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 tothree 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 oMaximum Weights and Jump HeightsHa: p oMaximumJumpheight, yDetermine the critical value(s).weight, x19060to =(Round to three decimal places as needed. Use a comma to separate answers as needed.)1855615555Determine the standardized test statistic.1805917555t= (Round to three decimal places as needed.)17065What is the conclusion?1505216052Ho. ThereV enough evidence at the 5% level of significance to conclude that there is a significant linear correlation between the maximum w16050180571906021063Fail to rejectRejectPrintDone

Answered: The maximum weights (in kilograms) 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 below are the ages and systolic blood pressures (measured in millimeters of mercury) of 9 randomly selected adults. What is the best predicted value for y given x = 60? Assume that the variables x and y have a significant correlation. Age, x Pressure, y A. 152 B. 148 C. 150 D. 146 38 41 45 48 51 53 57 116 120 123 131 142 145 148 61 65 150 152
Lieutenant Dan, a personal trainer, was interested in whether or not there was a linear relationship between the number of visits his soldiers made to the gym each week and the average amount of time they exercised per visit. He took the following data. Soldier 1 2 3 4 5 6 Number of visits per week 1 3 4 2 3 5 Average time spent exercising per visit (hours) 2 1.5 1 2 2 0.30 Determine the correlation coefficient significant. Please explain how you derived the answer.
Assume that you have paired values consisting of heights (in inches) and weights (in Ib) from 40 randomly selected men. The linear correlation coefficient r is 0.563. Find the value of the coefficient of determination. What practical information does the coefficient of determination provide? Choose the correct answer below. A. The coefficient of determination is 0.317. 68.3% of the variation is explained by the linear correlation, and 31.7% is explained by other factors. O B. The coefficient of determination is 0.683. 31.7% of the variation is explained by the linear correlation, and 68.3% is explained by other factors. O C. The coefficient of determination is 0.683. 68.3% of the variation is explained by the linear correlation, and 31.7% is explained by other factors. O D. The coefficient of determination is 0.317. 31.7% of the variation is explained by the linear correlation, and 68.3% is explained by other factors.
The accompanying table lists the ages of acting award winners matched by the years in which the awards were won. Construct a scatterplot, find the value of the linear correlation coefficient r, and find the P-value of r. Determine whether there is sufficient evidence to support a claim of linear correlation between the two variables. Should we expect that there would be a correlation? Use a significance level of α = 0.01. Click the icon to view the ages of the award winners. Construct a scatterplot. Choose the correct graph below. Best Actor (years) A. 70- 20+ CHIT • 20 70 Best Actress (years) The linear correlation coefficient is r = (Round to three decimal places as needed.) B. Best Actor (years) 70- 20- IDI ... ● 20 70 Best Actress (years) Best Actress 28 30 Best Actor 41 37 O Best Actor (years) Print 70- 20+ Best Actresses and Best Actors 29 61 33 32 36 43 49 50 20 70 Best Actress (years) 46 64 29 60 23 53 40 56 Done 43 43 57 32 n X Best Actor (years) 70- 20+ Mid 18 ● ● 20 70 Best…
For a sample of eight bears, researchers measured the distances around the bears' chests and weighed the bears. Minitab was used to find that the value of the linear correlation coefficient is r= 0.846. Using a = 0.05, determine if there is a linear correlation between chest size and weight. What proportion of the variation in weight can be explained by the linear relationship between weight and chest size? Click here to view a table of critical values for the correlation coefficient. .... a. Is there a linear correlation between chest size and weight? A. No, because the absolute value of the test statistic exceeds the critical value of 0.707. B. Yes, because the absolute value of the test statistic exceeds the critical value of 0.707. C. Yes, because the test statistic falls between the critical values of - 0.707 and 0.707. D. The answer cannot be determined from the given information. b. What proportion of the variation in weight can be explained by the linear relationship between…
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=0.707. At & = 0.05, is there enough evidence to conclude that there is a significant linear correlation between the variables? Click the icon to view the soccer player data. Determine the null and alternative hypotheses. Ho:p = 0 Ha:p # 0 Determine the critical value(s). to = (Round to three decimal places as needed. Use a comma to separate answers as needed.)
Describe the relationship between x and y in a positive correlation and in a negative correlation. In the calculations for the correlation coefficient, what does the sum of products of deviations measure and how is this number used in the calculation of r?
Use Excel, Google Sheets, or SPSS to calculate the Pearson r (correlation) for the following data. Upload a screenshot of the data and result. X Y 2 5 5 1 3 4 4 2
Please explain
For a data set of weights (pounds) and highway fuel consumption amounts (mpg) of twelve types of automobile, the linear correlation coefficient is found and the P-value is 0.009. Write a statement that interprets the P-value and includes a conclusion about linear correlation.
The weights (in pounds) of 6 vehicles and the variability of their braking distances (in feet) when stopping on a dry surface are shown in the table. Can you conclude that there is a significant linear correlation between vehicle weight and variability in braking distance on a dry surface? Use a = 0.05. Weight, x Variability in braking distance, y 5980 5370 6500 5100 5820 4800 1.72 1.97 1.87 1.63 1.64 1.50 Click here to view a table of critical values for Student's t-distribution. Setup the hypothesis for the test. Ho: Ha: P Identify the critical value(s). Select the correct choice below and fill in any answer boxes within your choice. (Round to three decimal places as needed.) O A. The critical value is. B. The critical values are - to and to %3D Calculate the test statistic. t= (Round to three decimal places as needed.) What is your conclusion? There enough evidence at the 5% level of significance to conclude that there a significant linear correlation between vehicle weight and…
Police sometimes measure shoe prints at crime scenes so that they can learn something about criminals. Listed below are shoe print lengths and heights of males. Construct a scatterplot, find the value of the linear correlation coefficient r, and find the P-value of r. Determine whether there is sufficient evidence to support a claim of linear correlation between the two variables. Use a significance level of a = 0.01. Click to view the data on shoe print lengths and heights. Construct a scatterplot. Choose the correct graf Shoe Print Lengths versus Heights of Males O B. OD. 210- 200- 190- 180- 170- 160- 20 24 28 32 36 40 Shoe Print Length (cm) 210 200 190 180 170 160 2101 200- Shoe Print 190- Length (cm) Height (cm) 176.0 180- 170- 160- 20 24 28 32 36 40 31.4 28.9 181.2 31.5 194.5 Shoe Print Length (cm) 32.3 172.9 30.4 174.0 The linear correlation coefficient is r= 32.9 190.5 (Round to three decimal places as needed.) 31.0 176.6 30.8 170.0 30.5 171.9 32.3 175.5 30.5 200.1 32.6 199.3…

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