(c) Compute the linear correlation coefficient between the weight of a car and its miles per gallon. An engineer wanted to determine how the weight of a car affects gas mileage. Thefollowing data represent the weight of various cars and their gas mileage. Complete parts(a) through (d).CarABCDEWeight(pounds)36803310300035902580Miles perGallon1919Click the icon to view the critical values table.221827(b) Draw a scatter diagram of the data. Choose the correct scatter plot.O A.B.Weight (lbs)mes per galon.Weight (lbs)40002500+15O. C.400012500+15..•.mpgmpg.30B30QQQGd30-O D.15+2500ra(Round to three decimal places as needed.)30-184000Weight (lbs)..15+2500Weight (lbs)4000QQ(c) Compute the linear correlation coefficient between the weight of a car andits miles per gallon.

We have given that, Weight (X) :- 3680, 3310, 3000, 3590, 2580 Miles per gallon (Y) :- 19, 19, 22,…

Answered: (c) Compute the linear correlation…

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 correlation between a variable and itself is always equal to 0.5 -1 1
Let x be the weight of a car (in hundreds of pounds), and let y be the miles per gallon (mpg). Suppose a car weighs 3800 pounds. What does the data forecast for the miles per gallon expected? x= 27 44 32 47 23 40 34 52 y= 30 19 24 13 29 17 21 14 State the correlation coefficient and state the regression lin equation.
(d) Compute the linear correlation coefficient between MRI count and IQ for females. Compute the linear correlation coefficient between MRI count and IQ for males. The linear correlation coefficient for females is enter your response here. 2.The linear correlation coefficient for males is enter your response here. Cirticul value table 3 0.997 4 0.950 5 0.878 6 0.811 7 0.754 8 0.707 9 0.666 10 0.632 11 0.602 12 0.576 13 0.553 14 0.532 15 0.514 16 0.497 17 0.482 18 0.468 19 0.456 20 0.444 21 0.433 22 0.423 23 0.413 24 0.404 25 0.396 26 0.388 27 0.381 28 0.374 29 0.367 30 0.361 n
Find the equation of the regression that model the relationship between the weight of mail and number of order using HYPERBOLIC EQUATION. Compute for the correlation coefficient using PEARSON PRODUCT MOMENT CORRELATION COEFFICIENT (PPMCC).
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A random sample of college students was surveyed about how they spend their time each week. The scatterplot below displays the relationship between the number of hours each student typically works per week at a part- or full-time job and the number of hours of television each student typically watches per week. The correlation between these variables is r = –0.63, and the equation we would use to predict hours spent watching TV based on hours spent working is as follows: Predicted hours spent watching TV = 17.21 – 0.23(hours spent working) Since we are using hours spent working to help us predict hours spent watching TV, we’d call hours spent working a(n) __________________ variable and hours spent watching TV a(n) __________________ variable. The correlation coefficient, along with what we see in the scatterplot, tells us that the relationship between the variables has a direction that is _________________ and a strength that is ______________________. According to the…
A veterinarian collects data about puppies in her practice. She makes a scatterplot with their age in days on the horizontal axis and weight in ounces (oz) on the vertical axis and sees that the scatterplot is football shaped. The average age of the puppies is 42 day, with an SD of 7 days. The average weight is 25 oz, with an SD of 5 oz. The correlation is moderately strong, with r = 0.7. 4. (a) The age of two puppies differ by 20 days. The difference in their weight is predicted to be oz. (b) The baseline prediction for the weight of puppies ignores their age and just uses the information from all puppies. The baseline predicted weight is oz and the RMS error for this prediction is o. (c) Suppose we converted the units of age from days to weeks. The average age is now weeks, with an SD of weeks. The correlation between age in weeks and weight in oz is now r =
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Can you please check my work
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