A set of n = 5 pairs of X and Y scoresproduces a Pearson correlation of r = -0.10.The X values vary from 0 to 5 and the Yvalues vary from 2 to 6. If one new individualwith X = 12 and Y = 14 is added to thesample, then what is the most likely value forthe new correlation?%3D

Given: n=5 (X values vary from 0 to 5 and Y values varies from 2 to ) r=-0.10

Answered: A set of n = 5 pairs of X and Y scores…

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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10. Describe the pattern of data that is observed when there is a strong negative correlation between X and Y.
In running a Pearson correlation, you notice that there are a few outliers. Please describe the next course of action, if any, for the dataset and explain your reasoning.
Which of the following best describes the Pearson correlation for these data?
An exercise scientist wants to know if there is a relationship between explosive leg power and standing jump height. To determine if a relationship exists, a group of 30 participants' (N = 30) Leg power was measured by recording each participants' single squat weight max (in pounds). Next, each participant was asked to perform a standing jump and their Jump height was recorded (in meters). Using the variables “Leg power” and “Jump height”, conduct a Pearson's Correlation, at α = 0.05, to determine if leg power and jump height are correlated. Identify the correct null hypothesis A. H0: There is no relationship between leg power and jump height B. H0: There is a relationship between leg power and jump height C. HA: There is a relationship between leg power and jump height D. HA: There is no relationship between leg power and jump height
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 = 0.862. 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? O A. No, because the absolute value of r exceeds the critical value of 0.707. O B. Yes, because r falls between the critical values of -0.707 and 0.707. O C. Yes, because the absolute value of r exceeds the critical value of 0.707. O 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 weight and chest size? (Round to three decimal…
2. A researcher measures the relationship between temperature and freeway traffic, with a correlation of -0.61. We would say this relationship is: Negative and Moderate Negative and Strong Positive and Moderate Positive and Weak O o o O
An exercise scientist wants to know if there is a relationship between explosive leg power and standing jump height. To determine if a relationship exists, a group of 30 participants' (N = 30) Leg power was measured by recording each participants' single squat weight max (in pounds). Next, each participant was asked to perform a standing jump and their Jump height was recorded (in meters). Using the variables “Leg power” and “Jump height”, conduct a Pearson's Correlation, at α = 0.05, to determine if leg power and jump height are correlated. What does this type of correlation indicate about how leg strength and jump height change together? A. Both leg strength and jump height increase and decrease together B. There is no relationship between leg strength and jump height C. As leg strength increases, jump height decreases D. As jump height increases, leg strength decreases
A nutritionist collects data from 25 popular breakfast cereals. For each cereal, the number of calories per serving is plotted on the x-axis against the number of milligrams of sodium on the y-axis. The value of r for the resulting scatterplot is 0.83. How would the value of the correlation coefficient, r, change if sodium was plotted on the x-axis and calories plotted on the y-axis? The value of r would increase. The value of r would not change. The value of r would change to –0.83. The value of r could increase or decrease, depending of the strength of the new relationship.
What is the Pearson's correlation coefficient for a sample of data with values (2, 3), (4, 5), (6, 7), (8, 9), and (10, 11)?
A personnel manager wants to find out if a test carried out during an employee’s interview and a skills assessment at the end of basis training is a guide to performance after working for the company for one year. The table below shows the results of the interview test of 10 employees and their performance after one year. Please provide the Pearson r correlation coefficient for the indicated data. How would you rate the correlation coefficient (strong, moderate, weak)? Employee A B C D E F G H I J Interview test, x % 65 71 79 77 85 78 85 90 81 62 Performance after one year, y % 65 74 82 64 87 78 61 65 79 69
An exercise scientist wants to know if there is a relationship between explosive leg power and standing jump height. To determine if a relationship exists, a group of 30 participants' (N = 30) Leg power was measured by recording each participants' single squat weight max (in pounds). Next, each participant was asked to perform a standing jump and their Jump height was recorded (in meters). Using the variables “Leg power” and “Jump height”, conduct a Pearson's Correlation, at α = 0.05, to determine if leg power and jump height are correlated Interpret r. What type of correlation exists between leg strength and jump height? A. A negative correlation B. No correlation C. A positive correlation D. We cannot know based on the available information
Why is the degrees of freedom equation df=n-2 for the correlation equation instead of df=n-1 as we have used in all the other statistical tests?

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A set of n = 5 pairs of X and Y scores produces a Pearson correlation of r = -0.10. The X values vary from 0 to 5 and the Y values vary from 2 to 6. If one new individual with X = 12 and Y = 14 is added to the sample, then what is the most likely value for the new correlation?