The output below shows a regression between the square footage and asking price (in thousands of dollars)for nine homes for sale in Orange County, California in February 2010.1800160014001200SUMMARY OUTPUT1000Regression Statistics800Multiple R0.908689165600R Square0.825715999400Adjusted R Square0.800818284Standard Error166.9198439200Observations100020003000400050006000ANOVAdfSignificance F924032.3089 924032.3 33.16433 0.000692097MSRegression1Residual7195035.64 27862.23Total81119067.949Coefficients Standard Errort StatP-valueLower 95% Upper 95% Lower 95.0% Upper 95.0%Intercept-48.51516784133.4610236 -0.363520.72695 -364.1003409 267.070005 -364.1003409 267.0700052Sqft0.2819225410.048954677 5.758848 0.000692 0.166163124 0.397681960.166163124 0.397681958What is the regression equation?y =48.52x + 0.28- 48.52 + 0.28xy =48.52 + 0.28x48.52x +0.28Interpret the y –intercept of the line.On average, when x =0, a house has 282 square feet.On average, each increase in 1 square foot of a house increases its asking price by $282. Onrage, when x = U, a house costs –$48, 515.On average, each increase in 1 square foot of a house decreases its asking price by $48, 515.We should not interpret the y –intercept in this problem.We should interpret the y – intercept, but none of the above are correct.Give a practical interpretation of the coefficient of determination.82.57% of the sample variation in home asking price can be explained by the least-squares regressionline.We can predict the home asking price correctly 90.87% of the time using square footage in a least-squares regression line.90.87% of the sample variation in home asking price can be explained by the least-squares regressionline.We can predict the home asking price correctly 82.57% of the time using square footage in a least-squares regression line.90.87% of the differences in home asking price are caused by differences in square footage.82.57% of the differences in home asking price are caused by differences in square footage.Is it reasonable to use the regression equation to make a prediction for a 550 square foot house? Justifyyour answer.No, r does not indicate that there is a reasonable amount of correlation.No, this prediction is far outside the scope of available data.No, the regression line does not fit the points reasonably well.Yes, all of the criteria are met.

The independent variable is Square feet. The dependent variable is asking price. This is simple…

Answered: The output below shows a regression…

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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Use the following ANOVA table for regression to answer the questions. Analysis of Variance Source DF SS MS F P Regression 1 3388.1 3388.1 18.5 0.000 Residual Error 174 31859.4 183.1 Total 175 35247.5 Give the F-statistic and p-value.Enter the exact answers.The F-statistic is .The p-value is . SHOW HINT LINK TO TEXT Choose the conclusion of this test using a 5% significance level. Reject H0. The model is not effective. Do not reject H0. We did not find evidence that the model is effective. Reject H0. The model is effective. Do not reject H0. We did not find evidence that the model is not effective
Consider the ANOVA table that follows. Analysis of Variance Source DF SS MS F Regression 2 3,947.18 1,973.59 42.48 Residual Error 47 2,183.37 46.45 Total 49 6,130.55 a-1. Determine the standard error of estimat About 95% of the residuals will be between what two values? (Round your answers to 2 decimal places. Determine the coefficient of multiple determination. (Round your answer to 3 decimal places.) Determine the percentage variation for the independent variables. (Round your answer to 1 decimal place.) Determine the coefficient of multiple determination, adjusted for the degrees of freedom. (Round your answer to 3 decimal places.)
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. You can calculate the P-value for a chi-square test using technology. After calculating the standardized test statistic, use the cumulative distribution function (CDF) to calculate the area under the curve. Use the P-value method to test the claim. A hospital spokesperson claims that the standard deviation of the waiting times experienced by patients in its minor emergency department is no more than 0.5 minutes. A random sample of 21 waiting times has a standard deviation of 0.7 minutes. At α = 0.01, is there enough evidence to reject the spokesperson's claim? Identify the null and alternative hypotheses. OA. Ho: 0.5 Hos0.5 Identify the standardized test statistic. 2²-[ Identify the P-value P= (Round to two decimal places as needed.) (Round to four decimal places as needed.) Choose the correct conclusion below. O A. Fail to reject Ho. There is enough evidence at the 1% level of significance to reject the spokesperson's claim. OB. Fail to reject Ho. There is not enough evidence at the…
A multiple regression analysis produced the following tables. Predictor Coefficients StandardErrort Statistic p-valueIntercept 624.5369 78.49712 7.956176 6.88E-06x 8.569122 1.652255 5.186319 0.000301x 4.736515 0.699194 6.774248 3.06E-05Source df SS MS F p-valueRegression2 1660914 830457.1 58.319561.4E06Residual 11 156637.5 14239.77Total 13 1817552The adjusted R is ____________.0.91380.88910.88510.8981

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