16.2 PracticeMSEWhat is the p-value?less than 0.01What is the value of the F test statistic (to 2 decimals)? Use Table 4 in Appendix B.(toUsing a = 0.05, what is your conclusion?Conclude the relationship is significantSuppose variables ₁ and 4 are dropped from the model and the following estimated regression equation is obtained.ŷ 11.1 3.6x2 +8.1x3=2For this model SST = 1805 and SSR = 1,705.b. Compute SSE(x₁, x2, x3, x4).c. Compute SSE(x2, x3).What is the p-value?less than 0.01decimals, if necessary)(to 1 decimal, if necessary)(to 1 decimal, if necessary)d. Use an F test and a = 0.05 to determine whether ₁ and 4 contribute significantly to the model.What is the value of the F test statistic (to 2 decimals)? Use Table 4 in Appendix B.Hide FeedbackWhat is your conclusion about the two variables 1 and 4?Conclude that these variables contribute significantly to the modelPartially Correct+Hint(s)Check My WorkX 16.2 Practice2.In a regression analysis involving 30 observations, the following estimated regression equation was obtained.ŷ 17.6 +3.8x₁ − 2.3x2 + 7.6x3 +2.7x4=For this estimated regression equation SST 1805 and SSR = 1748.a. At a = 0.05, test the significance of the relashionship among the variables.SSEMSRMSE57437What is the p-value?Using a =less than 0.01What is the value of the F test statistic (to 2 decimals)? Use Table 4 in Appendix B.(to 1 decimal, if necessary)c. Compute(to 1 decimal, if necessary)(to decimals, if necessary)20.05, what is your conclusion?Conclude the relationship is significantSuppose variables 1 and 4 are dropped from the model and the following estimated regression equation is obtained.ŷ 11.1 3.6x2 +8.1x3For this model SST = 1805 and SSR = 1,705.b. Compute SSE(x1, x2, x3, x4).(to 1 decimal, if necessary)SSE(x2,What is the p-value?x3).(to 1 decimal, if necessary)+d. Use an F test and a = 0.05 to determine whether 1 and 4 contribute significantly to the model.What is the value of the F test statistic (to 2 decimals)? Use Table 4 in Appendix B.111ιπ,псса мy wuinX

is given that, Regression equation is,a. We know that,F-statistics is,Decision: , we reject…

Answered: at is the value of the F test statistic…

College Algebra

1st Edition

ISBN:9781938168383

Author:Jay Abramson

Publisher:Jay Abramson

Chapter4: Linear Functions

Section: Chapter Questions

Problem 30PT: For the following exercises, use Table 4 which shows the percent of unemployed persons 25 years or...

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Find the equation of the regression line for the following data set. x 1 2 3 y 0 3 4
For the following exercises, use Table 4 which shows the percent of unemployed persons 25 years or older who are college graduates in a particular city, by year. Based on the set of data given in Table 5, calculate the regression line using a calculator or other technology tool, and determine the correlation coefficient. Round to three decimal places of accuracy
The estimated regression equation for a model involving two independent variables and 10 observations follows. Y=25.7067 + 0.2795x1 + 0.7337x2 A. Interpret b1 and b2 in this estimated trgression equation. B1 = ? B2 = ? Thank you
A regression was run to determine if there is a relationship between hours of TV watched per day (xx) and number of situps a person can do (yy).The results of the regression were:y=ax+b a=-0.629 b=39.045 r2=0.806404 r=-0.898 Use this to predict the number of situps a person who watches 6.5 hour(s) of TV can do, and please round your answer to a whole number.
A regression was run to determine if there is a relationship between hours of TV watched per day (x) and number of situps a person can do (y).The results of the regression were: y=ax+b a=-1.383 b=24.599 r2=0.962361 r=-0.981 Use this to predict the number of situps a person who watches 5.5 hours of TV can do (to one decimal place)
In a regression analysis involving 27 observations,the following estimated regression equation was developed. y=30.6+8x1. For this estimated regression equation SST=1000 and SSE=500. test whether x1 is significant.(Fa=4.24) Suppose that variables x2 and x3 are added to the model and the following regression equation is obtained.y=21+3.6x1+15.7x2-9.3x3.For estimated regression equation SST=1000 and this SSE=80. (2)Use F test to determine whether x2 and x3 contribute significantly to the model.(Fa=3.42)
Consider the following multiple regression model : y hat = -6.72 + 0.51x1 - 0.69x2 + 1.452x3 a. What is the mean value of y when x1 = 9, x2 = 4, and x3 = 7? b. write a sentence interpreting the value of ℬ2 hat = -0.69. c. Would the mean value of y be larger or smaller if x2 wereincreased, but x1 and x3 were held constant? Explain in a few sentences. d. If there were an interaction between x2 and x3, what additional predicator variable should be added to the model?
A regression was run to determine if there is a relationship between hours of TV watched per day (x) and number of situps a person can do (y). The results of the regression were: y=ax+b a=-0.926 b=28.863 r²=0.470596 r=-0.686 Use this to predict the number of situps a person who watches 10.5 hours of TV can do (to one decimal place)
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.
The following results were obtained from a simple linear regression analysis. Total sum of square = 5.7640. Unexplained sum of square = 0,2225. The coefficient of determination is: OA 0.0386 O B, 0.0402 OC0.9614 O D.0.9805
The following estimated regression equation based on 10 observations was presented. ŷ = 27.1270 +0.5104x₁ + 0.4980x₂ 1 Here, SST = 6,736.125, SSR = 6,222.375, 5b₁ = 0.0813, and = 0.0567. 562
A regression was run to determine if there is a relationship between hours of TV watched per day (x) and number of situps a person can do (y).The results of the regression were:y=a+bx b=-0.736 a=32.667 r2=0.576081 r=-0.759 Use this to predict the number of situps a person who watches 6 hours of TV can do.

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