We want to estimate the treatment effect of D ony via a linear regression model y = bo+b1x +rD+u.where x is an exogenous controi variable. If the value of the treatment variable Disrandomly assigned. thereO a. the OLS estimate of r is biased due to sample selection.Ob.the OLS estimate ofris unbiased.Oche error u is homoskedastic.Od.the OLS estimator is BLUE. O a. the OLS estimate of r is biased due to sample selection.O b.the OLS estimate of r is unbiased.Oc the error u is homoskedastic.O d. the OLS estimator is BLUE.

Answer : If the value of the treatment variable D is randomly assigned, then the OLS estimate of r…

Answered: We want to estimate the treatment…

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 researcher was interested in investigating whether the parent-child interaction time (in hours) is a predictor of child’s cognitive development. A sample of 100 children shown cognitive tests score as below. The least-square regression equation is: y’= 54 + 11.5 *x.Note. cognitive test score is denoted as y and the interaction time is denoted as x. 1) Describe the correlation between x and y base on the scatterplot 2) Find the correlation coefficient between x and y. 3) Interpret the intercept and the slop 4) Base on the regression equation, estimate the predicted cognitive level for a child who parent interacts with him 7 hours per day?
researcher would like to predict the dependent variable YY from the two independent variables X1X1 and X2X2 for a sample of N=20N=20 subjects. Use multiple linear regression to calculate the coefficient of multiple determination and test the significance of the overall regression model. Use a significance level α=0.05α=0.05. X1X1 X2X2 YY 33.2 35.8 45.7 25.1 47.7 41.9 86.6 54.4 89.5 55.8 51.3 76.8 65.8 60.5 40.9 26.9 50.1 44.7 67.5 58.1 61.7 35.9 61.9 20 48.8 44.8 52.8 47.9 42.7 44.9 43.4 56.6 15.4 50.9 43.7 53.9 51.3 46 79.8 39.8 55.1 56 33 56.1 30.6 45.9 55 47.6 19.4 50.9 40.4 74.7 45.8 84.6 62.3 58.9 86.5 64.2 64.1 49.2 SSreg=SSreg= SSres=SSres= R2=R2= F=F= P-value = What is your decision for the hypothesis test? Reject the null hypothesis, H0:β1=β2=0H0:β1=β2=0 Fail to reject H0H0 What is your final conclusion? The evidence supports the claim that one or more of the regression coefficients is non-zero The evidence supports the…
The sample regression line Y,-A+bX, provides a measure of O 1.the estimated population Value of the dependent varlable given a specific value of the independent varlable O2.the estimated population value of the Independent varlable glven a specific value of the dependent variable O 3. the estimated population value of the dependent varlable regardless of the value of the Independent variable 04.the estimated population value of the independent varlable regardless of the value of the dependent variable O5. the estimated population average value of the dependent variable glven a specific value of the independent variable 6.the estimated population average value of the independent variable given a specific value of the dependent variable 07.the estimated population average value of the dependent variable regardless of the value of the independent varlable O8. the estimated population average value of the independent variable regardless of the value of the dependent varlable
Comoute the least-squres regression equation for predicting y from x given the following summary statistics.
A student used multiple regression analysis to study how family spending (y) is influenced by income (x1), family size (x2), and additions to savings(x3). The variables y, x1, and x3 are measured in thousands of dollars. The following results were obtained. ANOVA df SS Regression 3 45.9634 Residual 11 2.6218 Coefficients Standard Error Intercept 0.0136 x 1 0.7992 0.074 x2 0.2280 0.190 x3 -0.5796 0.920 Required: c. Carry out a test to determine whether y is significantly related to the independent variables. Use a 5% level of significance. d. Carry out a test to see if x3 and y are significantly related. Use a 5% level of significance.
A baseball enthusiast carried out a simple linear regression to investigate whether there is a linear relationship between the number of runs scored by a player and the number of times the player was intentionally walked. Computer output from the regression analysis is shown. Variable DF Estimate SE Intercept 1 16 2.073 Intentional Walks 1 0.50 0.037 R-sq=0.63R-sq=0.63 Let β1β1 represent the slope of the population regression line used to predict the number of runs scored from the number of intentional walks in the population of baseball players. A tt-test for a slope of a regression line was conducted for the following hypotheses. H0:β1=0Ha:β1≠0 below are the options provided thank you
q13-
Q2. The Update to the Task Force Report on Blood Pressure Control in Children reported the observedaverage SBP in single years of age from age 1 to 17 based on prior studies. The data for boys of averageSBP at each age are given in HW 5 data.sav on Blackboard. Suppose we seek a more efficient way todisplay the data and choose linear regression to accomplish this task.1) Fit a regression line using age as independent variable and SBP as dependent variable. (Theappropriate output is required here if you used software)2) Interpret the regression equation.3) Provide and interpret a 90% confidence interval for the parameters of the regression line.4) Perform ANOVA test for the population slope. Use α = 0.05, and both rejection region and p-value to make a decision.5) What the predicted (estimated) blood pressure on average is for a 10-year-old boy as estimatedfrom the regression line?6) Interpret R2.7) Check the conditions for your regression analysis using the appropriate plots. Age…
A student used multiple regression analysis to study how family spending (y) is influenced by income (x1), family size (x2), and additions to savings (x3). The variables y, x1, and x3 are measured in thousands of dollars. The following results were obtained. ANOVA df SS Regression 3 45.9634 Residual 11 2.6218 Total Coefficients Standard Error intercept 0.0136 x1 0.7992 0.074 x2 0.2280 0.190 x3 -0.5796 0.920 answer please : 1: Carry out a test to see if x3 and y are significantly related. Use a 5% level of significance.
Assume that you have collected a sample of observations from over 100 households and their consumption and income patterns. Using these observations, you estimate the following regression C₁ = Bo + B₁Y₁+H₁, where C is consumption and Y is disposable income. The estimate of B₁ will tell you: AIncome O A. APredicted Consumption OB. APredicted Consumption AIncome OC. Predicted Consumption Income OD. The amount you need to consume to survive.
Suppose that you run a regression of Y, on X, with 110 observations and obtain an estimate for the slope. Your estimate for the standard error of ₁ is 1. You are considering two different hypothesis tests: The first is a one-sided test: Ho: B1-0, Ha: 31>0, a = .05 The second is a two-sided test: Ho: 31-0, Ha: B1 0,a = .05 (a) What values of , would lead you to reject the null hypothesis in the one-sided test? (b) What values of , would lead you to reject the null hypothesis in the one-sided test? (c) What values of would lead you to reject the mill hypothesis in the one-sided test, but not the two-sided test? (d) What values of 3 would lead you to reject the null hypothesis in the two-sided test, but not the one-sided test?
Bxi + €i, where €; are independently and Consider a simple linear regression model Y; = identically distributed with mean 0 and variance o?, and i = 1,..., n. Note: this model does not have a intercept term. Derive the Best Linear Unbiased Estimator (BLUE) for B. Denote this by BBLUE. Make sure to state why this is the BLUE.

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