If the p-value of F tests in the Excel linear regression outputis 0.7, then there is no statistical evidence to suggest that:O a. all individual regression coefficients are zero.O b. one or more independent variables are associatedwith the dependent variable.O c. all individual regression coefficients are zero; one ormore independent variables are associated with thedependent variable.O d. one or more regression coefficients are zero.O e.one or more regression coefficients are notzero; one or more independent variables areassociated with the dependent variable.

The p-value of F tests in the Excel linear regression output is 0.7.

Answered: If the p-value of F tests in the Excel…

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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If a p-value reported in the Excel linear regression output associated with a particular variable is 0.007, the confidence interval for the related regression coefficient includes the zero value at: Oa. both the 90% and 95% confidence levels. O b. none of the answers provided Oc. the 95% confidence level. Od. the 99% confidence level. Oe. the 90% confidence level. Clear my choice
6. One of the most important uses of a regression line is; a. to determine if any x-values are outliers. b. To know the relation between the random variables. c. to determine if any y-values are outliers. d. to estimate the unknown parameters. e. to estimate the change in y for a one-unit change in x. 7. The estimated simple linear regression model represents: a. the strength of the relationship between x and y b. the expected x value when y is zero c. the expected y value when x is zero d. a population parameter. e. None of the above. 8. A chi-square test involves a set of frequencies called observed frequency and expected frequency. What are the observed frequencies? a. Hypothetical frequencies that would occur of the alternative hypothesis were true. b. Hypothetical frequencies that would occur if the null hypothesis were true. c. The actual frequencies that did occur in the given experiment. d. The long-run counts that would be expected if the observed counts are representative. e.…
1. What is the greatest concern about the regression below? a) It has a small slope. b) It has a high . c) The investigator should not be using a linear regression on these data. d) The residuals are too large. e) The regression line does not pass through the origin. 2. The scatterplots below display three bivariate data sets. The correlation coefficients for these data sets are 0.03, 0.68, and 0.89. Which scatter plot corresponds to the data set with = 0.03? a) Plot 1 b) Plot 2 c) Plot 3
The U.S. Department of Energy’s Fuel Economy Guide provides fuel efficiency data for cars and trucks. The following regression output was obtained for a sample of 45 cars. The variable of interest is highway miles per gallon (Hwy MPG). The independent variables used in the analysis are as follows: The class of the vehicle: Compact, Midsize or Large. Midsize = 1 if the car is a midsize, 0 otherwise. Similarly, Large = 1 if it is a large car, 0 otherwise. Displcement: The engine displacement (size) in liters Premium: Equals 1 if premium fuel is used, 0 if regular fuel is used Cylinders: Number of cylinders Regression Statistics Multiple R 0.90 R Square Adjusted R Square 0.79 Standard Error 1.78 Observations 45 ANOVA df SS MS F Significance F…
Assume that you have collected a sample of observations from over 100 households and their consumption and income patterns. Using these observations, you estimate the following regression C₁ = Bo + B₁Y₁+H₁, where C is consumption and Y is disposable income. The estimate of B₁ will tell you: AIncome O A. APredicted Consumption OB. APredicted Consumption AIncome OC. Predicted Consumption Income OD. The amount you need to consume to survive.
