Determine the regression equation in three different ways (using raw data, deviation data and standard data) by first determining the values of the coefficients b and a from the data using 3 different formulas. Find the value of the correlation coefficient and write down the interpretation of the correlation coefficient with r2

Determine the regression equation in three different ways (using raw data, deviation data and standard data) by first determining the values of the coefficients b and a from the data using 3 different formulas. Find the value of the correlation coefficient and write down the interpretation of the correlation coefficient with r2

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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A regression analysis was performed to determine if there is a relationship between hours of TV watched per day (xx) and number of sit ups a person can do (yy). The results of the regression were: y=ax+b a=-1.013 b=27.906 r2=0.498436 r=-0.706 Use this to predict the number of sit ups a person who watches 12 hours of TV can do, and please round your answer to a whole number.
you take a bar of soap and weigh it after each shower. will the regression line be positive or negative
The data show the chest size and weight of several bears. Find the regression equation, letting chest size be the independent (x) variable. Then find the best predicted weight of a bear with a chest size of 48 inches. Is the result close to the actual weight of 538 pounds? Use a significance level of 0.05.
The data show the chest size and weight of several bears. Find the regression equation, letting chest size be the independent (x) variable. Then find the best predicted weight of a bear with a chest size of 63 inches. Is the result close to the actual weight of 442 pounds? Use a significance level of 0.05. Chest size (inches) 58 50 65 59 59 48 Weight (pounds) 414 312 499 450 456 260 Click the icon to view the critical values of the Pearson correlation coefficient r. What is the regression equation? Critical Values of the Pearson Correlation Coefficient r y=D+x (Round to one decimal place as ne Critical Values of the Pearson Correlation Coefficient r 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. a = 0.05 a = 0.01 4 0.950 0.990 0.878 0.811 0.754 5 0.959 6 0.917 7 0.875 8 0.707 0.834 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…
Researchers are interested in predicting the height of a child based on the heights of their mother and father. Data were collected, which included height of the child (height ), height of the mother ( mothersheight), and height of the father (fathersheight ). The initial analysis used the heights of the parents to predict the height of the child (all units are inches). The results of the analysis, a multiple regression, are presented below. . regress height mothersheight fathersheight Source Model Residual Total height mothersheight fathersheight _cons SS df 208.008457 314.295372 37 2 104.004228 8.49446952 MS 522.303829 39 13.3924059 Coef. Std. Err. .6579529 .1474763 .2003584 .1382237 9.804327 12.39987 t P>|t| 4.46 0.000 C 0.156 0.79 0.434 Number of obs = F( 2, 37) = Prob > F R-squared Adj R-squared Root MSE = .3591375 -.0797093 -15.32021 = 40 12.24 0.0001 0.3983 0.3657 2.9145 [95% Conf. Interval] .9567683 .4804261 34.92886 What is the predicted height for a child born to a mother…
The data show the chest size and weight of several bears. Find the regression equation, letting chest size be the independent (x) variable. Then find the best predicted weight of a bear with a chest size of 40 inches. Is the result close to the actual weight of 352 pounds? Use a significance level of 0.05. Chest size (inches) *Weight (pounds) 44 54 328 528 41 55 39 51 418 580 296 503 Click the icon to view the critical values of the Pearson correlation coefficient r. - What is the regression equation? x (Round to one decimal place as needed.)
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The accompanying table shows results from regressions performed on data from a random sample of 21 cars. The response (y) variable is CITY (fuel consumption in mi/gal). The predictor (x) variables are WT (weight in pounds), DISP (engine displacement in liters), and HWY (highway fuel consumption in mi/gal). Which regression equation is best for predicting city fuel consumption? Why? Click the icon to view the table of regression equations. Choose the correct answer below. A. The equation CITY=6.86 -0.00131WT -0.258DISP+0.659HWY is best because it has a low P-value and the highest value of R². B. The equation CITY=6.73 -0.00157WT +0.668HWY is best because it has a low P-value and the highest adjusted value of R². C. The equation CITY= -3.15+0.823HWY is best because it has a low P-value and its R² and adjusted R² values are comparable to the R² and adjusted R² values of equations with more predictor variables. O D. The equation CITY=6.86 -0.00131WT-0.258DISP + 0.659HWY is best because it…
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A car lot wants to predict the # of car accessories they need to sell monthly based on the number of cars sold. The car lot owner randomly selects 12 months of data. The simple linear regression equation is ý = 14 - 2x. The coefficient of determination is 0.6364. What is the correlation coefficient to describe the relationship between these two variables? Multiple Choice
A statistics professor wants to use the number of hours a student studies for a statistic final exam (x) to predict the final exam score (y). A regression model was fit based on data collected for a class during the previous semester, with the following results: y =35.0 + 3x Which of the following is the correct interpretation of the regression coefficient (slope)? Select the correct response: When the student does not study for the final exam, the mean final exam score is 35.0. None of the above are an interpretation of the slope For each increase of one hour in studying time, the mean change in the final exam score is predicted to be 35.0 For each increase of one hour in studying time, the mean change in the final exam score is predicted to be 3.0.
A regression was run to determine if there is a relationship between hours of TV watched per day (xx) and number of situps a person can do (yy). The results of the regression were: y=ax+ba=-1.392b=32.952r2=0.527076r=-0.726Use this to predict the number of situps a person who watches 2 hour(s) of TV can do, and please round your answer to a whole number.