1008060402020406080100The scatterplot shows the Exam 1 and Exam 2 scores for a class of 755 integral calculus students during a previous semester. The x-coordinate is theexam 1 score; the y-coordinate is the exam 2 score. Based on the picture, you may assume correlation and regression is appropriate to use for this dataset, and assume scores need not necessarily be integer values. The summary statistics:Average Exam 1 score 70SD - 22.5Average Exam 2 score 74SD x 18r z 0.7For this group of integral calculus students, an extra 10 points on Exam 1 is associated with an extrapoints on Exam 2, on average.Compute the r.m.s.-error associated with using the regression method to estimate Exam 2 from Exam 1.

The summary statistics is provided for exam 1 score (x) and exam 2 score (y). The scatter plot of…

Answered: 100 80 60 40 20 20 40 60 80 100 The…

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 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.
UnattemptedQuestion 1Expand PreviousNext The data below was collected from manufacturer advertisements of their vehicles' horsepower and highway gas mileage (mpg.). Use this data to answer the following questions. x 158 250 340 350 390 190 220 y 33 28 15 17 11 35 42 1. Find the p-value to determine if there is a linear correlation between horsepower and highway gas mileage (mpg). If there is a linear correlation, write the correlation coefficient below if there is a linear correlation, write the regression equation below. Using the data shown above, predict the the highway gas mileage (mpg) for a car that has a horsepower of 225 If there is a linear correlation, what percentage of variation in highway gas mileage (mpg) can be explained by horsepower?
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Do all Please calculate: Parameters a and b Correlation coefficient Determination coefficient Write correlation equation Visualise data in Scatter chart Provide explanation of results Year Sales, MEUR (Yi) Employees with higher education (Xi) 2010 70 10 2011 90 12 2012 100 15 2013 80 13 2014 110 17 2015 120 20 2016 150 25 2017 200 25 2018 200 25
Instructions: Use the technology of your choice (calculator, Excel, GeoGebra, Google Sheets) to find the regression line, the correlation coefficient of the following data sets, and interpolate or extrapolate the requested data. (Round all numbers to the nearest hundredth, when necessary.) Data Set: {(0,7),(1,5),(1.5,3),(2,5),(2.3,3.2),(3,2),(3.5,4)}{(0,7),(1,5),(1.5,3),(2,5),(2.3,3.2),(3,2),(3.5,4)} The regression line is: y=y= Answer −− Answer xx Based on the regression line, we would expect the value of response variable to be Answer when the explanatory variable is 0. (NOTE: This is the yy-intercept.) For each increase of 1 in of the explanatory variable, we can expect a(n) Answerdecreaseincrease of Answer in the response variable. (NOTE: This is the slope.) If x=2.5x=2.5, the yˆ=y^= Answer. This is an example of Answerextrapolationinterpolation. The correlation coefficient is r=r= Answer.
Direction: Answer the problem below. Show your solution. Mr. David runs to keep himself physically fit. He wants to know if there is a relationship between the time elapsed and the kilometers he ran. Kilometers ran Time (in minutes) (X) (Y) 1 10 90 2 10 60 3 16 150 4 16 90 5 21 160 21 180 a. Calculate the Pearson correlation coefficient and regression line based on the data below, b. Draw a scatter plot. Activate Windows N Go to PC settings to activate Windows.
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Listed below are paired data consisting of movie budget amounts and the amounts that the movies grossed. Find the regression equation, letting the budget be the predictor (x) variable. Find the best predicted amount that a movie will gross if its budget is $135 million. Use a significance level of alpha equals 0.05 . Budget left parenthesis $ right parenthesis in Millions 45 24 115 74 72 48 117 67 4 60 124 24 6 152 8 Gross left parenthesis $ right parenthesis in Millions 115 10 95 69 127 112 93 101 50 102 223 26 18 282 56 The regression equation is ŷ = __ + __x. (Round to one decimal place as needed.)
Stan measured the circumference of his classmates' heads and compared it with their current grade in Math. The results are in the given table. Head (cm) Grade 55 23 53 92 57 84 56 88 55 97 53 87 54 72 57 64 57 91 Is there a strong correlation in this data set? How do you know? Yes, there is a strong correlation because the slope is 126.5. No, there is NOT a strong correlation because the correlation coefficient is -0.89, which is close to -1.
For the paired data below, do a complete regression analysis: a) Compute the value of the correlation coefficient. b) Use the Critical Value (CV) from the table to determine if there is a significant linear correlation. c) Determine the linear regression equation.

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