Problem 4 Based on the regression output in the following table (sample size n = 14), test the followinghypothesis using a = 0.05:Table 1: Regression Output for Computer Repair DataVariable Coefficients.e. t-Test p-value3.355 1.24 0.2385Constant4.162Units15.509 0.505 30.71 <0.0001(a) Ho 3115 versus H₁ : 31 # 15:=(b) Ho 31 15 versus H₁: 3₁ > 15:=(c) Ho 300 versus H₁: 30 0:=(d) Ho So 5 versus H₁: 30 # 5=Using the regression output in Problem 4, construct the 98% confidence interval for 30.

The question is about regression analysis.Regression output for computer repair data.Variable…

Answered: Problem 4 Based on the regression…

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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Question 12 An analysis of variance experiment produced portions of a company ANOVA table Source of variations S.S Between groups Within groups d.f MS 25.08 Fral 3 a C. Total 117.72 79 N.B F(3,76)5% 2.724 and F3 79, 5% = 2.70 1. Specify the competing hypothesis in order to determine the population means. 2. Find the missing statistics a, b and c in the ANOVA table. 3. At the 5% significant level, what is the conclusion to the test? • Paragraph B IU A A I E E I I * x, E E E- & V G D I T 12pi
The data is given as follow. xi 2 6 9 13 20 yi 7 18 9 26 23 The estimated regression equation for these data is = 7.6 + .9x.Compute SSE, SST, and SSR (to 1 decimal). SSE SST SSR What percentage of the total sum of squares can be accounted for by the estimated regression equation (to 1 decimal)? %What is the value of the sample correlation coefficient (to 3 decimals)?
The following output was obtained from a multiple regression analysis. Analysis of variance SOURCE DF SS MS Regression 5 100 20 Residual Error 20 40 2 Total 25 140 Predictor Coefficient SE Coefficient t Constant 3.00 1.50 2.00 x1 4.00 3.00 1.33 x2 3.00 0.20 15.00 x3 0.20 0.05 4.00 x4 −2.50 1.00 −2.50 x5 3.00 4.00 0.75 a. What is the sample size? b. Compute the value of R2. (Round your answer to 4 decimal places.) c. Compute the multiple standard error of estimate. (Round your answer to 4 decimal places.) Conduct a global test of hypothesis H0: β1 = β2 = β3 = β4 = β5 = 0; H1: Not all β's are 0 at the 0.05 significance level. d-1. State the decision rule. (Round your answer to 2 decimal places.) Reject H0 if F> d-2. What is the computed value of F? (Round your answer to 1 decimal place.) Value of F d-3. Determine whether any of the…
QUESTION 4 PART A- What is the standard error of the correlation coefficient for the association of extroversion and neuroticism in personalities? QUESTION 4 PART B- Carry out a formal t-test for the association between extroversion and neuroticism in personalities.xls. H0: The population correlation coefficient is 0. HA: The population correlation coefficient is not 0. What is the value of the t-statistic corresponding to this test? (Report the absolute value. The t-distribution is symmetric, thus it is easier to compare the absolute value of the t-statistic with the positive values in statistical table C). DATA IS ATTACHED.
The following output comes from regression using the actual HW9 scores and the Final test scores from intro stats one semester. Sample size: 50R (correlation coefficient) = 0.3658R-sq = 0.1338Estimate of error standard deviation: 10.36256Parameter estimates: Parameter Estimate Std. Err. DF T-Stat P-Value Intercept 43.558118 11.1716 48 3.899 0.0003 Slope 0.360048 0.132225 48 2.723 0.009 Assume no assumptions are violated. Form a 87% Confidence interval for how much your final is supposed to increase for each problem done on homework 9.Use 5 decimal places
12. The components of the OLS variances Under assumptions MLR.1 through MLR.5, conditional on the sample values of the independent variables, the variance of B; under OLS is: Var (B) sST, (1-15) where o2 = the variance of the error term SST; = the total sample variation of a; R; = the R-squared from a regression of r; on all of the other explanatory variables, along with an intercept parameter The variance of Bi v as R; increases. decreases For the given mode e following would lead to a reduction of the sampling variance of B;? increases O Adding variable that is equal to 5+ *j O Using the same data set with the same number of observations O Adding irrelevant explanatory variables to the model that are correlated with r; O Choosing an explanatory variable z that is uncorrelated with the other explanatory variables in the model
In this problem, we use your critical values table to explore the significance ofr based on different sample sizes. Critical Values for Correlation Coefficient r a = 0.05 =0.01 „A=0.05 a = 0.01 a = 0.05 a = 0.01 3. 1.00 1.00 13 0.53 0.68 23 0.41 0.53 4. 0.95 0.99 14 0.53 0.66 24 0.40 0.52 0.88 0.96 15 0.51 0.64 25 0.40 0.51 6. 0.81 0.92 16 0.50 0.61 26 0.39 0.50 0.75 0.87 17 0.48 0.61 27 0.38 0.49 8. 0.71 0.83 18 0.47 0.59 28 0.37 0.48 9. 0.67 0.80 19 0.46 0.58 29 0.37 0.47 10 0.63 0.76 20 0.44 0.56 30 0.36 0.46 11 0.60 0.73 21 0.43 0.55 12 0.58 0.71 22 0.42 0.54
The cadet is a popular model of sport utility vehicle, known for its relatively high resale value. The bivariate data given below were taken from a sample of sixteen cadets, each bought new two years ago, and each sold used within the past month . For each cadet in the sample, we have listed both the mileage x (in thousands of miles) that the cadet had on its odometer at the time it was sold used and the price y (in thousand dollars) at which the cadet was sold used. The least-squares regression line for these data has equation of y=41.79-0.50x. This line is shown in the scatter plot below. A) for these data, used selling prices that are greater than the mean of the used selling prices tend fo be paired with mileages that are ___ the mean of the mileages ? B) according to the regression equation, for an increase of one thousand miles in cadet mileage, there is a corresponding decrease of how many thousand dollars in the used selling price?
Part A of question one asks for the variance (sigma squared), not the equation of regression line. Is the variance 2.79 as I listed on my answer sheet? Maybe my image didn't go through, but I already had the answer to subparts B and C to the question, which you list as #2 and #3. But I need help with parts D, E, and F. I have the following answers for D. d. Y hat (fitted value = 426.46), with residual of -1.62. e. r squared value = 1.00 g. 99% confidence interval = (-11.63, -1.05) Thanks thanks

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