t test of a multiple regression modelJ OO D 3/5EspañolUsing 26 observations on each variable, a computer program generated the following multiple regression model.y = 85.8 + 8.05x, -2.61x,-2.41x,+7.77x4+8.05x5If the standard errors of the coefficients of the independent variables are, respectively, 5.00, 2.53, 1.44, 6.48, and 3.82, can you conclude that theindependent variable x, is needed in the regression model?Let B, B,, ., B, denote the coefficients of the 5 variables in this model, and use a two-sided hypothesis test and significance level of 0.10 to determineyour answer.(a) State the null hypothesis H and the alternative hypothesis H,.H, 0H, :0(b) Determine the type of test statistic to use.Degrees of freedom: dfn: dfd:|O=0OSO(c) FtChi-Squaree test statistic. (Round to two or more decimal places.)(d) Find the two critical values at the 0.10 level of significance. (Round to two or more decimal places.)I and 0(e) Can you conclude that the independent variable x, is needed in the regression model?O Yes ONo

Given, Using 26 observations on each variable, a computer program generated the following multiple…

Answered: Español Using 26 observations on each…

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 is interested to measure returns to schooling. He ran the regression below: w = a + b*School where w is the hourly wage, 'School' measures years of schooling and b is the coefficient on schooling. Fill in the missing blanks to make the statement correct. Omitting an important variable а. biases b only if it is not related to the 'School' variable. b. does not affect the estimate of b. It only affects the standard error of the estimated coefficient. C. biases b and affects the standard error of the estimated coefficient. d. biases b only if it is related to the 'School' variable.
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The regression equation is: ŷ = 67.16 + 8.417x where ŷ is the miles traveled, and x is the MPG. The sample size used was all 110 MPG records. The correlation coefficient r = 0.620. Use the information to obtain an estimate of my mileage if my MPG is 22. Is it option: a.) cannot estimate ŷ rcrit = 0.195; the correlation IS NOT significant b.) ŷ = 252.33 rcrit = 0.195; the correlation IS significant c.) ŷ = 252.33 rcrit = 0.187; the correlation IS significant d.) cannot estimate ŷ rcrit = 0.187; the correlation IS NOT significant
The following table shows the starting salary and profile of a sample of 10 employees in a certain call center agency. Run a multiple regression analysis with starting salary as the dependent variable (pesos) and GPA, years of experience and civil service ratings as the independent variables. Use .05 level of significance. Copy-paste the data and run the analysis using MS Excel. starting salary GPA Years of experience Civil Service Ratings 15000 80.1 1 79.5 15000 81.2 1 78.0 15500 81.3 2 79.0 16000 82.4 3 80.0 16200 83.4 3 85.0 17500 87.9 4 89.9 18000 90.3 5 89.1 16300 84.2 3 84.1 17000 87.0 4 89.0 17900 88.1 5 89.2 What is the computed R square of the resulting multiple linear regression and its interpretation?
A set of n = 25 pairs of scores (X and Y values) produces a regression equation Y = 3X – 2. Findthe predicted Y value for each of the following X scores: 0, 1, 3, -2.
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. ý = 0.406 + 1.3385X, + 2X2 The SSE for the above model is 43. When two other independent variables were added to the model, the following information was provided. ý = 1.9 – 3X + 12X2 + 4Xg + 8x, 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 = B₂= B3 =B4 = 0 O Ho: One or more of the parameters is not equal to zero. H₂: B3 =B4 = 0 O Ho: B3 = P4 = 0 H₂: None of the parameters are equal to zero. ⒸH₁: B3 =B₁ = 0 H: One or more of the parameters is not equal to zero. O Ho: B₁ = P₂ = B3 =B4 = 0 H: One or more of the parameters is not equal to zero. ✔ Find…
Answer A B C
The statistic used to test whether individual regression coefficients are different from zero in the population is: Select one: a. F O b. b Oc. R2 O d. t Clear my choice
A math professor selects 8 students at random from her calculus class and asks them to record their study times. After two weeks, she gives an exam. the number of hours, x, of study time and the test score, y, yield the following sample data summary: Ex = 117 Ey = 660 8 %3| n 3= Ea2 = 1869 54638 9519 The professor is a bit surprised. a) Find the equation of the regression line. b) Use the regression line to predict the score of a student who studies for 20 hours over the two weeks.
An automobile rental company wants to predict the yearly maintenance expense (Y) for an automobile using the number of miles driven during the year () and the age of the car (, in years) at the beginning of the year. The company has gathered the data on 10 automobiles and run a regression analysis with the results shown below:. Summary measures Multiple R 0.9689 R-Square 0.9387 Adj R-Square 0.9212 StErr of Estimate 72.218 Regression coefficients Coefficient Std Err t-value p-value Constant 33.796 48.181 0.7014 0.5057 Miles Driven 0.0549 0.0191 2.8666 0.0241 Age of car 21.467 20.573 1.0434 0.3314 Use the information above to estimate the annual maintenance expense for a 10 years old car with 60,000 miles.
A real estate builder wishes to determine how house size (House) is influenced by family income(Income X1), family size (Size X 2), and education of the head of household (School X 3). House size is measured inhundreds of square feet, income is measured in thousands of dollars, and education is in years. The builder randomlyselected 50 families and ran the multiple regression. Microsoft Excel output is provided below: Coefficients Standard Error t Stat P-valueIntercept -1.6335 5.8078 -0.281 0.7798Income 0.4485 0.1137 3.955 0.0003Size 4 .2615 0.8062 5.286 0.0001School -0.6517 0.4319 -1.509 0.1383 What is the predicted house size (in hundreds of square feet) for an individual earning an annual incomeof $40,000, having a family size of 4, and going to school a total of 13 years?A) 11.43 B) 15.15 C) 24.88 D) 53.87
If the regression equation is ỹ =1.3+0.20x, and r =0.81 then the correlation is equal to O -0.09 O -0.90 O none O 0.20 O -0.20 O 0.90

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