1. See the output of the following model,Dependent Variable: Y, Method: Least Squares, Observations 569.VariableCoefficientStd. ErrorConstantX1X2X3Y = a + B₁X₁ + B₂X₂ + B3X3 + ε6.177290-0.9277640.990047-0.003697R-squared 0.842971Mean dependent var 4.488665Adjusted R-squared 0.842138S.D. dependent var 1.171166S.E. of regression 0.465327Akaike info criterion 1:314851Sum squared residual 122.3388a) 82.429b) 84.429c) 86.421d) None of the above0.2462110.0714050.0264100.018770t-statistic25.08946-12.9930537.48715-0.196992P-value0.000000.000000.000000.08439Schwarz criterion 1.345388Log likelihood 370.0750Hannan-Quinn 1.326766F-statistic 1011.023Durbin-Watson stat 2.027822Prob(F-statistic) 0.000000In the context of the above model, test the joint hypothesis P₁ = B3 = 0 using a 5%significance level. The RSS of the restricted model is 158.9015. What is the F-statistic?

ObservationsN=569RRSS=158.9015URSS=122.3388 where RRSS and URSS are the residual sums of squares of…

Answered: 1. See the output of the following…

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 null hypothesis being tested in the least-squares regression output for B is B1 = B1,0=1. True False
The y-interept bo of a least-squares regression line has a useful interpretation only if the x-values are either all positive or all negative. Determine if the statement is true or false. Why? If the statement is false, rewrite as a true statement.
There may be an association between a country's birthrate and the life expectancy of its inhabitants. A report this past year, coming from a random sample of 20 countries, contained the following information: the least-squares regression equation relating the two variables number of births per one thousand people (denoted by x) and female life expectancy (denoted by y and measured in years) is y = 82.28 – 0.51 x, and the standard error of the slope of this least-squares regression line is approximately 0.35. Based on this information, test for a significant linear relationship between these two variables by doing a hypothesis test regarding the population slope B,. (Assume that the variable y follows a normal distribution for each value of x and that the other regression assumptions are satisfied.) Use the 0.10 level of significance, and perform a two-tailed test. Then complete the parts below. (If necessary, consult a list of formulas.) (a) State the null hypothesis H, and the…
The model, y = Bo + B₁×₁ + ß₂×2 + ε, was fitted to a sample of 33 families in order to explain household milk consumption in quarts per week, y, from the weekly income in hundreds of dollars, x₁, and the family size, x₂. The total sum of squares and regression sum of squares were found to be, SST = 162.1 and SSE(R) = 90.6. The least squares estimates of the regression parameters are bo = -0.022, b₁ = 0.051, and b₂ = 1.19. A third independent variable-number of preschool children in the household-was added to the regression model. The sum of squared errors when this augmented model was estimated by least squares was found to be 83.1. Test the null hypothesis that, all other things being equal, the number of preschool children in the household does not affect milk consumption. Use α=0.01. Click here to view page 1 of a table of critical values of F. Click here to view page 2 of a table of critical values of F. ''1' M P2 P3 Find the critical value. The critical value is 7.60⁰. (Round to…
Can someone please explain to me ASAP??!!
Assuming the model is re-estimated with additional new variable (lagged of LGDP), based on the new estimated results, what is your conclusion about the autocorrelation diagnostic test? If there is diagnostic problem, show how to solve the problem.
For a linear regression problem, The actual observed y-value when x = 2 is y = 6. The least squares line is y = 2x+1. The predicted value when x=2 is y = 5. Determine the value of the residual (prediction error) oooo 1 6 5 Cannot be determined from the information given
In a study of 1991 model cars, a researcher computed the least-squares regression line of price (in dollars) on horsepower. He obtained the following equation for this line. Price = – 6677 + 175× Horsepower Based on the least-squares regression line, what would we predict the cost to be of a 1991 model car with horsepower equal to 200? If the actual cost of a 1991 car with 200 horsepower is $27500, what is the residual? Is the predictionan underestimate or an overestimate? What does the slope of 175 and y intercept of (0,-6677) mean in the context of the problem? The coefficient of determination is ?2=84%. Interpret in the context of the problem. Find the correlation and interpret.
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A prospective MBA student would like to examine the factors that impact starting salary upon graduation and decides to develop a model that uses program per-year tuition as a predictor of starting salary. Data were collected for 37 full-time MBA programs offered at private universities. The least squares equation was found Y; = -13258.594 + 2.422X;, where X; is the program per-year tuition and Y; is the predicted mean starting salary. To perform a residual analysis for these data, the following results are obtained. of regression have been seriously violated. Residual index plot QQ Plot of Residuals Residuals Residuals 20000- 20000 0. -20000 -20000 a) To evaluate whether the assumption of linearity has been violated, which of the following graph shou be examined? A. Predicted Values vs. Residuals B. Residual index plot C. QQ plot of residuals D. Residuals vs. Progrm Per-Year Tuition ($) b) To evaluate whether the assumption of normality has been violated, which of the following graph…

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