15) Find the unexplained variation for the paired data. The equation of the regression line for thepaired data below is y = 3x.%3Dx| 2 45 6y 7 11 13 20A) 88.75B) 78.75C) 14.25D) 10.00

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Answered: 15) Find the unexplained variation for…

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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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 58 inches. Is the result close to the actual weight of 572 pounds? Use a significance level of 0.05. Chest size (inches) 46 57 53 41 40 40 Weight (pounds) 384 580 542 358 306 320 LOADING... Click the icon to view the critical values of the Pearson correlation coefficient r. What is the regression equation? y=nothing+nothingx (Round to one decimal place 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.…
. Determine the regression equation using values you create for x and y for at least 10 pairs of data. Show the regression equation, correlation coefficient, and coefficient of determination. Then switch the x and y values for each data point. Based on that, again show the regression equation, correlation coefficient, and coefficient of determination. Discuss the similarities and differences between the results.
Data were gathered and displayed on the scatter plot. 15 y 14 13 12 11 10 9 8 7 6 5 4 3 2 1 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 Which of the following is the best equation to model the data? ŷ = 11.5(0.81)* Oŷ = 8.1(0.115)* Oŷ= -0.115x+8.1 Oŷ=-0.81x+11.5
Listed below are the overhead widths (cm) of seals measured from photographs and the weights (kg) of the seals. Overhead width 7.2 7.4 9.8 9.4 8.8 8.4 Weight 116 154 245 202 200 191 The four pairs of values below were obtained from the regression equation. Which is an extrapolation? a) overhead width 7.5 cm, weight 144 kg b) overhead width 10 cm, weight 245 kg c) overhead width 7.2 cm, weight 132 kg d) overhead width 8.1 cm, weight 169 kg
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Name: Shalisha Robertson Topic 30-8th Core Hours spent looking for Shooting Stars Number of shooting stars seen 0 2 0 3 2 4 1 4 2 6 4 6 2 7 What is the equation of your regression line for this data eate a scatter plot with your regression line drawn in.
5. Find the equation of the regression line for the below data (Absences and Final Grades) Students A B C D E F G Number of Absences X 6 2 15 9 12 5 8 Final Grades(%) Y 82 86 43 74 58 90 78 i) Graph the line on the scatter plotii) Predict the final grade for a student who missed 4 classes.
An instructor asked a random sample of eight students to record their study times at the beginning of a course. She then made a table for total hours studied (x) over 2 weeks and test score (y) at the end of the 2 weeks. The table is given below. Complete parts (a) through (f). x 10 13 10 18 6 15 16 21 y 93 79 81 74 85 81 85 80 a. Find the regression equation for the data points. b. Graph the regresson equation c. Describe the apparent relationship between the two variables. d. Identify the predictor and response variables. e. Identify outliers and potential influential observations. f.Predict the score for a student that studies for 17 hours.
Need help answering A, B and C. B) Perform the linear regression calculation and provide the linear regression equation that describes the relationship between Y (number of air conditioning units sold) and X (outside temperature in degrees Fahrenheit. Calculate: Coefficient of determination (r2) and the coefficient of correlation (r).  SST, SSE and SSR for this linear regression.  Estimate for the variance (σ2) and the standard deviation for the linear regression model you have developed. C) Using the linear regression equation that you developed in topic (b), calculate the estimated sales for a day that will reach 72 degrees F and for a day that will reach 94 F and for both temperature levels calculate the error “e” when comparing the estimated value against the actual data provided. At which of the two temperatures, is your model more accurate? Justify.
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Use the data in the table below to complete parts (a) through (d). x 37 34 40 46 42 50 62 56 51 y 22 20 25 32 27 30 30 25 28 Find the equation of the regression line. y=

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15) Find the unexplained variation for the paired data. The equation of the regression line for the paired data below is y = 3x. %3D x| 2 4 5 6 y 7 11 13 20 A) 88.75 B) 78.75 C) 14.25 D) 10.00