One of the biggest factors in determining the value of a home is the square footage. The accompanying data represent the square footage and selling price (in thousands of dollars) for a random sample of homes for sale in a certain region.Complete parts (a) through (h) below.Click the icon to view the housing data.(d) Is there a linear relation between square footage and asking price?O NoYes(e) Find the least-squares regression line treating square footage as the explanatory variable.y = 0.16 x++(15.65)(Round to two decimal places as needed.)(f) Interpret the slope. Select the correct choice below and, if necessary, fill in the answer box to complete your choice.OA. For every additional square foot, the selling price increases bythousand dollars, on average.(Round to two decimal places as needed.)OB. For every additional thousand dollars in selling price, the square footage increases by(Round to two decimal places as needed.)OC. For a house that is sold for $0, the predicted square footage is(Round to two decimal places as needed.)OD. For a house that is 0 square feet, the predicted selling price is thousand dollars.(Round to two decimal places as needed.)OE. It is not appropriate to interpret the slope.square feet, on average. 12:38Data TableSquare Footage, x214832311177198830642735414622152625162917733887KOne of the biggest factors in determining the value of a home is the square footage. The accompanying datarepresent the square footage and selling price (in thousands of dollars) for a random sample of homes for sale in acertain region. Complete parts (a) through (h) below.Click the icon to view the housing data.|||=PrintSelling Price ($000s), y371.1382.2197.6339.9614.9364.7632.7377.7427.5285.9268.9701.4DoneOVo) 10ا... 46 ... 248%X

Given is the data on the selling price and area (square feet) of houses. We need to predict the…

