Consider the linear regression model: log(wage)=DB1+B2 MATH+B3 ARABIC + e where wage is the hourly wage and MATH is you scorein Math courses and ARABIC is your score in Arabic courses. You would like to test the hypothesis that MATH and ARABIC have thesame effect on log(wage) against the alternative that MATH has a greater weight on log(wage)a. Formally state the null and the alternative hypothesesb. The OLS results from a sample of 300 observations are reported belowlog(wage)= 2.55+ 0.85 MATH+2.75 ARABICR-square= 0.55)se (1.55) (0.035)(1.25)Test the hypothesis that you stated in (a) at 5% level given that the cov(b2,b3)=0.

The linear regression model: wage= The hourly wageMATH= Score is math coursesARABIC= Score in Arabic…

Answered: Consider the linear regression model:…

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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Juliet is studying the change in the amount of chemical (in grams) present in her solution as time (in minutes) goes on. She finds a least squares regression line of y = 30 − 2.1 x and a coefficient of determination r 2 = 0.49. After 10 minutes, we predict she has how many grams left. The correlation coefficient is
A soft drink bottling company wants to develop a regression model to predict delivery time (in minutes) based on the number of cases delivered. A sample of 16 deliveries was selected. The Excel output for this regression model is given below. (a) State the regression equation. (b) Interpret the meaning of b0 and b1 in this problem. (c) What is the predicted delivery time when the number of cases is 30 , assuming 30 is within the range of number of cases delivered for this problem? (d) At the 0.05 level of significance, is there evidence of a linear relationship between the number of cases delivered and the delivery time? (e) Construct a 95% confidence interval estimate of the population slope β1. Interpret the confidence interval estimate. (f) How useful do you think this regression model is for predicting the delivery times?
With the aim of predicting the selling price of a house in Newburg Park, Florida, from the distance between the house and the beach, we might examine a regression equation relating the two variables. In the table below, the distance from the beach (x, in miles) and selling price (y, in thousands of dollars) for each of a sample of fifteen homes sold in Newburg Park in the past year are given. The least-squares regression equation relating the two variables is î-310.94 – 6.27x. The line having this equation is plotted in Figure 1. Selling price, y (in thousands of dollars) Distance from the beach, x (in miles) 8.6 290.7 12.8 198.8 10.0 230.7 350 7.6 272.4 3.6 261.5 300 10.4 274.8 12.5 267.2 250 15.1 183.6 18.8 225.6 200 7.6 244.0 4.3 314.0 150 8.2 220.8 6.1 311.1 12.0 234.1 Figure 1 10.6 205.8 Send data to Excel Based on the above information, answer the following: 1. Fill in the blank: For these data, distances from the beach that are greater than the mean of the distances from the…
Use Excel to find an estimated linear regression equation using X1 and X2 as the independent variables and Y as the dependent. Put your output on a fresh sheet. Then answer the following questions (excel chart in the picture) Write down the estimates regression equation What is the expected value of Y if X1 is 240 and X2 is 290? Show your calculation at a 5%level of significance, are the true coefficients of X1 and X2 different from zero? Justify briefly using numbers from your excel output. 4. Using Excel, determine a 97% confidence interval for the true coefficient of X1
A sports statistician was interested in the relationship between game attendance (in thousands) and the number of wins for baseball teams. Information was collected on several teams and was used to obtain the regression equation ý = 4.9+ 15.2, where x represents the attendance (in thousands) and ý is the predicted number of wins. What is the predicted number of wins for a team that has an attendance of 17,000? O 83.3 wins O 98.5 wins O 258.4 wins 263.3 wins
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Suppose you wanted to estimate the effect of being educated on being in the labor force. You estimate a model with two variables, LF is a binary variable = 1 if the person is participating in the labor force and the variable educ measures the number of years of education a person has received. You get the following estimated regression: LF = 0.1 + 0.15educ Which of the following is the correct interpretation about the effect of an additional year of education? O A 1% increase in education increases the number of those participating in the labor force by 15 percentage points. A additional year in education increases the number of those participating in the labor force by 0.15 percentage points A additional year in education increases the number of those participating in the labor force by 0.15% An additional year of education increases the probability of participating in the labor force by 15 percentage points. An additional year of education increases the probability of participating…
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You may need to use the appropriate technology to answer this question. A regression analysis involving 45 observations relating a dependent variable and two independent variables resulted in the following information. ŷ = 0.406 + 1.3385x₁ + 2x₂ The SSE for the above model is 43. When two other independent variables were added to the model, the following information was provided. ŷ = 1.9 - 3x₁ + 12x2 + 4x3 + 8x4 This model's SSE is 36. At a 0.05 level of significance, test to determine if the two added independent variables contribute significantly to the model. State the relevant null and alternative hypotheses. O Ho: One or more of the parameters is not equal to zero. H₂: B₁ = P₂ = P3 =B4 = 0 B1 O Ho: One or more of the parameters is not equal to zero. H₂: B3 =B₁ = 0 O Ho: B3 = P4 = 0 H₂: None of the parameters are equal to zero. O Ho: B3 B4= = = H₂: One or more of the parameters is not equal to zero. O Ho: P₁ = P₂ = P3= P4= H: One or more of the parameters is not equal to zero. Find…

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