Using the following information. CoefficientsIntercept−12.8094Independent variable2.1794ANOVA dfSSMSFRegression112,323.5612,323.5690.0481Residual81,094.842136.8553 Total913,418.4 Estimate the value of Ŷ when X = 4. Multiple Choice 10.45 3.73 8.718 −4.092

We have given that the following information Coefficients Intercept −12.8094…

Answered: Using the following information.…

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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	Coefficients
Intercept	−12.8094
Independent variable	2.1794

	df	SS	MS	F
Regression	1	12,323.56	12,323.56	90.0481
Residual	8	1,094.842	136.8553
Total	9	13,418.4

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A student who took Stat 2 last semester learns that their overall course score was at the 85th percentile. Assuming that a histogram of all the Stat 2 course scores from last semester follows the normal curve, this means that this student had a score that was how many SDs above the average score? Choose the answer that is closest. Group of answer choices 1.15 1.05 1.10 1.0 PreviousNext
Analysis of Variance Source DF SS MS Regression 1 Residual Error 13 0.2364 Total 14 11.3240 What is the value for MSR (Mean Square for Regression)?
Using the following information: Coefficients Intercept -12.8094 Independent variable 2.1794 ANOVA df SS MS F Regression 1 12323.56 12323.56 90.0481 Residual 8 1094.842 136.8550 Total 9 13418.4 a) What is the standard error of the estimate? A) 136.8552B) 1094.842C) 11.6985D) 13418.4 b) What is the coefficient of determination? Round the percentage to one decimal point. A) 91.8%B) 8.2%C) 90.0%D) 136.9% c) What is the correlation coefficient? A) 0.9184B) 0.9583C) −0.9583D) 0.9004 d) The regression equation is ________. A) = 2.1794 − 12.8094XB) = −12.8094 + 2.1794XC) 12.8094X = 2.1794D) X = −12.8094 + 2.1794 e) The regression analysis can be summarized as follows: A) No significant relationship exists between the variables.B) A significant negative relationship exists between the variables.C) A significant positive relationship exists between the variables.D) For every unit increase…
If a regression model of the form y= B,+B,x,+... + B,x, is fit to 132 observations on each variable and yields an R´value of 0.87, fill in the blanks in the following ANOVA table. Do all calculations to at least three decimal places. Source of Degrees of freedom Sums of Mean F statistic variation squares squares Regression 69 Error Total
The commercial division of a real estate firm is conducting a regression analysis of the relationship between x, annual gross rents (in thousands of dollars), and y, selling price (in thousands of dollars) for apartment buildings. Data were collected on several properties recently sold and the following computer output was obtained. Analysis of Variance SOURCE DF Adj SS Regression 1 41587.3 Error 7 Total 8 51984.1 Predictor Coef SE Coef T-Value Constant 20.000 3.2213 6.21 X 7.210 1.3626 5.29 Regression Equation Y = 20.0 + 7.21 X (a) How many apartment buildings were in the sample? (b) Write the estimated regression equation. ŷ = (c) What is the value of sb1? Find the value of the test statistic. (Round your answer to two decimal places.) Find the p-value. (Round your answer to three decimal places.) p-value =
Data for 34 results are given below: Source SS Regression Residual Total 2,306,075.92 1,217,176.18 3,823,252.10 ABC df 1 32 33 MS 2,306,075.92 47,411.76 Based on the results what do you conclude and why using a = .05? F
X Y 14 25 26 28 19 27 13 21 28 31 23 24 30 31 26 27 19 29 Mean. 22 27 Std. Dev 6.08 3.28 Correlation 0.76 What is the t value using the direct difference approach? (report positive t-value)
Rewrite the regression model to include coefficients from your regression analysis output and then answer the following question What would be the company's loss if the significant variable(s) change per unit? SUMMARY OUTPUT Regression Statistics Multiple R 0.93082 R Square 0.866425 Adjusted R Square 0.85833 Standard Error 4108.993 Observations 36 ANOVA df SS MS F Significance F Regression 2 3.61E+09 1.81E+09 107.0261 3.75E-15 Residual 33 5.57E+08 16883824 Total 35 4.17E+09 Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Lower 95.0% Upper 95.0% Intercept 3996.678 6603.651 0.605223 0.549171 -9438.55 17431.91 -9438.55 17431.91 X Variable 1 43.5364 3.589484 12.12887 1.05E-13 36.23354 50.83926 36.23354 50.83926 X Variable 2…
Item9 eBook Item 9 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 9.601 3.153 3.045 0.010 x1 0.221 0.110 2.009 0.000 x2 − 1.168 0.577 − 2.024 0.028 x3 − 0.106 0.073 − 1.452 0.114 x4 0.631 0.361 1.748 0.001 x5 − 0.043 0.029 − 1.483 0.112 Analysis of Variance Source DF SS MS F p-value Regression 5 1,844.31 368.9 9.37 0.000 Residual Error 56 2,205.62 39.39 Total 61 4,049.93 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…

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Using the following information. Coefficients Intercept −12.8094 Independent variable 2.1794 ANOVA df SS MS F Regression 1 12,323.56 12,323.56 90.0481 Residual 8 1,094.842 136.8553 Total 9 13,418.4 Estimate the value of Ŷ when X = 4. Multiple Choice 10.45 3.73 8.718 −4.092