Question 3 of 8View PoliciesCurrent Attempt in ProgressUse StatKey or other technology to find the regression line to predict Y from X using the following data.Click here to access StatKey.escⓇRound your answers to three decimal places.The regression equation is Y<eTextbook and MediaHintSave for Later1A>Z2WUse technology to find the regression equation for the given data.S#3XXEY6D4.5$10 4 14 12C8iR2 10%5TV9.56X.MacBook ProGAttempts: 0 of 3 usedY&7BAssistance UsedH*USubmit Answer8NL9

The regression equation is defined by,The value of slope is,The value of intercept is,

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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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Find the equation of the regression line for the given data. Then construct a scatter plot of the data and draw the regression line. (The pair of variables have a significant correlation.) Then use the regression equation to predict the value of y for each of the given x-values, if meaningful. The number of hours 6 students spent for a test and their scores on that test are shown below. Hours spent studying, x 0 Test score, y 38 Find the regression equation. 2 3 3 4 6 45 50 49 61 68 0 (a) x = 2 hours (c) x = 15 hours (b)x=4.5 hours (d) x=2.5 hours y = X + (Round the slope to three decimal places as needed. Round the y-intercept to two decimal places as needed.)
The file Galton on D2L contains the 928 observations Francis Galton used in 1885 to estimate the relationship between the heights of parents and the heights of their children. The column Children refers to the height (in inches) of a child, and the column Mid-Parents refers to the average height (in inches) of the mother and father of that child. You can download this file into Excel and Minitab. a. Calculate the regression Height of Children = a +b (Height of Mid-Parents). b. Calculate the average for Height of Children, and calculate the average Height of Mid-Parents. c. Create a new variable in Minitab which is the Height of Children measured in terms of deviations from its mean. Call this new variable y. Also, create a new variable in Minitab with is the Height of Mid-Parents measured in terms of deviations from its mean. Call this new variable x. Calculate the regression y = a + bx. You can create the new y and x variables in Excel of Minitab, whichever you find more convenient.…
A regression was run to determine if there is a relationship between hours of TV watched per day (x) and number of situps a person can do (y). The results of the regression were: y=a+bx b=-0.66 a=25.376 r²=0.390625 r=-0.625 Use this to predict the number of situps a person who watches 2.5 hours of TV can do. place. Round to one decimal
Use StatKey or other technology to find the regression line to predict Y from X using the following data. Click here to access StatKey. Round your answers to three decimal places. The regression equation is Y = X 6 Y 3.5 10 8 + 4 2 14 12 12 8.5 X.
A table of data points is given below. a. Find the regression equation for the data points. Use the formulas Sxx=∑ xi−x2 and Sxy=∑ xi−x yi−y to obtain Sxx and Sxy. b. Graph the regression equation and data points. x 1 2 3 y 6 5 10 a. The regression equation is y= + () x. (Simplify your answers. Type integers or decimals. Do not round.)
Use the data below for the PHONES questions that follow. Data compiled from the CIA World Factbook, https://www.cia.gov/. The number of cell phones in use in a country can be approximated from the number of main lines in use in that country by using the regression equation yhat=25+3x which had an R² of about 90%. (The numbers here are millions. For example, "1.2" would represent 1.2 million or 1,200,000.) PHONES 1 At the time the data was collected, the USA had about 314 million cell phones in use and about 150 million main lines in use. What was the residual for the USA? Enter a number of millions. For example, if your answer is 2.85 million enter "2.85". Do not enter anything else such as symbols or words.
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 522 pounds? Use a significance level of 0.05. Chest size (inches) 58 50 65 59 59 48 D 414 312 499 450 456 260 Weight (pounds) Click the icon to view the critical values of the Pearson correlation coefficient r. What is the regression equation? y=+x (Round to one decimal place as needed.) What is the best predicted weight of a bear with a chest size of 63 inches? The best predicted weight for a bear with a chest size of 63 inches is pounds. (Round to one decimal place as needed.) Is the result close to the actual weight of 522 pounds? O A. This result is not very close to the actual weight of the bear. O B. This result is exactly the same as the actual weight of the bear. O C. This result is close to the actual weight of the…
please help me answer this for my data analysis class
Part a: Make a scatter plot and determine which type of model best fits the data.Part b: Find the regression equation.Part c: Use the equation from Part b to determine y when x = 1.5.
A regression was run to determine if there is a relationship between hours of TV watched per day (x) and number of situps a person can do (y).The results of the regression were: y=a+bx b=-0.997 a=20.107 r2=0.950625 r=-0.975 Use this to predict the number of situps a person who watches 0.5 hours of TV can do.Round to one decimal place.
Use the given data set to answer parts (a) and (b). a. Find the regression equation for the data points. X 5 b. Graph the regression equation and the data points. y -1 10 -10 y B. 10 -10- C. a. Find the regression equation for the data points. ŷ-0 (Use integers or decimals for any numbers in the expression. Round to two decimal places as needed.) b. Graph the regression equation and the data points. O A. 10 T -10- 1 2 -1 3 327 D. -10 4 y 4 28 0
Find the intercept (b0) for the regression equation for the following values. Round to 3 decimal places. Intercept xi yi 33 180 25 170 50 200 65 172 57 160 27 165

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Use StatKey or other technology to find the regression line to predict Y from X using the following data.de 10 4 14 12 Click here to access StatKey. Round your answers to three decimal places. The regression equation is = i X 6 Y 4.5 8 + Mi 2 10 9.5 X.