Answered: (d) Is there a linear relation between…

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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Given the data points listed below, use linear regression to find the expression y = %3D mx + b. List the slope of the best-fit line in the box below (round to one digit past the decimal point). y 2 2 3 15 5 14 8 55 |13 68 15 121
The data show the number of viewers for television stars with certain salaries. Find the regression equation, letting salary be the independent (x) variable. Find the best predicted number of viewers for a television star with a salary of $7 million. Is the result close to the actual number of viewers, 5.2 million? Use a significance level of 0.05. Salary (millions of $) 108 14 5 8 5.1 4.3 7.8 2.5 Viewers (millions) Click the icon to view the critical values of the Pearson correlation coefficient r. What is the regression equation? y=+x (Round to three decimal places as needed.) X 1 1 5 8 5.7 8.2 10.9 4.4
Match the coefficient of determination to the scatter diagram. The scales on the x-axis and y-axis are the same for each scatter diagram (a) R2 0.90, (b) R2 0.58, (c) R2 =1 (a) Scatter diagram Explanatory (b) Scatter diagram (c) Scatter diagram Explanatory III Explanatory
The arm span and foot length were both measured (in centimeters) for each of 20 students in a biology class. The computer output displays the regression analysis. Which of the following is the best interpretation of the coefficient of determination r2? About 37% of the variation in arm span is accounted for by the linear relationship formed with the foot length. About 65% of the variation in foot length is accounted for by the linear relationship formed with the arm span. About 63% of the variation in arm span is accounted for by the linear relationship formed with the foot length. About 63% of the variation in foot length is accounted for by the linear relationship formed with the arm span.
We run the following regression using a sample of 148 women living in the western states. The variable wage is the hourly wage ( in USD) and educ is the number of years of education completed. wage = -3.07 + 1.52 educ Which statement best described the estimated slope coefficient? Group of answer choices A 1.52 extra years of education is predicted to increase wage by 1 USD An extra year of education is predicted to increase hourly wage by 1.52 USD A 1 percent increase in education is predicted to increase wage by 1.52 percent A 1.52 percent increase in education is predicted to increase wage by 1
B-4 A toy store manager would like to know how the price can affect the sales of toys. In his data, he obtained 100 pairs of prices (unit: £) and volume of sales (unit: hundreds) for the toys. A simple linear regression was run with Sales as the dependent variable and Price as the independent variable with the following results. Regression Statistics R Square Adj. RSqr StErr of EST 0.876 0.767 0.701 4.184 Summary Table Variable Coeff. Std. Err Intercept 64.742 11.310 Price -2.645 0.602 (a) Write down the estimated regression equation. Explain this regression equation. (b) What's the value for R Square? Explain the meaning of this value. (c) Test the significance of the model at a=0.05 level. Write down the hypothesis you are testing, the statistic you use to evaluate the test, and your conclusion. (d) Use the regression output to construct a 99% confidence interval for the predicted sales when the price is set to be 15. (e) A colleague in the toy store claims that £1 increase in the…
Fill in the blanks. a. Multicollinearity is considered to be severe if the VIF for one or more predictor variables is ______. or greater. b. If the coefficient of multiple determination for the regression of the predictor variable x11 on all the other predictor variables in a regression equation is 0.6, then the VIF for x1 is ______. c. The effect of multicollinearity in a polynomial regression analysis can be reduced by ______. the predictor variable.
Fill in the blank. Given a collection of paired sample data, the Given a collection of paired sample data, the y=bo+b,x algebraically describes the relationship between the two variables, x and y. y=bo+b₁x algebraically describes the relationship between the two variables, x and y. intercept of probability equation slope of regression equation
Consider the following table, find the value of the INTERCEPT (b) of the regression line. X Values Y Values 23 8 22 25 4 24 7 21
A grocery store is interested in how customers' income affects the amount of money they spend each week. A regression analysis is performed to test this analysis. The results of the analysis are that the intercept is $17.80 and the slope is 0.0012. Based on these results, what is the predicted weekly grocery expenditure (to the nearest cent) for a household with an income of $65,000? $95.80 O$78.00 $45.23 $17.80 Ho: mean weekly expenditure = $78.00
Q: Use the dataset posted below to find a multiple regression model to predict the salary as a function of all five of the adjusted cost of living rates. (Solve in Excel) Interpret the slope coefficient for the Housing rate in context. Interpret the adjusted R2 value and the standard error of the estimate value in context. Cost of Living Adjustments City Comparative Salary Groceries Housing Utilities Transportation Healthcare Atlanta $60,482.00 15% 25% -10% 6% 5% Austin $57,530.00 -8% 13% -1% -1% 6% Boston $85,904.00 16% 141% 43% 11% 33% Charleston $60,904.00 18% 22% 11% -7% 10% Charlotte $58,012.00 11% 5% 2% -2% 6% Chicago $70,000.00 23% 73% 1% 18% 2% Columbus $54,578.00 1% -1% -5% -6% -1% Dallas $58,072.00 12% -4% -3% 1% 5% DC $87,892.00 20% 214% -6% 13% -3% Denver $65,843.00 7% 64% -8% 1% 11% Detroit $57,590.00 -2% 15% 0% 6% -1% Indianapolis $55,120.00 3% 4% -11% -6% 1% Los Angeles $83,795.00 16% 168% 10% 29% 13%…
2. Participants were kept awake for a certain number of hours before given a visuospatial task. Researchers measured how many correct responses each participant had. Results are shown below. Use alpha = .01. Number of Correct Hours Kept Awake (X) Responses (Y) 21 X = 10 SS, = 422 Y = 10 SS, = 690 2 4 19 %3D 6. 13 SPxy =-525 ху 5 20 S, =5.70 S, = 7.29 9. 11 10 9. 14 5 15 5 17 2 18 1 17 1 13 4 18 8 11 A. Graph the data. B. State the hypotheses. C. Make a decision about the null. a. Calculate Pearson's r i. Decision about null hypothesis? b. Calculate effect size i. Interpret effect size. D. State your conclusion. E. Relate your conclusion to the research. F. Calculate the regression formula. G. If someone was kept awake for 9 hours, what is the predicted number of correct responses?

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