Suppose a regression model with 654 observations was specified as: y = 0 + ₁1 + B₂₂. A joint hypothesis test ofHo: B₁ = 0, 3₂ = 0 would have _______ denominator degrees of freedom.

It is given that Number of observations n = 654 Number of explanatory variables p = 2

Answered: Suppose a regression model with 654…

MATLAB: An Introduction with Applications

6th Edition

ISBN:9781119256830

Author:Amos Gilat

Publisher:Amos Gilat

Chapter1: Starting With Matlab

Section: Chapter Questions

Problem 1P

See similar textbooks

Similar questions

The following table shows the starting salary and profile of a sample of 10 2 p 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.What is the computed R square of the resulting multiple linear regression and its interpretation? * Civil Years of Starting salary GPA service experience ratings 79.5 15000 80.1 15000 81.2 78.0 15500 81.3 79.0 16000 82.4 80.0 16200 83.4 85.0 17500 87.9 89.9 89.1 18000 90.3 16,300 84.2 17000 87.0 17900 88.1 84.1 89.0 89.2 R squared = 0.8053; This means that 80.53% of the total variation in the starting salary can be explained by its linear relationship with GPA, years of experience and civil service ratings. R squared = 0.9651; This means that 96.51% of the total variation in the starting salary can be explained by its linear relationship with GPA, years of…
In multiple regression analysis involving 10 independent variables and 100 observations, the critical value tt for testing individual coefficients in the model will have:A. 10 degrees of freedomB. 89 degrees of freedomC. 100 degrees of freedomD. 9 degrees of freedom In a multiple regression analysis involving 40 observations and 5 independent variables, the total variation SST=350 and SSE=50. The multiple coefficient of determination is:A. 0.8469B. 0.8529C. 0.8408D. 0.8571
In the multiple regression model with a quarterback's salary the response variable Y and the X variables pass completion percentage (PCT), number of touchdowns (TD), and age; the test of joint significance rejected HO: beta1 = beta2 = beta3 = 0. What does that mean? O Most of these X variables are significant in explaining salary. O At least one of these X variables is significant in explaining salary. O Each of these X variables is significant in explaining salary. O The model with all three X variables is significantly better than using sample mean salary alone to estimate expected salary
12 young batsmen practiced batting at the nets for varying periods of time, and their dot ball percentage was calculated at the end of the month: a) Find the relationship between dot ball percentage and practice time per month using a scatter diagram and interpret. b) Find correlation coefficient and comment. c) Fit a least square regression equation (line) of dot ball percentage on practice time per month and comment. d) What will be the dot ball percentage when practice time per month is 32hr? e) Comment on the regression equation and explore how well it fits.
A study was conducted on 64 female college athletes. The researcher collected data on a number of variables including percent body fat, total body weight, height, and age of athlete. The researcher wondered if % body fat (%BF), height (HGT), and/or age are significant predictors of total body weight. All conditions have been checked and are met and no transformations were needed. The technology output from the multiple regression analysis is given below. Interpret the coefficient of % body fat
The following is the result of the multiple linear regression analysis in STATISTICA, where the response Y = lung capacity of a person, xage = age of the person in years, xheight = height of the person in inches, = a categorical variable with 2 levels (0 = non- X smoke smoker, 1 = smoker), and xCaesarean = a categorical variable with 2 levels (0 = normal delivery, 1 = %3D %3D Caesarean-section delivery). b* Std.Err. Std.Err. t(720) p-value N=725 Intercept Age Height Smoke Caesarean of b 0.467772 0.017626 of b* -11.8001 0.1372 0.2790 -0.6407 -25.2263 7.7846 28.6552 -5.0142 0.000000 0.000000 0.000000 0.206427 0.026517 0.026340 0.754765 -0.074205 -0.033054 0.009735 0. 127774 0.092146 0.000001 0.022851 0.014799 0.014492 -0.2102 -2.2808 What is the predicted lung capacity of an 14-year old non-smoker whose height is 71 inches born by normal delivery? (final answer to 4 decimal places)
Suppose we have fit a multiple linear regression with 8 explanatory variables and an intercept with 85 observations. We want to test the joint significance of the first 5 explanatory variables using an F test. Please fill in the blanks for the numerator and denominator degrees of freedom of the F statistic of the test: "The F statistic is F(
Using 25 observations on each variable, a computer program generated the following multiple regression model: yhat=69.2+2.87x1+5.81x21.83x3 If the standard errors of the coefficients of the independent variables are, respectively, 1.34, 4.84, and 0.70, can you conclude that the independent variable x1 is needed in the regression model? Let β1, β2, and β3 denote the coefficients of the 3 variables in this model, and use a two-sided hypothesis test and significance level of 0.05 to determine your answer. Carry your intermediate computations to at least three decimal places and round your answers as specified in the table. The null hypothesis: H0: The alternative hypothesis: H1: The type of test statistic: (Choose one)ZtChi squareF The value of the test statistic:(Round to at least two decimal places.) The two critical values at the 0.05 level of significance:(Round to at least two decimal places.) and Can you…
An automotive engineer computed a least-squares regression line for predicting the gas mileage (mpg) of a certain vehicle from its speed in mph. The results are presented in the following Excel output: What is the regression equation? Intercept Speed R-Sq Coefficients 40.69 -0.22 0.588. Og = 40.69 0.22X Oy = 40.69 0.588X Oŷ = 0.22 + 40.69X Oy = 0.588 0.22X
Suppose the following data were collected from a sample of 1515 CEOs relating annual salary to years of experience and the economic sector their company belongs to. Use statistical software to find the following regression equation: SALARYi=b0+b1EXPERIENCEi+b2SERVICEi+b3INDUSTRIALi+eiSALARY�=�0+�1EXPERIENCE�+�2SERVICE�+�3INDUSTRIAL�+��. Is there enough evidence to support the claim that on average, CEOs in the service sector have lower salaries than CEOs in the financial sector at the 0.010.01 level of significance? If yes, write the regression equation in the spaces provided with answers rounded to two decimal places. Else, select "There is not enough evidence." Copy Data CEO Salaries Salary Experience Service (1 if service sector, 0 otherwise) Industrial (1 if industrial sector, 0 otherwise) Financial (1 if financial sector, 0 otherwise) 144225144225 1010 11 00 00 187765187765 2020 00 00 11 142500142500 66 11 00 00 169650169650 2828 11 00 00 167250167250 3131 00…
A linear regression model based on a random sample of 36 observations on the response variable and 4 predictors has a multiple coefficient of determination equal to 0.697. What is the value of the adjusted multiple coefficient of determination?
Suppose the following regression equation was generated from the sample data of 50 cities relating number of cigarette packs sold per 1000 residents in one week to tax in dollars on one pack of cigarettes and if smoking is allowed in bars: PACKS, 58803.462982-1005.438507TAX, +284.030008BARS, + BARS, 1 if city / allows smoking in bars and BARS,= 0 if city i does not allow smoking in bars. This equation has an R² value of 0.305162, and the coefficient of BARS, has a value of 0,088136. Which of the following conclusions is valid? Answer Keypad Keyboard Shortcuts m Tables O If there is no cigarette tax in a city that allows smoking in bars, the approximate number of cigarette packs sold per 1000 people is 58803. O According to the regression equation, cities that allow smoking in bars have lower cigarette sales than cities that do not allow smoking in bars. O More than half of the variation in cigarette sales is explained by cigarette taxes and whether or not a city allows smoking in bars.…

