Which of the following statements is true regarding correlation coefficient andcovariance?Correlation coefficient has no unit of measure, while covariance has a unit ofunits of xmeasure ofunits of yBoth correlation coefficient and covariance do not have any units of measure.Both correlation coefficient and covariance could assume positive and negativevalues.OCorrelation coefficient is another name used for covariance.Correlation coefficient has a unit of measure ofunits of X x units of Y, while covariance does not have a unitof measure.

Introduction: Denote Corr (X, Y) as the correlation coefficient between the two variables, X and Y,…

Answered: Which of the following statements is…

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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Similar questions

Which of the following refers to the variable that is being manipulated during an experiment? Independent variable Descriptive variable Constant Dependent variable
What can be said about the correlation of two variables if the correlation coefficient is r=-0.075 ? There is a strong positive correlation There is a weak negative correlation. There is a weak positive correlation. There is a strong negative correlation There is little or no correlation
True or False The correlation coefficient is a measure of thestrength of a linear relation between two variables and mustlie between - 1 and 1, inclusive.
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What is the difference between our Correlation Coefficient and our Multiple R value? Multiple R is only positive Multiple R can't be below 1 Correlation Coefficients are only positive Correlation Coefficients can't be below 1
1. Determine the type of direction of correlation that you would expect of the given case / situation. Slope of a hill and amount of walking speed. negative correlation zero correlation positive correlation no correlation
Which of the following is incorrect? (a) The correlation and the covariance tell us about the direction of the linear relationship between two variables (b) The covariance ranges between -1 and 1 (c) We can use a scatterplot to observe the strength and direction of the linear relationship between two variables (d) If two variables are independent, then their correlation is zero. (e) We cannot tell the strength of the linear relationship of two variables from the covariance
For this project you will collect at least 10 pairs of quantitative data and conduct a hypothesis test for linear correlation and also give the best predicted value of y for a given value of x. Below is my example:(you can't use my example, you have to come up with your own) I am collecting data to see if there is correlation between a person's age and how many hours they use the phone I will also give the best predicted value for a person who is 40 years old. My data age hours 19 7.3 25 6.5 31 4.6 18 3.2 25 5.2 27 2.9 26 3.7 39 6.5 47 4.1 55 3.9 A)l will test the claim that there is no correlation (start with the Claim and finish with the conclusion) C: Ho HA etc B) will give the best estimate for how many hours a person who is 35 years old uses the phone. (make sure to use the write y and x and include the P value in your answer and decide if you will plug in x or find the average of the y's) The P value is (big/small) so I have to (either give the average of the y's or plug in the x…
If variable X remains constant as variable Y decreases, the correlation is said to be: zero negative positive strong
Which of the following measures the direction of the linear relationship between two variables but does not measure the strength of the relationship? coefficient of variation correlation coefficient covariance variance
If r = +0.2 for 'Age' versus 'Hours spent on social media', what is your conclusion? There is a weak/no covariance between the two variables There is a moderate covariance between the two variables There is a moderate correlation between the two variables There is a weak/no correlation between the two variables
A correlation of 0.95 for two variables X and Y implies that if X increases, there is a 95% chance that Y will also increase. Is this statement true or false?

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