Listed below are annual data for various years. The data are the numbers of cars sold (thousands) and the numbers of points scored in the Super Bowl. Construct a scatterplot, find the value of the linear correlation coefficient rand find the P-value using a = 0.05. Is there sufficient evidence to conclude that there is a linear correlation between those two variables? Would it be reasonable to expect a correlation?Car SalesSuper Bowl Points61708170 8220 8510 900076864085268273 8144 D4245555553What are the null and alternative hypotheses?OA. Ho: p=0H₁: p>0.OC. Ho: p=0H₁: p<0Construct a scatterplot. Choose the correct graph below.OA.100-8000Car Sales9000The linear correlation coefficient is r=(Round to three decimal places as needed.)B.100-Q50-80009000Car SalesB. Ho: p 0H₁ p=0D. Ho p=0H₁: p 0C.100+Q100-500+8000Car Sales9000o+80009000Car Sales

Explanation in Problems 1 & 21. For linear correlation, the null hypothesis states that the…

Answered: Listed below are annual data 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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Listed below are annual data for various years. The data are weights (metric tons) of imported lemons and car crash fatality rates per 100,000 population. Construct a scatterplot, find the value of the linear correlation coefficient r, and find the P-value using a = 0.05. Is there sufficient evidence to conclude that there is a linear correlation between lemon imports and crash fatality rates? Do the results suggest that imported lemons cause car fatalities? Lemon Imports Crash Fatality Rate 229 266 359 480 530 15.9 15.6 15.5 15.3 14.9 Construct a scatterplot. Choose the correct graph below. OA. В. Ос. D. Ay 17- Ay 17- Ay 17- Ay 17- 16- 16- 16- 16- 15- 15- 15- 15- 14+ 14+ 14- 14+ 200 400 600 200 400 600 200 400 600 200 400 600 The linear correlation coefficient r is (Round to three decimal places as needed.)
Does a major league baseball team's record during spring training indicate how the team will play during the regular season? Over a six-year period, the correlation coefficient between a team's winning percentage in spring training and its winning percentage in the regular season is 0.18. Shown are the winning percentages for the 14 American League teams during a season. Team SpringTraining RegularSeason Team SpringTraining RegularSeason Baltimore Orioles 0.415 0.430 Minnesota Twins 0.497 0.548 Boston Red Sox 0.427 0.594 New York Yankees 0.585 0.527 Chicago White Sox 0.418 0.564 Oakland A's 0.694 0.474 Cleveland Indians 0.566 0.497 Seattle Mariners 0.497 0.385 Detroit Tigers 0.566 0.465 Tampa Bay Rays 0.732 0.607 Kansas City Royals 0.534 0.470 Texas Rangers 0.642 0.492 Los Angeles Angels 0.717 0.625 Toronto Blue Jays 0.444 0.539 a. What is the correlation coefficient between the spring training and the regular season winning…
The maximum weights (in kilograms) for which one repetition of a half-squat can be performed and the jump heights (in centimeters) for 12 international soccer players are given in the accompanying table. The correlation coefficient, rounded to three decimal places, is r=0.692. At a =0.05, is there enough evidence to conclude that there is a significant linear correlation between the variables? E Click the icon to view the soccer player data. Determine the null and alternative hypotheses. Ho: p o Maximum Weights and Jump Heights Ha: p o Maximum Jump height, y Determine the critical value(s). weight, x 190 60 to = (Round to three decimal places as needed. Use a comma to separate answers as needed.) 185 56 155 55 Determine the standardized test statistic. 180 59 175 55 t= (Round to three decimal places as needed.) 170 65 What is the conclusion? 150 52 160 52 Ho. There V enough evidence at the 5% level of significance to conclude that there is a significant linear correlation between the…
The data below are the ages and annual pharmacy b ills (in dollars) of 9 randomly selected employees. Calculate the linear correlation coefficient.Age, x: 30, 33, 37, 40, 43, 45, 49, 53, 57Pharmacy bill ($), y: 119, 123, 126, 134, 145, 148, 151, 153, 155A. 0.890B. 0.908C. 0.998D. 0.960
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The data shown below consists of the price (in dollars) of 7 events at a local venue and the number of people who attended. Determine if there is significant negative linear correlation between ticket price and number of attendees. Use a significance level of 0.05 and round all values to 4 decimal places. Ticket Price Attendence 6. 109 10 114 14 139 18 142 22 139 26 132 30 154 Но: = 0 На: р Find the Linear Correlation Coefficient r = Find the p-value p-value = %3D The p-value is Less than (or equal to) a Greater than a The p-value leads to a decision to Reject Ho Аcсept Ho Do Not Reject Ho The conclusion is There is a significant linear correlation between ticket price and attendance. There is a significant negative linear correlation between ticket price and attendance. There is a significant positive linear correlation between ticket price and attendance. There is insufficient evidence to make a conclusion about the linear correlation between ticket price and attendance.
grocery store manager did a study to look at the relationship between the amount of time (in minutes) customers spend in the store and the amount of money (in dollars) they spend. The results of the survey are shown below. Time 12 26 5 28 11 24 20 21 16 Money 37 84 34 84 53 92 72 92 75 Find the correlation coefficient: r=r= Round to 2 decimal places. The null and alternative hypotheses for correlation are:H0:H0: P == 0 H1:H1: P ≠≠ 0 The p-value is: (Round to four decimal places) r2 = (Round to two decimal places)
Give an example of a correlation study, and identify the independent and dependent variables.
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.

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