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.
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
Mark Gershon, owner of a musical instrument distributorship, thinks that demand for guitars may be related to the number of television appearances by the popular group Maroon 5 during the previous month. Gershon has collected the data shown in the following table: Maroon 5 TV Appearances Demand for Guitars 3 4 5 8 6 5 9 6 8 8 6 9 This exercise contains only parts b, c, and d. b) Using the least-squares regression method, the equation for forecasting is (round your responses to four decimal places): Y=0+0x
5) Please answer the question and show your work in the space provided. Medhavi suspects that there is a relationship between the number of text messages high school students send and their academic achievement. To explore this, she asks a random sample of 52 students at her school how many text messages they sent yesterday and what their grade point average (GPA) was during the most recent marking period. Her data are summarized in the scatter plot below. The least squares regression line is also shown. The equation of the least squares regression line is GPA=3.8-0.005(TextsSent). Interpret the quantities -0.005 and 3.8 in the context of the data. 4.25 4.00 - 3.75 3.50 3.25 3.00 - 2.75 2.50 - 20 40 60 80 100 120 140 160 Texts Sent
This data table contians the listed prices and weights of the diamonds in 48 rings offered for sale in The Singapore Times. The prices are in Singapore dollars, with the weights in crats. Estimate the linear regression using weight as the explanatory variable and price as the response variable using the following two methods: a) Create a scatterplot with trendline (be sure to show the equation and R-square on the chart.) b) Use the Data Analysis Toolpak to do the work for you.

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