1. Let's say X is a main predictor (or exposure) of your interest and M is a potential mediator,and Y is an outcomeModel 1: Y = Bo(1) + tX+ E(1)Model 2: Y= Bo(2) + t’X+ BM+ ɛ(2)Model 3: M Воз) + аХ + £(3)Indicate the p-value of each regression parameter in the table below. Please fill the p-value in thetable whether it must be greater or less than 0.05 for confirming M as a mediator as per Baron-Kenny's Criteria.t' (full mediationmodel)t' (partial mediationтodel)p-value

In this problem, we are investigating the potential mediating effect of variable M in the…

Answered: 1. Let's say X is a main predictor (or…

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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The manufacturer of Beanie Baby dolls used quarterly price data for 2012/-2020/V (t = 1, ..., 36) and the regression equation Pt= a + bt+c₁D1 t + c2 D2 + + c3 D3 t to forecast doll prices in the year 2021. Pt is the quarterly price of dolls, and D1, D2t, and D3+ are dummy variables for quarters I, II, and III, respectively. DEPENDENT VARIABLE: PT R-SQUARE P-VALUE ON F 0.0001 OBSERVATIONS: F-RATIO 76.34 STANDARD 36 0.9078 PARAMETER VARIABLE ESTIMATE ERROR T-RATIOP-VALUE INTERCEPT 24.0 6.20 3.87 0.0005 T 0.8 0.240 3.33 0.0022 D1 -8.0 2.60 -3.08 0.0043 D2 -6.0 1.80 -3.33 0.0022 D3 -4.0 0.60 -6.67 0.0001 The estimated quarterly increase in price is and the estimated annual increase in price is Multiple Choice O O $1.50; $6.00 $1.40; $4.00 $0.60; $2.40 $0.80; $3.20 None of the choices are correct.
5. A researcher measures how positive a person's mood is and how creative he or she is, obtaining the following interval scores. (a) Calculate the linear regression equation. (b) What is the predicted (Y') creativity score for anyone scoring 3 on mood? (c) What is the standard error of the estimate? (d) Interpret the standard error of the estimate for this relationship. Participant Mood X Creativity Y 110728639114645556377748 259 46 10 14
The maintenance manager at a trucking company wants to build a regression model to forecast the time (in years) until the first engine overhaul based on four predictor variables: (1) annual miles driven (in 1,000s of miles), (2) average load weight (in tons), (3) average driving speed (in mph), and (4) oil change interval (in 1,000s of miles). Based on driver logs and onboard computers, data have been obtained for a sample of 25 trucks. A portion of the data is shown in the accompanying table. Time Miles Load Speed Oil 7.7 42.9 22.0 44.0 16.0 0.8 98.3 20.0 47.0 34.0 6.3 61.1 22.0 62.0 15.0 E Click here for the Excel Data File b. Estimate the regression model. (Negative values should be indicated by a minus sign. Round your answers to 2 decimal places.) Time = Miles Load Speed oil + + d. What is the predicted time before the first engine overhaul for a particular truck driven 60,000 miles per year with an average load of 25 tons, an average driving speed of 53 mph, and 21,000 miles…
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.…
Look at the following regression table where the dependent variable is the demand for illegal massage services in a city in the United States. Specifically, the dependent variable is the number of customers per hour (Models 1 and 2) or per day (Models 3 and 4). (a) Explain why the coefficient for Population/1,000 in Model 2 is very different from the one in Model 4?(b) Can you reject H0 in Model 1 if H0 : βP opulation/1,000 = 0.01, H1 : βPopulation/1,000 6= 0.01, and α = 0.01?
ANSWER THE FOLLOWING QUESTION.
4. Our R² implies that lots of stuff, other than health, also affects doctor visits. One such thing is a person's insurance status. The data file includes a third variable that records whether the person had health insurance during 2019. Estimation a regression of the form y = Bo + B₁x1 + B₂x₂ where x₁ is the health status variable from above, but now x₂ records whether the person had insurance. a) Interpret the estimate of B₁ in words. b) Interpret the estimate of B₂ in words. c) Forecast a person's number of doctor visits in 2019 if he/she was in excellent health, but did not have insurance. d) Forecast a person's number of doctor visits in 2019 if he/she was in poor health, and did have insurance. e) The R² for this regression is
29)
3. Consider the following regression model: Weekly Hours = Bo + B1 × Wage + uj Weekly Hours is the average number of hours the individual worked over the course of the year and Wage is the individual's average hourly wage over the course of the year. A researcher who collects data and regresses Weekly Hours against Wage finds that B1 > 0. The OLS estimator, B, however, likely suffers from omitted variable bias because those individuals who earn high wages may be driven personalities who would work long hours no matter the wage. Because of this omitted variable bias, it is likely the case that B1_B1. A) В)
1
Suppose we have a multiple regression model with 2 predictors and an intercept. (Without any interaction or higher order terms, we have only the 2 predictors in the model and the intercept.) We have only n= 6 observations (so it would be rather silly to fit this model to this data, but let's pretend it is reasonable). We find the values of the first 5 residuals are: 2.6, 2.3, 2.5, -1.5, -1.4 What is the value of MSRes for this multiple regression model?
It’s not 49 ()()))()(!&&):$:&:&

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