CENGAGE | MINDTAPEnd-of-Chapter Problems: Chapter 14 Correlation and RegressionFor the following scores,X Y11135157685 9Compute SS for X and Y and SP.r =ΣΧΣΥCompute the Pearson correlation.(to two decimal places)ΣΧΥSSXMacBook ProSSYSPa Se

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Answered: IN ENGAGE End-of-Chapter Problems:…

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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Consider the following data.x 3 4 6 7y 1 2 4 5We want to predict y based on observations of x. Answer the following questions. P= Sum Px = 20; Py = 12; Pxy = 70; Px P 2 = 110; and y 2 = 46. n=4 (a) Calculate the sample correlation coefficient r. (b) Calculate the estimates for the y-intercept, βˆ 0, and the slope, βˆ1, for the regression line. (c) Using what you get in part (b), write down the equation of the regression line correctly. (d) Find the coefficient of determination R2. Based on R2 is the regression line useful for us to do prediction? (e) Estimate y for x=5.5
If the linear association between two numerical variables is studied and the correlation coefficient is 0.32, it can be concluded that:a. The relationship between the variables is inverse and weak. b. The percentage of variability observed in the data that is explained by the error is 89.76%. c. The percentage of observed variability in the data that is explained by the model is 32%. d. None is correct Please explain clearly, thank you
The numbers of pass attempts and passing yards for seven professional quarterback for a recent year are listed in the table below. Round all answers to the nearest 1000th. Pass attempts (x) 449 565 528 197 670 351 218 Passing Yards (y) 3265 4018 3669 1141 5177 2362 1737 Calculate the sample correlation coefficient, r. Describe the type of correlation coefficient and interpret the correlation in the context of the data. Find the equation of the regression line for the data. Use the regression equation to predict the average number passing yards if the pass attempts are 250.
The table below gives the age and bone density for 5 women. Use the equation of the regression line, y= b0 + b1x, for predicting a women's bone density based on her age. The correlation coefficient may or may not be statically significant for the data given. Remember it wouldn't be appropiate to use regression line to make a prediction if the correlation coefficient isn;t statically significant. (y has a "hat" on the top) age 39 51 54 56 67 bone density 355 349 347 315 313 Find the estimated slope. Rund your answer to three decimal places. Find the estimated y-intercept. Round your answer to three decimal places. Determine the value of the dependent variable y at x+ 0 (y has a "hat" onthe top) Find the estimated value of y when x = 51. Round your answer to three decimal places. Substitute the values you found in steps 1 and 2 into the equation for the regression line to find the estimated linear model. According to this model, if the valueof the…
9. Find the equation of the regression line for the given data. Then construct a scatter plot of the data and draw the regression line. (Each pair of variables has a significant correlation.) Then use the regression equation to predict the value of y for each of the given x-values, if meaningful. The caloric content and the sodium content (in milligrams) for 6 beef hot dogs are shown in the table below. Calories, x Sodium, y 160 130 330 120 70 190 (a) x = 170 calories (c) x = 150 calories 180 (b) x = 80 calories 420 470 360 250 530 (d) x = 210 calories Find the regression equation. x+( (Round to three decimal places as needed.) y = Choose the correct graph below. OA. О В. OC. OD. 560- 560 560 560- 200 G 0IN T> 200 200 Calories Calories Calories Calories (a) Predict the value of y for x = 170. Choose the correct answer below. O A. 411.632 O B. 543.752 O C. 455.672 O D. not meaningful (b) Predict the value of y for x = 80. Choose the correct answer below. O A. 411.632 О В. 257.492 O C.…
Consider the following correlations -0.9 , -0.5 , -0.2 , 0 , 0.2 , 0.5 and 0.9. For each give the fraction of the variation in y that is explained by the least-squares regression of y on x.
Consider the following table of data. 2 3 4 y 1 1 1 1.1 (a) Calculate the correlation coefficient. (b) Sketch a graph of the data. (c) Based on how closely the data fits a straight line, is your answer to part (a) surprising? Discuss the extent to which the correlation coefficient describes how well the data fit a horizontal line.
Please provide answers for the empty boxes. The following data are the morning and evening high tide levels for Charleston, SC from January 1-14,2017. The information for the PM high tide for January 4 is missing. Create a scatter plot. Find the regression line and use it to estimate the PM high tide for January 4. Then find the correlation coefficient. (NOTE: The first column identifies the day. This data will not be used in the scatter plot.)
Sir Francis Galton, in the late 1800s, was the first to introduce the statistical concepts of regression and correlation. He studied the relationships between pairs of variables such as the size of parents and the size of their offspring. Data similar to that which he studied are given below, with the variable x denoting the height (in centimeters) of a human father and the variable y denoting the height at maturity (in centimeters) of the father's oldest son. The data are given in tabular form and also displayed in the Figure 1 scatter plot. Also given is the product of the father's height and the son's height for each of the fifteen pairs. (These products, written in the column labelled "xy", may aid in calculations.) Height of father, x (in centimeters) 176.6 181.3 171.6 158.3 181.5 190.5 161.2 191.2 175.9 Height of son, y (in centimeters) 173.4 188.9 180.7 175.0 176.3 189.2 168.5 194.8 179.5 191.3 171.2 200.0 170.1 192.2 162.0 186.8 184.9 Send data to calculator 190.9 172.1 176.4…
Problem 2 1. The table below shows data obtained in a study involving the number of fireworks (in millions) used and the number of related injuries. Construct a scatter plot for the data set. 2. Find the value of the linear correlation coefficient r. Analyze this value to identify the type of relationship these data values have. 3. Find the slope b and the y-intercept a of the regression line. 4. Graph the regression line. 5. Predict the number of related injuries if 240 million fireworks are used. 6. Run a hypothesis test to determine if there is a linear relationship between the two variables. Number of 213.9 205.9 234.1 207.5 186.4 225.3 fireworks used X Number of 8800 8600 9600 8600 11,400 10,500 related injuries Y
The correlation coefficient r is a measure of how well a regression line fits data. The value of r is always between −1 and 1. If the fit is exact, the value of r is 1 or −1. Values of r near zero indicate a poor fit. Most calculators can be set to display the correlation coefficient.†For the following data set, do the following. x 2 4 6 8 10 y 6 10 22 18 24 Get the regression line. y = Plot the data and the regression line on the same screen. Report the value of the correlation coefficient. (Round your answer to two decimal places.) r =
8. The table depicted below lists randomly selected students' self-reported hours of studying (x) and their corresponding final grades (y). a. Find the correlation coefficient r. b. Find the critical value. c. Is there a linear correlation (i.e., can Họ be rejected)? d. Find the regression equation. X 30 37 10 18 7 15 y 93 100 75 88 72 86

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IN ENGAGE End-of-Chapter Problems: Chapter 14 Correlation and Regression For the following scores, X 11 3 5 6 5 r = Y 1 15 Compute SS for X and Y and SP. ΣΧ 7 8 9 ΣΥ Compute the Pearson correlation. (to two decimal places) ΣΧΥ SSX MacBook Pro SSY SP a s