6565 7777
The data below is for a hypothetical study investigating the Systolic Blood Pressure (SBP) of construction workers. The table shows respective measurements of 20 workers along with hours of work per day and the area of the city the work took place. [You can use Excel-Data Analysis – Regression or http://vassarstats.net/multU.html] SBP Hrs/day Area 109.6 7 east 107.4 8 east 140.3 9 east 146.5 12 east 98.2 6 east 137.8 9 east 124.1 10 east 113.2 8 east 127.8 9 east 125.3 8 east 108.5 6 west 181.3 13 west 137.4 10 west 146.2 10 west 142.4 9 west 123.7 8 west 129.6 8 west 143.6 9 west 160.7 11 west 148.3 9 west a) What is the regression equation? b) Interpret the meaning of the slopes in this problem. d) At the 0.05 level of significance, determine whether each independent variable makes a contribution to the…
A researcher interested in explaining the level of foreign reserves for the country of Barbados estimated the following multiple regression model using yearly data spanning the period 2001 to 2016: FR=a+BOIL+yEXP+8FDI Where FR = yearly foreign reserves ($000's), OIL = annual oil prices, EXP = yearly total exports ($000's) and FDI = annual foreign direct investment ($000's). The sample of data was processed using MINITAB and the following is an extract of the output obtained: Predictor Сoef StDev t-ratio p-value Constant 5491.38 2508.81 2.1888 0.0491 OIL 85.39 18.46 4.626 0.0006 EXP -377.08 112.19 0.0057 FDI -396.99 160.66 -2.471 ** S = 2.45 R-sq 96.3% R-sq (adj) 95.3% Analysis of Variance Source DF MS F Regression 1991.31 663.77 ?? Error 12 77.4 6.45 Total 15 a) What is dependent and independent variables? b) Fully write out the regression equation c) Fill in the missing values *', ***', '?'and ??' d) Hence test whether B is significant. Give reasons for your answer. e) Perform the F…
Company Legal Name ASX Code ORIGIN ENERGY LTD ORG ORIGIN ENERGY LTD ORG ORG ORG ORG ORIGIN ENERGY LTD ORIGIN ENERGY LTD ORIGIN ENERGY LTD ORIGIN ENERGY LTD ORG ORIGIN ENERGY LTD ORG ORIGIN ENERGY LTD ORG ORIGIN ENERGY LTD ORG ORIGIN ENERGY LTD ORIGIN ENERGY LTD ORIGIN ENERGY LTD ORG ORIGIN ENERGY LTD ORG ORIGIN ENERGY LTD ORG ORIGIN ENERGY LTD ORG ORIGIN ENERGY LTD ORG ORIGIN ENERGY LTD ORG ORIGIN ENERGY LTD ORG ORIGIN ENERGY LTD ORG ORIGIN ENERGY LTD ORG ORG QEarnings 1 Date 2 2019/4/1 3 2019/4/2 4 2019/4/3 5 2019/4/4 6 2019/4/5 7 2019/4/8 8 2019/4/9 9 2019/4/10 10 2019/4/11 11 2019/4/12 12 2019/4/15 13 2019/4/16 14 2019/4/17 15 2019/4/18 16 2019/4/23 17 2019/4/24 18 2019/4/26 19 2019/4/29 20 2019/4/30 21 2019/5/1 22 2019/5/2 2019/5/3 23 24 2019/5/6 25 2019/5/7 26 2019/5/8 27 2019/5/9 28 2019/5/10 29 2019/5/13 30 2019/5/14 31 2019/5/15 32 2019/5/16 33 2019/5/17 34 2019/5/20 2019/5/21 35 36 2019/5/22 37 2019/5/23 38 2019/5/24 39 40 2019/5/27 2019/5/28 41 2019/5/29 42 2019/5/30 1 Date…
Show the best fitted line on scatter diagram and Find the predicted value for each y using the exposure time and the equation obtained in part b (b. Find the equation of regression line between radiation doses on exposure time .usingleast square method)
Use the Stata output below. The data comes from students in an undergraduate economics course. The regression of interest is: final = 30 +3₁atndrte + B₂hwrte +33priGPA + B ACT .reg final atndrte hwrte priGPA ACT Source Model 3094.70776 Residual 11929.9465 Total final . reg atndrte hwrte priGPA ACT _cons Total .reg final priGPA ACT final priGPA ACT _cons Source Model 2950.00785 Residual 12074.6465 Source ss 15024.6543 Total atndrte hwrte priGPA ACT _cons Coef. Std. Err. SS atndrte hwrte priGPA ACT .0138725 .0183476 .010863 1.906347 -3750236 .3990516 .0535332 9.225908 1.46863 15024.6543 Calculate the se(3₁). Model 89295.5136 Residual 93922.8539 df 4 669 SS 183218.367 673 22.324895 Coef. Std. Err. df 2.390227 .3233916 .3534674 .0505019 11.74854 1.147688 673 MS 773.676939 17.832506 2 1475.00393 671 17.9950022 df MS 22.324895 t P>|t| [95% Conf. Interval] MS Number of obs F(4, 669) Prob > F R-squared 3 29765.1712 670 140.183364 Adj R-squared Root MSE 673 272.241259 Number of obs F(2, 671)…