Parameter estimatesDirect effectsIndirect effectsFreedomFreedomFreedomHappiness ScoreGenerosityHappiness ScoreFamilyHappiness ScoreHappiness ScoreLife ExpectancyNote. Delta method standard errors, normal theory confidence intervals, ML estimator.GenerosityGenerosityFamilyFamilyTotal effectsEstimate Std. ErrorHappiness ScoreHappiness ScoreHappiness ScoreGDP per CapitaGovernment CorruptionGDP per CapitaGovernment CorruptionGDP per CapitaGovernment CorruptionGDP per CapitaGovernment Corruption.Happiness ScoreHappiness ScoreHappiness ScoreLife ExpectancyHappiness ScoreHappiness ScoreLife ExpectancyNote. Delta method standard errors, normal theory confidence intervals, ML estimator.FreedomGenerosity1.4760.3811.1181.289Happiness ScoreHappiness Score0.3350.3220.1970.314z-valueEstimateEstimate Std. Error z-value1.8280.2531.4022.2780.352-0.1280.2840.989p4.409<.0011.1830.2375.661 <.0014.102<.0010.3310.3320.1940.227FreedomGenerosityFamilyHappiness ScoreHappiness ScoreLife ExpectancyNote. Delta method standard errors, normal theory confidence intervals, ML estimator.Total indirect effectsEstimate0.1190.233-0.2250.0970.2980.1760.150-0.0140.9350.0545.519<.0010.761 0.4467.208 <.00110.035 <.0010.1050.246pStd. Error z-valueHappiness ScoreHappiness ScoreFamilyHappiness ScoreHappiness ScoreLife ExpectancyNote. Delta method standard errors, normal theory confidence intervals, ML estimator.95% Confidence IntervalUpperLower0.820-0.2500.7310.673Std. Error0.1050.1410.116Р0.2480.0441.1291.656-1.9410.0711.3580.0963.0970.027 -0.4992.1321.0111.5051.905Lowerz-value95% Confidence IntervalUpper1.179-0.3981.0211.833Р0.2590.0980.0520.1740.0020.6183.770 <.0011.2420.2142.4770.9031.7832.72395% Confidence IntervalUpperLower1.9950.0460.006-0.8570.392 -0.4222.6990.0074.014 <.0010.0780.5060.6980.1650.4911.47295% Confidence IntervalUpperLower-0.087-0.043-0.452-0.0430.109-0.0670.449-0.0310.3240.5090.0020.2370.4860.0401.4210.140 To further elaborate the regression model, the researchers of the research projectidentified that the factors can be categorized as psychosocial and political-economicfactors. The Psychosocial factors were the freedom, generosity, family and lifeexpectancy while the political-economic factors were GDP per Capita and GovernmentCorruption. To make sense of the factor distinctions, the researcher placed thepsychosocial factors as predictors while the political-economic factors as mediators,thereby, implementing a mediation analysis.A. Which among the political-economic factors can be considered significantmediators between psychosocial factors and happiness? Provide parameterestimates and p-values.B. Using the Total-Indirect Effects table, which among the psychosocial factors(predictors) have significant indirect effects towards happiness? Provide parameterestimates and p-values.C. Describe why the mediators significantly mediate the relationships in 6A. Provide atleast 2 research literature or articles per mediator.D. Based on your answer to 6B, why is the effect of the psychosocial factors of "indirectnature" towards happiness. Provide at least 2 research literature or articles per eachpredictor.

Please note that only sub parts A and B are solved because sub parts C and D require external…

