Consider the following generic fixed effects model, along with a two-period (t = 1, 2), randomly sampled panel data set with dependent variable y andindependent variable x:Yit = Bo + 5o d24 + B1rit + ai +UitwhereYit = value of y for individual i at timetd2; = binary variable equal to 1 in the second time period (t = 2), and o otherwise (t = 1)Cit = value of a for individual i at time ta; = unobserved (time-invariant) effectUj = idiosyncratic errorTaking the first difference of the model (that is, subtracting the regression equation for t=1 from the regression equation for t=2) yields the followingfirst-differenced equation:Note: Let A denote the change from t=1 to t = 2.Ay; = Yi2 – Yi1=SoAu;You now plan to use OLS to estimate your first-differenced equation, in order to obtain the first-differenced estimator. Suppose that ait is a binaryvariable equal to 1 if the individual was born male, and o otherwise.True or False: The first-differenced estimator cannot be estimated in this case.O TrueO False

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Answered: Consider the following generic fixed…

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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A student at a junior college conducted a survey of 20 randomly selected full-time students to determine the relation between the number of hours of video game playing each week, x, and grade-point average, y. She found that a linear relation exists between the two variables. The least-squares regression line that describes this relation is y = -0.0552x +2.9446. (a) Predict the grade-point average of a student who plays video games 8 hours per week. The predicted grade-point average is 2.50 (Round to the nearest hundredth as needed.) (b) Interpret the slope. For each additional hour that a student spends playing video games in a week, the grade-point average will by points, on average. increase decrease Enter your answer in the answer box and then click Check Answer. parts remaining Clear All Check Answer P Type here to search TOUC Panasonic CF-54 ED 10 F8 #5 F6 F3 8. 2 FOA F1 Es & 7 0 8 8 99 2 A2 10 161 1 A1 Y 15 + 1/ ID 4圣 %U4 12
An SRS of apartment listings in a large northeastern city comparing monthly rent ($) versus size (〖ft〗^2) yields following computer output:
. A manufacturing facility uses the following model to estimate the weekly number of data chips that can be produced where: Z = number of chips produced X = number of Manufacturing Specialists Y = number of Quality Control Specialists = f(x, y) = 14,000x+400y+22x²y-13y³
Suppose that researchers obtain a random sample of adults ages 18 – 40 and collect data on the following variables: shoe size – in inches age – in years height – in inches forearm length – in inches Suppose further that a multiple linear regression model is fit to the resulting data set using R Studio and that the following output is obtained from it. Use this output to answer the question that follows: > summary(lm(shoesize ~ age + height + forearm, data = measures)) Coefficients: (Intercept)ageheightforearm Estimate10.14882 0.06045 -0.02108 -0.06479 Std. Error 4.49245 0.06838 0.06350 0.06847 t value2.259 0.884 -0.332 -0.946 Pr(>|t|) 0.0264 0.3792 0.7408 0.3467 Residual standard error: 1.719 on 85 degrees of freedomMultiple R-squared: 0.01983, Adjusted R-squared: -0.01477 F-statistic: 0.5731 on 3 and 85 DF, p-value: 0.6342 Using the information from above, fill in the blanks for the least-squares regression equation. Input all values to 5…
Suppose that researchers obtain a random sample of adults ages 18 – 40 and collect data on the following variables: shoe size – in inches age – in years height – in inches forearm length – in inches Suppose further that a multiple linear regression model is fit to the resulting data set using R Studio and that the following output is obtained from it. Use this output to answer the question that follows: > summary(lm(shoesize ~ age + height + forearm, data = measures)) Coefficients: (Intercept)ageheightforearm Estimate10.14882 0.06045 -0.02108 -0.06479 Std. Error 4.49245 0.06838 0.06350 0.06847 t value2.259 0.884 -0.332 -0.946 Pr(>|t|) 0.0264 0.3792 0.7408 0.3467 Residual standard error: 1.719 on 85 degrees of freedomMultiple R-squared: 0.01983, Adjusted R-squared: 0.01477 F-statistic: 0.5731 on 3 and 85 DF, p-value: 0.6342 What is the estimate for the standard deviation of the residuals? 1.719 0.01983 -0.946 0.6342
A researcher collected data on ( X, Y ), and wanted to test if the true relationship is linear. Therefore, she collected replicated values of X. Here is a summary of the data, and some results: Xi No. of replicates yi SSE = 312. 1 3 9 10 5 11 8. 3 2 12 14 4 4. 15 13 3 9 6 6 5 11 10 7 1 8 4 8 3 7 5 At significance level a = 0.05, carry out the test of Lack-of-Fit for linearity of the model. No need to show an ANOVA Table. Calculations: Test statistic: T = Critical value = Conclusion: Since we 2.
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The Update to the Task Force Report on Blood Pressure Control in Children [12] reported the observed 90th per-centile of SBP in single years of age from age 1 to 17 based on prior studies. The data for boys of average height are given in Table 11.18. Suppose we seek a more efficient way to display the data and choose linear regression to accomplish this task. age sbp 1 99 2 102 3 105 4 107 5 108 6 110 7 111 8 112 9 114 10 115 11 117 12 120 13 122 14 125 15 127 16 130 17 132 Do you think the linear regression provides a good fit to the data? Why or why not? Use residual analysis to justify your answer. Am I supposed to run a residual plot and QQ-plot for this question?
https://drive.google.com/file/d/1-aOh_O4aiPAqBldOcC70gIxAYx5PCMnx/view?usp=sharing
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.
The authors of a paper investigated whether water temperature was related to how far a salamander would swim and whether it would swim upstream or downstream. Data for 14 streams with different mean water temperatures where salamander larvae were released are given (approximated from a graph that appeared in the paper). The two variables of interest are x = mean water temperature (°C) and y = net directionality, which was defined as the difference in the relative frequency of the released salamander larvae moving upstream and the relative frequency of released salamander larvae moving downstream. A positive value of net directionality means a higher proportion were moving upstream than downstream. A negative value of net directionality means a higher proportion were moving downstream than upstream. y 0.3 0.2 Mean Temperature (x) 0.1 0.0 -0.1 6.22 8.01 5 8.67 10.61 12.5 11.94 12.55 18.03 18.24 (a) Construct a scatterplot of the data. 19.94 20.3 19.02 17.78 19.67 USE SALT Net…
Forest bathing, also called Shinrin-yoke, is the practice of taking short, leisurely walks in a forest to enhance positive health. To test if forest bathing and mood are related, a clinical psychologist records the time spent forest bathing in minutes (X) and the corresponding change I mood using a standard self report affect grid (Y) among 8 patients with a history of depression. The data are given in the following table. times spent forest bathing (in minutes) change in mood X Y 32 +24 24 +20 28 +25 17 +22 12 +16 Compute the method of least squares to find the equation of the regression line. Use the regression equation computed in part (a) to determine the predicted change in mood of a person who spends 20 minutes forest bathing.

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