The accompanying data gives the high and low temperatures in January for arandom sample of 50 cities.a. Decide whether use of the linear correlation coefficient as a descriptive measurefor the data is appropriate.b. If appropriate, obtain the linear correlation coefficient.c. If appropriate, interpret the value of r in terms of the linear relationship between thetwo variables.Click the icon to view the High and Low data values.a. The linear correlation coefficient as a descriptive measure for the dataappropriate becauseb. Select the correct choice and, if necessary, fill in the answer box to complete yourchoice.O A. The linear correlation coefficient is.(Round to three decimal places as needed.)B. The linear correlation coefficient as a descriptive measure for the data is notappropriate. High(x)36349383696263564234295446436788LowSNF 844 FN 4342(y)22227471554545413321184536334573High(x)38576164853488415466355459348727Low(y)284146517923683441482442522578150

The Pearson's correlation coefficient(r) formula is given by,

Answered: The accompanying data gives the high…

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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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.
C learn.vccs.edu/courses/474823/assignments/9794759 Identify if the following correlation. Describe if it is strong, weak, or no correlation. Then determine if the relationship is positive, negative, or neither. Explain your answer. O ● OHO ● ● ● ● O acer Provide your answer in the box below.
Use the data values in the table below to calculate the correlation between the variables x and y. Submit the link through Canvas to either a photo of your work on paper or a link to a google sheet. y 5 2.86 4.48 7 3 8. 3.92 3.34 10 0.96 11 2.38 12 2.8 Give your answer to three decimal places. Submit Question 6.
Identify the type of correlation in the following scatter plot. e Skip step McCully, Aliyah 9. 24AMCCully21C
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.
use the sample data to construct a scatterplot. Use the first variable for the x-axis. Based on the scatterplot, what do you conclude about a linear correlation? FORECAST AND ACTUAL TEMPERATURES The table lists actual high temperatures and the high temperatures that were previously forecasted for these same days. The table includes data for ten different days near the author’s home. What does the result suggest about the accuracy of five-day predicted high temperatures?
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…
Please use the values from the given r values and assign the accurate value to the plots A,B,C, or D.
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 41 116 120 45 48 51 53 57 123 131 142 145 148 61 65 150 152
PLEASE SHOW AN EXPLANATION FOR EACH PART
Jim and Larry run a soup stand. The data below represents the sales of soup that they had on 8 randomly selected days, along with the high temperature on that day. a) Create a scatter diagram for this data set and comment on whether there is a positive or negative relationship between these variables.b) Calculate the correlation coefficient between temperature and sales.c) Does a linear relationship exist between these variables? Explain to me why or why not this is the case.d) Using excel, calculate the least squares regression equation for this data set. e) Using your equation, predict the soup sales on a day where the temperature is 29 degrees.f) Interpret the slope of this equation.g) Can we use this data set and regression equation to predict the sales on a day when the average temperature is 102 degrees? Why or why not?h) What is the R-squared for this equation? How do you interpret that R-squared?
Use the table to find the following, unit price being your (x) and units sold (y). a. Find linear correlation coefficient. Years of Experience Salary in 1000$ 2 3. 15 28 42 13 64 50 16 90 11 58 8 54 b. Find the line of best fit. c. How many units will sell if the unit price was 0.85.

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