We take a random sample of houses currently for sale in Rexburg and we want to use the squarefootage of a house to predict it's corresponding sale price. After regression, we find the regressionequation to be: Y- 80000 + 160 X. Use this information to answer all parts. Part 1: Which is the explanatory variable?O All houses currently for sale in RexburgO All houses in our sample of houses currently for sale in RexburgO Square footage of a houseO Price of a housePart 2: What would be the response variable?O All houses currently for sale in RexburgO All houses in our sample of houses currently for sale in RexburgO Square footage of a houseO Price of housePart 3: Based on the material taught in this course, which of the followingis the most apportehypothesis to determine if the square footage of a house has a statistically signifcant ler oiwith the price of the house?O Ho : Bo = 0O Ho : B + 43O Ho : Bi 0O He : B = 43O Ho : Bi =0Part 4: Based on the data, the agency decides to reject their null hypothesis. How should they statetheir conclusion?O There is insufficient evidence to say that there is a linear relationship between the square footage and the saleprice of the house.O There is sufficient evidence to say that true slope of the regression line is not 0.01.O There is sufficient evidence to say that there is a linear relationship between the square footage and the saleprice of a house.There is sufficient evidence to say that there is NO linear relationship between the square footage and the salenrice of a house.Part 5: Predict the sale price of a house that has a size of 1700 square feet.

Answered: Image /qna-images/answer/4c7c4b3e-c618-48e9-8de1-6a74a098b89e.jpg

Answered: We take a random sample of houses…

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

Part a: Make a scatter plot and determine which type of model best fits the data.Part b: Find the regression equation.Part c: Use the equation from Part b to determine y when x = 7.
The table gives the amount of money (in dollars) spent on football by a major university. Letting x represent the number of years since 2006, and letting y represent the amount of money spent on football, in thousands of dollars, use the regression capabilities of a graphing calculator to find the equation of the line of best fit. Round values off to the nearest hundredth. Then, use your equation to make the following predictions. Year 2006 Dollars spent on football 173,000 The equation of the line of best fit is: Hint 2009 2010 2011 2007 2008 19 000 210,000 223,000 243,000 271,000 Predict the amount of money that will be spent on football in the year 2022. Predict the amount of money that will be spent on football in the year 2041.
We would like to study the relationship between the number of family members in a household a and number of meals they eat together y. A regression equation based on a sample from the study is shown below. ŷ = = 1.47 – 0.08x Estimate the number of meals shared by a family with 6 members. Select one: O a. 3 O b. 0 O c. 1 O d. 2
1. Develop a simple linear regression equation for starting salaries using an independent variable that has the closest relationship with the salaries. Explain how you chose this variable.
Please solve by hand as I need to understand the steps. Please don’t use excel
A regression analysis was performed to determine if there is a relationship between hours of TV watched per day (2) and number of sit ups a person can do (y). The results of the regression were: y=ax+b a=-0.666 b=21.511 72=0.543169 r=-0.737 Use this to predict the number of sit ups a person who watches 7 hours of TV can do, and please round your answer to a whole number. Hìnt: Help Question Help: Message instructor Submit Question 围 hp Ce %23 & 3. 4. 6. 8. W e y 61
The table shows the total square footage (in billions) of retailing space at shopping centers and their sales (in billions of dollars) for 10 years. The equation of the regression line is y = 674.071x- 2582.997. Complete parts a and b. Total Square Footage, x Sales, y 5.1 5.2 5.3 5.4 5.5 5.6 5.7 5.9 5.9 6.1 852.7 927.6 978.6 1057.5 1104.2 1221.6 1277.9 1325.5 1439.3 1530.9 (a) Find the coefficient of determination and interpret the result. (Round to three decimal places as needed.) How can the coefficient of determination be interpreted? O A. The coefficient of determination is the fraction of the variation in sales that is unexplained and is due to other factors or sampling error. The remaining fraction of the variation explained by the variation in total square footage. O B. The coefficient of determination is the fraction of the variation in sales that can be explained by the variation in total square footage. The remaining fraction of the variation is unexplained and is due to…
The linear regression equation for the data from the number 2 is y=.27x+5.82. Describe the slope of the linear best-fit equations.
This question has do with linear regression.If I consider attendance in a class versus final grade in the class can you explain where there is a positive and when there is a negative relationship between them.
A real estate company wanted to see the relationship between home prices and square footage of homes for sale. The regression line is ŷ = –802,456 + 144x, where x is the square footage of a home. Complete the statement based on the information. For every -square-foot increase, the predicted home price is predicted to increase by $
Refer to the data set:(-1, 2), (1, 3), (1, 5), (2, 7), (3, 8), (4, 11)Part a: Make a scatter plot and determine which type of model best fits the data.Part b: Find the regression equation.Part c: Use the equation from Part b to determine y when x = 10
A sports-equipment researcher was interested in the relationship between the speed of a golf club (in feet per second) and the distance a golf ball travels (in yards). Information was collected on several golfers and was used to obtain the regression equation ŷ = 2x - 106, where x represents the club speed and ŷ is the predicted distance. Which statement best describes the meaning of the slope of the regression line? For each increase in distance by 1 yard, the predicted club speed increases by 2 ft/sec. For each increase in distance by 1 yard, the predicted club speed decreases by 106 ft/sec. For each increase in club speed by 1 ft/sec, the predicted distance increases by 2 yards. For each increase in club speed by 1 ft/sec, the predicted distance decreases by 106 yards.

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

We take a random sample of houses currently for sale in Rexburg and we want to use the sqare footage of a house to predict it's corresponding sale price. Alter regression, we find the regression equation to be: Y- 80000 + 160 X. Use this information to answer all parts