Square FeetNumber of BedroomsAgeSelling Price2848424210012701136001825281700223551991002072430750021974.142788002184427530017647107200227614103000Copy DataNPrevStep 2 of 2: Determine if a statistically significant linear relationship exists between the independent and dependent variables at the 0.05 level ofsignificance. If the relationship is statistically significant, identify the multiple regression equation that best fits the data, rounding the answers tothree decimal places. Otherwise, indicate that there is not enough evidence to show that the relationship is statistically significant.E TablesE KeypadAnswerHow to enter your answer (opens in new window)Keyboard ShortcutsSelecting a checkbox will replace the entered answer value(s) with the checkbox value. If the checkbox is not selected, the entered answer is used.X O There is not enough evidence.X2 +© 2021 Hawkes LearningJIS A The following data was collected to explore how the number of square feet in a house, the number of bedrooms, and the age of the house affectthe selling price of the house. The dependent variable is the selling price of the house, the first independent variable (x) is the square footage, thesecond independent variable (x2) is the number of bedrooms, and the third independent variable (x3) is the age of the house.Effects on Selling Price of HousesSquare FeetNumber of BedroomsAge Selling Price28484.6.2421001270113600182548.281700223551991002072430750021974.14278800N2184275300Prev176447107200227614103000Copy DataStep 1 of 2: Find the p-value for the regression equation that fits the given data. Round your answer to four decimal places.E Tables團 KeypadAnswerHow to enter your answer (opens in new window)Keyboard Shortcuts© 2021 Hawkes LearningJIS A

The independent variables are square feet, number of bedrooms, and age. The dependent variable is…

Answered: The following data was collected to…

Elementary Linear Algebra (MindTap Course List)

8th Edition

ISBN:9781305658004

Author:Ron Larson

Publisher:Ron Larson

Chapter2: Matrices

Section2.CR: Review Exercises

Problem 89CR: Cellular Phone Subscribers The table shows the numbers of cellular phone subscribers y in millions...

