We ran the regression Y = ßo + ß₁X₁ + ß₂X2 + ɛ with 34 observations and obtained the estimateY = 4.59 +0.29X₁ +0.4X2 + ewhere the standard error of b₁ equals 0.1, the standard error of b₂ equals 0.3, and Cov(b₁,b₂) =-0.004. To test the nullhypothesis Ho 36₁ + ₂ = 0.3 against the two-sided alternative, compute the t-statistic.a. 2.46b. 3.20c. 2.34O d. 1.29

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Answered: We ran the regression Y = Bo + B₁X₁ +…

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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q14
You run a regression analysis on a bivariate set of data (n=82n=82). You obtain the regression equationy=4.373x+9.596y=4.373x+9.596with a correlation coefficient of r=0.987r=0.987 (which is significant at α=0.01α=0.01). You want to predict what value (on average) for the explanatory variable will give you a value of 60 on the response variable.What is the predicted explanatory value?x = (Report answer accurate to one decimal place.)
1) What is the slope(b1) and what is its statistical interpretation? 2) Perform the test of hypothesis (t-test) on the following and state your statistical decision. Use α = 0.01. Please show all the relevant calculations. H0: β1 = 0 H1: β1 ≠ 0 3) Perform the test of hypothesis (F-test) on the following and state your statistical decision. Use α = 0.01. Please show all the relevant calculations. H0: β1 = 0 H1: β1 ≠ 0 4) What is the coefficient of determination (r2) i.e., percentage of variability in the monthly rent explained by the independent variable rental property area? Please show all the relevant calculations. 5) What is the 99% confidence interval for the population slope(β1)? Please show all the relevant calculations.?
N= 150 observations were collected on a time series that was identified as a AR(2) time series. The following statistics were computed from the data. Mean - 45.0 Variance 15.6 Autocorrelation function (up to lag 5) r1 = 0.80, r2 = .50, rз = .26, r4 = -.10, rs = 0.08: == Estimate the parameters of the model using the method of moments.
The following estimated regression equation based on 10 observations was presented ŷ = 25.1270 + 0.5309x1 + 0.4920x2 Here, SST = 6,738.125, SSR = 6,221.375, sb1 = 0.0818, and sb2 = 0.0563. Compute MSR and MSE. (Round your answers to three decimal places.) Compute F and perform the appropriate F test. Use α = 0.05. State the null and alternative hypotheses. a. H0: β1 ≠ 0 and β2 ≠ 0 Ha: One or more of the parameters is equal to zero. b. H0: β1 = β2 = 0 Ha: One or more of the parameters is not equal to zero. c. H0: β1 > β2 Ha: β1 ≤ β2 H0: β1 < β2 Ha: β1 ≥ β2 d. H0: β1 ≠ 0 and β2 = 0 Ha: β1 = 0 and β2 ≠ 0 2. Find the value of the test statistic. (Round your answer to two decimal places.) 3. Find the p-value. (Round your answer to three decimal places.) p-value = 4. State your conclusion. a. Reject H0. There is sufficient evidence to conclude that the overall model is significant. b. Do not reject H0. There…
A certain model of car can be ordered with either a large or small engine. The mean number of miles per gallon for cars with a small engine is 6.8. An automotive engineer thinks that the mean for cars with the larger engine is higher than this. State the appropriate null and alternate hypotheses. The null hypothesis is Hoμ (Choose one) The alternate hypothesis is H₁ μu (Choose one) ×
You are performing a two-tailed test.If α=.002α=.002, find the positive critical value, to three decimal places.zα/2
We calculate the correlation between the percent of adult population fully vaccinated for COVID-19 and the COVID-19 death rate per 100,000 in 2021 for the states and territories of the United States (55 observations). We observe a correlation r=-0.3558 Test the hypothesis H_0: ρ=0 against H_a: ρ≠0 at the 99% level (α=.01)
Consider a randomized block design involving three treatments and two blocks. Define all variables. X1 X2 Treatment 1 2. 1 3 Let x, = 0 if ---Select-- v is in effect and 1 if --Select--- v is in effect. Write a multiple regression equation that can be used to analyze the data. E(y) =
Suppose that n= 195 I.I.d. observations for (Y, X;) yleld the following regression results: v= 32.68 + 67.55Xx, SER= 18.63, R = 0.84 (15.7) (12.5) Another researcher is interested in the same regression, but he makes an error when he enters the data into the regression: He enters each observation twice, so he has 390 observations (with observation 1 entered twice, observation 2 entered twice, and so forth). Which of the following eatimated parameters change result? (Check all that apply) O A. The estimated intercept and slope. O B. The R of the regression. Yc. The standard error of the regrussion (SER). YD. The standard errors of the estimated coefficients. Using the 390 observations, what results will be produced by his regression program? Ý= 32.68 + 67.55X, SER =.R = 0.84 (Round your responses to two decimal places)

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