Here is partial output from a simple regression analysis. The regression equation is EAFE = 4.76 + 0.663 S&P Analysis of Variance Source DF MS F P Regression 1 3445.9 3445.9 9.50 0.005 Residual Error Total 29 13598.3 Calculate the values of the following: The regression standard error, s, (Round to 3 decimal places) The coefficient of determination, r2 (Round to 4 decimal places) The correlation coefficient, r (Round to 4 decimal places)

Here is partial output from a simple regression analysis. The regression equation is EAFE = 4.76 + 0.663 S&P Analysis of Variance Source DF MS F P Regression 1 3445.9 3445.9 9.50 0.005 Residual Error Total 29 13598.3 Calculate the values of the following: The regression standard error, s, (Round to 3 decimal places) The coefficient of determination, r2 (Round to 4 decimal places) The correlation coefficient, r (Round to 4 decimal places)

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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Listed below are systolic blood pressure measurements (in mm Hg) obtained from the same woman. Find the regression equation, letting the right arm blood pressure be the predictor (x) variable. Find the best predicted systolic blood pressure in the left arm given that the systolic blood pressure in the right arm is 80 mm Hg. Use a significance level of 0.05. Right Arm 100 99 91 78 77 D Left Arm 177 171 144 146 146 Data Table Click the icon to view the critical values of the Pearson correlation coefficient r The regression equation is y= Round to one decimal place as needed.) NOTE: To test Ho p= 0 against H,: p±0, reject Ho if the absolute value of r is greater than the critical value in the table. X. a = 0.05 a = 0.01 4. 0.950 0.990 0.878 0.959 0.811 0.917 0.754 0.875 0.834 7 0.707 9. 0.666 0.798 10 0.632 0.765 11 0.602 0.735 12 0.576 0.708 13 0.553 0.684 14 0.532 0.661 15 0.514 0.641 16 0.497 0.623 17 0.482 0.606 18 0.468 0.590 0.456 0.575 0.561 19 20 0.444 25 0.396 0.505 30 0.361…
Given the Analysis of Variance (ANOVA) for the simple linear regression as Table Q3 below: Table Q3: ANOVA Source df SS MS F Regression 1 27.8345 C E Error A 0.655 Total B (a) Calculate A, B, C, D and E. (b) At a = 0.05 test whether the regression model are fit.
d) Hence test whether ? is significant. Give reasons for your answer.
Please give handwritten
Consider the following data: x¯ = 20, sx = 2, y¯ = −5, sy = 4, and b1 = 0.40. Which of the following is the sample regression equation?
The regression equation is Health Index= y + a Age + ß Blood sugar + 8 Blood Pressu SE 20986 Coef 2912 339.28 71.95 Age Blood sugar 209.2 179.3 Blood pressure 207.2 225.4 S = 962.233 R-Sq = 86.6% R-Sq (adj) = 76.5% Coef Constant Analysis of Variance Source DF Regression 3 Residual Error 4 Total 7 SS 23863180 3703570 27566750 MS T 7.21 4.72 * 0.92 7954393 925892 F *** P 0.002 0.009 0.308 до 0.032 a) What is dependent and independent variables? b) Fully write out the regression equation. c) Fill in the missing values **, ***', and *****. d) Hence test whether & is significant. Give reasons for your answer. e) Perform the F Test making sure to state the null and alternative hypothesis.
Listed below are systolic blood pressure measurements (in mm H) obtained from the same woman. Find the regression equation, letting the fight arm blood pressure be the predictor (x) variable, Find the best predicted systolic blood pressure in the left arm given that the systolic blood pressure in the right arm is 90 mm Hg. Use a significance level of 0.05. Right Arm 100 99 93 76 77 9 Left Arm 176 169 118 145 146 EB Click the icon to view the critical values of the Pearson correlation coefficient r The regression equation is 9 = 7 + 7x (Round to one decimal place as needed.) Given that the systolic blood pressure in the right arm is 90 mm Hq, the best predicted systolic blood pressure in the left arm is mm Hg. (Round to one decimal place as needed.)
Fifty matched pairs of magnitude/depth measurements randomly selected from 10,594 earthquakes recorded in one year from a location in southern California. Find the best predicted depth of an earthquake with a magnitude of 2.2. Use a significance level of 0.05. : Click the icon to view the earthquake measurements. Click here to view the critical values of the Pearson correlation coefficient, r. The regression equation is y = ? + ?x. 回-ト i Critical Values of the Pearson Correlation Coefficient r (Round to one decimal place as needed.) Critical Values of the Pearson Correlation Coefficient r α= 0.05 0.950 0.878 0.811 0.754 0.707 0.666 0.632 0.602 0.576 a = 0.01 0.990 0.959 0.917 0.875 0.834 0.798 0.765 0.735 0.708 NOTE: To test Ho: p=0 Jagainst H,: p#0, reject H, if the absolute value of r is greater than the critical value in the table. 5 10 11 12
Consider the following computer output from a multiple regression analysis relating the cost of car insurance to the variables: number of car accidents, driver's credit score, and safety rating of the car. Answer Coefficients Coefficients Standard Error 680 Intercept Car Accidents (In last 3 years) Credit Score -74.80 Safety Rating - 104.25 122.40 Does the sign of the coefficient for the variable number of car accidents make sense? 79.71 14.75 8.89 11.46 t Stat P-value 8.531 0.0000 0.0000 8.298 -8.414 -9.097 0.0000 0.0000 O Yes, because it is expected that as the number of car accidents increases then the cost should also increase. O No, because it is expected that as the number of car accidents increases then the cost should also increase. O Yes, because it is expected that as the number of car accidents increases then the cost should decrease. O No, because it is expected that as the number of car accidents increases then the cost should decrease. Keyp- Keyboard Short Tables
Consider the following computer output from a multiple regression analysis relating the price of a used car to the variables: age of car, mileage, and safety rating. Intercept Age (Year) Mileage (in Thousands) Safety Rating Does the sign of the coefficient for the variable age make sense? Answer Coefficients 23287.65 7671.28 -551.27 Coefficients 894.16 Standard Error 3327.39 8976.75 45.10 96.36 t Stat 6.999 0.855 - 12.223 9.280 P-value 0.0000 0.3965 0.0000 0.0000 O No, because it is expected that as age increases then the price should decrease. O Yes, because it is expected that as age increases then the price should also increase. O No, because it is expected that as age increases then the price should also increase. O Yes, because it is expected that as age increases then the price should decrease. Tables Key Keyboard Shor
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Use the value of the linear correlation coefficient to calculate the coefficient of determination. What does this tell you about the explained variation of the data about the regression line? About the unexplained variation? r = -0.338 Calculate the coefficient of determination. (Round to three decimal places as needed.) % of the variation can be explained by the regression line (Round to one decimal place as needed.) % of the variation is unexplained and is due to other factors or to sampling error. (Round to one decimal place as needed.)