SEE MORE QUESTIONS

Recommended textbooks for you

MATLAB: An Introduction with Applications

Statistics

ISBN:

9781119256830

Author:

Amos Gilat

Publisher:

John Wiley & Sons Inc

Probability and Statistics for Engineering and th…

Statistics

ISBN:

9781305251809

Author:

Jay L. Devore

Publisher:

Cengage Learning

Statistics for The Behavioral Sciences (MindTap C…

Statistics

ISBN:

9781305504912

Author:

Frederick J Gravetter, Larry B. Wallnau

Publisher:

Cengage Learning

MATLAB: An Introduction with Applications

Statistics

ISBN:

9781119256830

Author:

Amos Gilat

Publisher:

John Wiley & Sons Inc

Probability and Statistics for Engineering and th…

Statistics

ISBN:

9781305251809

Author:

Jay L. Devore

Publisher:

Cengage Learning

Statistics for The Behavioral Sciences (MindTap C…

Statistics

ISBN:

9781305504912

Author:

Frederick J Gravetter, Larry B. Wallnau

Publisher:

Cengage Learning

Elementary Statistics: Picturing the World (7th E…

Statistics

ISBN:

9780134683416

Author:

Ron Larson, Betsy Farber

Publisher:

PEARSON

The Basic Practice of Statistics

Statistics

ISBN:

9781319042578

Author:

David S. Moore, William I. Notz, Michael A. Fligner

Publisher:

W. H. Freeman

Introduction to the Practice of Statistics

Statistics

ISBN:

9781319013387

Author:

David S. Moore, George P. McCabe, Bruce A. Craig

Publisher:

W. H. Freeman

SEE MORE TEXTBOOKS

Suppose a regression model with 654 observations was specified as: ŷ =B₁ + B₁x₁ + B₂2. A joint hypothesis test of Ho B₁0, B2=0 would have denominator degrees of freedom.