Answered: To further elaborate the regression…

MATLAB: An Introduction with Applications

6th Edition

ISBN:9781119256830

Author:Amos Gilat

Publisher:Amos Gilat

Chapter1: Starting With Matlab

Section: Chapter Questions

Problem 1P

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2. A study is conducted in patients with HIV. The primary outcome is CD4 cell count which is a measure of the stage of the disease. Lower CD4 counts are associated with more advanced disease. The investigators are interested in the association between vitamin and mineral supplements and CD4 count. A multiple regression analysis is performed relating CD4 count to use of supplements(coded as l ves, 0-no) and to duration of HIV, in years(ie., the number of years between the diagnosis of HIV and the study date). For the analysis, Y-CD4 count. Y 501.41 12.67 Supplements - 30.23 Duration of HIV A. What is the expected CD4 count for a patient taking supplements who has had HIV for 2.5 years? B. What is the expected CD4 count for a patient not taking supplements who was diagnosed with HIV at study enrollment? C. What is the expected CD4 count for a patient not taking supplements who has had HIV for 2.5 years
Consider a panel data set and the following regression model. Yit Bo + B₁Xit + ++ Uit What do subscripts / and t refer to? O A. Subscripts i and tidentify different time periods. O B. Subscripts / and t identify the entity and time period respectively. OC. Subscripts i and t identify different individuals. O D. Subscripts i and t identify the time period and entity respectively.
ABC Company is exploring different prediction models that can be used to forecast indirect labor costs. One independent variable under consideration is machine hours. Following are matching observations on indirect labor costs and machine hours for the past six months: Machine hours Indirect labor costs P20,000 P24,000 P17,000 P22,000 P13,000 P14,000 Month 1 300 400 240 4 370 5 200 6 225 Using linear regression analysis, the estimated variable indirect labor cost per machine hour is closest to: P35.62 P15.44 P52.71 P58.31
12 young batsmen practiced batting at the nets for varying periods of time, and their dot ball percentage was calculated at the end of the month: a) Find the relationship between dot ball percentage and practice time per month using a scatter diagram and interpret. b) Find correlation coefficient and comment. c) Fit a least square regression equation (line) of dot ball percentage on practice time per month and comment. d) What will be the dot ball percentage when practice time per month is 32hr? e) Comment on the regression equation and explore how well it fits.
The following is the data for Patient Days (the cost driver) and Maintenance Costs for a local hospital: Month Patient Days (Cost driver) Maintenance Costs January 3,300 $4,450 February 4,050 $4,750 March 3,000 $4,200 April 3, 750 $ 4,600 May 4,150 $5,050 June 4,500 $5,400 July 2,600 $4,400 Using regression analysis to analyze the hospital's cost structure, the estimated fixed maintenance cost for the hospital is: $5,468 $0.793 $2,734 $0.541
Training Dept. of Nimrod Inc wants to develop a regression-based compensation model (compensation in $ per year, Comp) for its mid-level managers to encourage performance, loyalty, and continuing education based on three variables. ▪ Business unit-profitability (Profit per year in $). ▪ Working experiences in Nimrod Inc (Years). ▪ Whether or not a manager has a graduate degree (Grads). If a manager has a graduate degree equals 1, 0 otherwise. Table Attached Question: Use the (full) model to determine the compensation for a manager who has been working for twelve years in a company, no graduate degree, and Nimrod Inc profit of $8.000.000 last year.
In reading the results of a multiple regression analysis that contained 4 predictor variables, the researcher noticed a column labeled Beta. Two of the Beta’s were positive and two were negative. He concluded that a.) Beta’s that were positive were statistically significant b.) Beta’s that were positive had more of an effect c.) Beta’s that were positive were associated with increases in the criterion variable d.) Beta’s that were positive did not affect the criterion because they were “controlled for…”
a. Develop the least squares estimated regression equation that relates labor hours to house square footage and type of flooring. b. Use the regression equation developed in part (a) to predict labor hours when the house size is 3350 square feet and the type of flooring is wood.
3. Setup: The data set "Healthy Breakfast" contains, among other variables, the Consumer Reports ratings of 77 cereals and the number of grams of sugar contained in each serving. (Data source: Free publication available in many grocery stores. Data set available through the Statlib Data and Story.) Considering "Sugars" as the explanatory variable and "Rating" as the response variable generated the following regression line: Rating = 59.3-2.40 - Sugars. and the ANOVA table Source Regression Error Total SS df MS 8654.7 1 8654.7 102.35 6342.1 75 84.6 14996.8 76 F Questions: (a) Write down the simple linear regression model and its model assumption. (b) State the null and alternative hypotheses for the model. Based on the ANOVA table, draw conclusion based on the p-value for the test. (c) Obtain Srz and the standard error se (3₁) to construct the 95% confidence interval for $₁.
Training Dept. of Nimrod Inc wants to develop a regression-based compensation model (compensation in $ per year, Comp) for its mid-level managers to encourage performance, loyalty, and continuing education based on three variables. ▪ Business unit-profitability (Profit per year in $). ▪ Working experiences in Nimrod Inc (Years). ▪ Whether or not a manager has a graduate degree (Grads). If a manager has a graduate degree equals 1, 0 otherwise. Table Attached Question: Which explanatory variables and interaction terms are significant and not significant at alpha = 5%? Explain your answer briefly.
An article in Technometrics by S. C. Narula and J. F. Wellington ["Prediction, Linear Regression, and a Minimum Sum of Relative Errors" (Vol. 19, 1977)] presents data on the selling price and annual taxes for 24 houses. The data are shown in the following table. Xis taxes paid. Taxes (Local, School), Price/1000 County)/1000 Sale 25.9 4.9176 29.5 5.0208 27.9 4.5429 25.9 4.5573 29.9 5.0597 29.9 3.8910 30.9 5.8980 28.9 5.6039 35.9 5.8282 31.5 5.3003 31.0 6.2712 30.9 5.9592 (a) Assuming that a simple linear regression model is appropriate, obtain the regression equation applicable and compute for the coefficient of determination. (b) Test the significance of the correlation coefficient. (c) Find the mean selling price given that the taxes paid is 6.111. (d) Construct a 95% Confidence Interval for the mean selling price computed in (C). Use the editor to formot your answer
In order to explain the U.S. defense budget, the following variables are considered. Data from 1962-1981 is collected. Variable Definition Y Defense budget-outlay for year t, $ billions X2 GNP for year t, $ billions X3 U.S. military sales/assistance in year t, $ billions X4 Aerospace industry sales, $ billions Dependent Variable: Y Method: Least Squares Date: 11/22/21 Time: 11:20 Sample: 1962 1981 Included observations: 20 Variable Coefficient Std. Error t-Statistic Prob. X2 0.016703 0.007017 2.380261 0.0301 X3 -0.696174 0.453978 -1.533497 0.1447 X4 1.467729 0.277608 5.287047 0.0001 22.77514 3.311695 6.877186 0.0000 R-squared 0.971088 Mean dependent var 83.86000 Adjusted R-squared 0.965667 S.D. dependent var 28.97771 S.E. of regression Sum squared resid 5.369339 Akaike info criterion 6.376143 461.2769 Schwarz criterion 6.575290 Log likelihood -59.76143 Hannan-Quinn criter. 6.415019 F-statistic 179.1337 Durbin-Watson stat 0.676777 Prob(F-statistic) 0.000000

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