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Similar questions

Cellular Phone Subscribers The table shows the numbers of cellular phone subscribers y in millions in the United States from 2008 through 2013. Source: CTIA- The Wireless Association Year200820092010201120122013Number,y270286296316326336 (a) Find the least squares regression line for the data. Let x represent the year, with x=8 corresponding to 2008. (b) Use the linear regression capabilities of a graphing utility to find a linear model for the data. How does this model compare with the model obtained in part a? (c) Use the linear model to create a table of estimated values for y. Compare the estimated values with the actual data.
A study of law firms produced the following regression output. The dependent variable is the total amount of fees in millions of dollars. Predictor Coefficient SE Coefficient t p-value Constant 7.443 2.990 2.489 0.010 x1 0.214 0.044 4.864 0.000 x2 -1.143 0.581 -1.967 0.028 x3 -0.159 0.057 -2.789 0.114 x4 0.579 0.314 1.844 0.001 x5 -0.024 0.021 -1.143 0.112 ANOVA Table Source DF SS MS F P-Value Regression 5 1975.76 395.2 9.68 0.000 Residual Error 55 2244.25 40.80 Total 60 4220.01 How large is the sample? Compute the F statistic (round to 2 decimal places) State the Decision Rule for each independent variable. Use the 0.05 significance level. Reject Ho if t<? or t>? (round to 3 decimal places).
The following regression output was obtained from a study of architectural firms. The dependent variable is the total amount of fees in millions of dollars. Predictor Coefficient SE Coefficient t p-value Constant 8.302 3.083 2.693 0.010 x1 0.207 0.155 1.335 0.000 x2 − 1.001 0.556 − 1.800 0.028 x3 − 0.168 0.405 − 0.415 0.114 x4 0.536 0.260 2.062 0.001 x5 − 0.029 0.023 − 1.261 0.112 Analysis of Variance Source DF SS MS F p-value Regression 5 2,064.77 413.0 10.15 0.000 Residual Error 57 2,318.84 40.68 Total 62 4,383.61 x1 is the number of architects employed by the company.x2 is the number of engineers employed by the company.x3 is the number of years involved with health care projects.x4 is the number of states in which the firm operates.x5 is the percent of the firm’s work that is health care−related. a. Write out…
The authors of a paper were interested in how the distance a deer mouse will travel for food is related to the distance from the food to the nearest pile of debris. Distances were measured in meters. The data and computer output are given below. Distance from Debris Distance Traveled 6.94 0.00 5.23 6.13 5.21 11.29 7.10 14.35 8.16 12.03 5.50 22.72 9.19 20.11 9.05 26.16 9.36 30.65 Simple Linear Regression Results: Dependent Variable: Traveled Independent Variable: Debris Sample size: 9 R (correlation coefficient) = 0.5657 R-sq = 0.32002088 Estimate of error standard deviation 8.670711 Parameter estimates: Parameter Estimate Std. Err. Alternative DF T-Stat P-Value Intercept -7.6854587 13.332196 ≠ 0 7 -0.5764586 0.5824 Slope 3.2340908 1.7818117 ≠ 0 7 1.8150575 0.1124 a)What is the least squares regression line for the output given above? b) what is the predicted traveled distance given the distance from debris is 6.5 meters?
It is believed that the annual repair cost for the sporty automobile Jeep is related to its age. A sample of 11 automobiles revealed the results in the table at the right. Car age (xi) 2 3 1 7 5 8 1 2 6 9 4 Repair cost (yi) in £ 72 99 65 138 67 140 83 101 170 121 114 Define the terms regression and correlation analysis . From the simple linear model ,y= a + bx ,determine parameters a and b
The following regression output was obtained from a study of architectural firms. The dependent variable is the total amount of fees in millions of dollars. Predictor Coefficient SE Coefficient t p-value Constant 7.096 3.245 2.187 0.010 x1 0.222 0.117 1.897 0.000 x2 − 1.024 0.562 − 1.822 0.028 x3 − 0.337 0.192 − 1.755 0.114 x4 0.623 0.263 2.369 0.001 x5 − 0.058 0.029 − 2.000 0.112 Analysis of Variance Source DF SS MS F p-value Regression 5 2,009.28 401.9 7.33 0.000 Residual Error 50 2,741.54 54.83 Total 55 4,750.81 x1 is the number of architects employed by the company. x2 is the number of engineers employed by the company. x3 is the number of years involved with health care projects. x4 is the number of states in which the firm operates. x5 is the percent of the firm’s work that is health care−related. Write out…
The following regression output was obtained from a study of architectural firms. The dependent variable is the total amount of fees in millions of dollars. Predictor Coefficient SE Coefficient t p-value Constant 7.096 3.245 2.187 0.010 x1 0.222 0.117 1.897 0.000 x2 − 1.024 0.562 − 1.822 0.028 x3 − 0.337 0.192 − 1.755 0.114 x4 0.623 0.263 2.369 0.001 x5 − 0.058 0.029 − 2.000 0.112 Analysis of Variance Source DF SS MS F p-value Regression 5 2,009.28 401.9 7.33 0.000 Residual Error 50 2,741.54 54.83 Total 55 4,750.81 x1 is the number of architects employed by the company. x2 is the number of engineers employed by the company. x3 is the number of years involved with health care projects. x4 is the number of states in which the firm operates. x5 is the percent of the firm’s work that is health care−related. Write out…
The following regression output was obtained from a study of architectural firms. The dependent variable is the total amount of fees in millions of dollars. Predictor Coefficient SE Coefficient t p-value Constant 7.096 3.245 2.187 0.010 x1 0.222 0.117 1.897 0.000 x2 − 1.024 0.562 − 1.822 0.028 x3 − 0.337 0.192 − 1.755 0.114 x4 0.623 0.263 2.369 0.001 x5 − 0.058 0.029 − 2.000 0.112 Analysis of Variance Source DF SS MS F p-value Regression 5 2,009.28 401.9 7.33 0.000 Residual Error 50 2,741.54 54.83 Total 55 4,750.81 x1 is the number of architects employed by the company. x2 is the number of engineers employed by the company. x3 is the number of years involved with health care projects. x4 is the number of states in which the firm operates. x5 is the percent of the firm’s work that is health care−related. c-1. At the…

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