x1 x2 x3 x4 x5 x6 bond width on bond pull strength loop height wire post height observation die height width on length the die the post 8.0 5.2 17.0 28.6 83.0 1.9 1.6 2. 8.0 5.2 19.6 29.6 94.9 2.1 2,3 3 8.3 5.8 19.8 32.4 89.7 2.1 1.8 4. 8.5 6.4 19.6 31.0 96.2 2.0 2.0 8.8 5.8 19.4 32.4 95.6 2.2 2.1 6 9.0 5.2 18.6 28.6 86.5 2.0 1.8 7 9.3 5.6 18.8 30.6 84.5 2.1 2.1 9.3 6.0 20.4 32.4 88.8 2.2 1.9 9.5 5.2 19.0 32.6 85.7 2.1 1.9 10 9.8 5.8 20.8 32.2 93.6 2.3 2.1 11 10.0 6.4 19.9 31.8 86.0 2.1 1.8 12 10.3 б.0 18.0 32.6 87.1 2.0 1.6 13 10.5 6.2 20.6 33.4 93.1 2.1 2.1 14 10.8 6.2 20.2 31.8 83.4 2.2 2.1 15 11.0 6.2 20.2 32.4 94.5 2.1 1.9 16 11.3 5.6 19.2 31.4 83.4 1.9 1.8 17 11.5 6.0 17.0 33.2 85.2 2.1 2.1 18 11.8 5.8 19.8 35.4 84.1 2.0 1.8 19 12.0 6.5 19.4 32.2 88.5 2.1 1.9 20 12.3 5.6 18.8 34.0 86.9 2.1 1.8 21 12.5 18.6 18.6 34.2 83.0 1.9 2.0 22 12.5 18.6 20.8 35.4 96.2 2.3 2.3 From the sample data above, complete the following steps.
The OLS estimator is derived by A. making sure that the standard error of the regression equals the standard error of the coefficient estimator. B. minimizing the sum of squared regression. C. minimizing the sum of squared error. D. connecting the Y; corresponding to the lowest X; observation with the Y; corresponding to the highest X; observation. Decat Celoction O O
A researcher would like to predict the dependent variable Y from the two independent variables X1 and X2 for a sample of N=15 subjects. Use multiple linear regression to calculate the coefficient of multiple determination and test the significance of the overall regression model. Use a significance level α=0.01. X1X1 X2X2 YY 44.5 64.2 39.3 27.4 58.2 18.8 37.6 74 40.2 40.5 12.1 73.3 33.4 60.8 15.5 0.3 21.7 27.4 91.4 57 69.8 56.4 58.9 98.4 30.8 46.8 40 54 83.2 31.5 31.6 66.2 29.8 37.5 57.1 46.7 55.3 38.7 99.8 66 61.2 84.1 53.7 37.9 45.9 SSreg=SSres=R2=F= P-value =
A researcher would like to predict the dependent variable Y from the two independent variables X1 and X2 for a sample of N = 12 subjects. Use multiple linear regression to calculate the coefficient of multiple determination and test statistics to assess the significance of the regression model and partial slopes. Use a significance level a = 0.01. X1 X2 53.2 50.8 61.2 91.9 84.6 66.8 62 57.8 61.9 46.4 43 70.1 59.1 65.5 56.5 72.6 61.7 62.7 69.5 70.4 63.7 70.9 70.6 64.9 68.6 61.5 61.1 54.7 57 49 34.6 43.9 45 67.2 This data set can be downloaded as a *.csv file: Download CSV. 41.6 44.4 R F = P-value for overall model = ti = for b1, P-value = tą = for b2, P-value = What is your conclusion for the overall regression model (also called the omnibus test)? O The overall regression model is statistically significant at a = 0.01. O The overall regression model is not statistically significant at a = 0.01. Which of the regression coefficients are statistically different from zero? O neither…
Suppose x1 ans x2 are predictor variables for a response variable y. a. The distribution of all possible values of the response variable corresponding to particular values of the two predictor variables is called a distribution of the response variable. b. State the four assumptions for multiple linear regression inferences.
. Interpret your result from part (a) if the assumptions for regression inferences hold. Choose the correct interpretation below. A. Presuming that the variables age and price for Corvettes satisfy the assumptions for regression inferences, the standard error of the estimate provides an estimate for the common population standard deviation, σ, of ages for all Corvettes of any particular price. B. Presuming that the variables age and price for Corvettes satisfy the assumptions for regression inferences, the standard error of the estimate provides an estimate for the slope, β1, of the population regression equation for ages for all Corvettes of any particular price. C. Presuming that the variables age and price for Corvettes satisfy the assumptions for regression inferences, the standard error of the estimate provides an estimate for the common population standard deviation, σ, of prices for all Corvettes of any particular age. D. Presuming that…
12. measures the strength of the relationship between the dependent and independent variables. 13. One way to examine whether two variables might be linearly related is to construct a 14. The assumption of equal standard deviations about the regression line is called

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