Square Footage, x219231781077202631052860394421502507170716993718Selling Price ($000s), y378.1373.7181.9346621.5383.6601.1367.4409.1298.1257.3675.2 10:32One of the biggest factors in determining the value of a home is the square footage. The accompanying datarepresent the square footage and selling price (in thousands of dollars) for a random sample of homes for sale in acertain region. Complete parts (a) through (h) below.Click the icon to view the housing data.(a) Which variable is the explanatory variable?Square FootageSelling Price(b) Draw a scatter diagram of the data. Choose the correct scatter diagram below.O A.AX4000-1000+150●●•700YesNoO B.Ay700-150-10004000C.Q700(Round to three decimal places as needed.)(d) Is there a linear relation between square footage and asking price?Ay150+(c) Determine the linear correlation coefficient between square footage and asking price.1000OA. No-a house of square feet is not possible.(Type an integer or a simplified fraction.)|||:=OB. For a house that is sold for $0, the predicted square footage is(Round to three decimal places as needed.)OC. For a house that is 0 square feet, the predicted selling price is(Round to three decimal places as needed.)OD. For every additional square foot, the selling price increases by(Round to three decimal places as needed.)OE. It is not appropriate to interpret the slope.The home's price isthousand.(Round to the nearest whole number as needed.)Vo) 1LTE 4G 15%(e) Find the least-squares regression line treating square footage as the explanatory variable.ý = [x+(D(Round the slope to three decimal places as needed. Round the intercept to one decimal place as needed.)O A. For every additional thousand dollars in selling price, the square footage increases byaverage.(Round to three decimal places as needed.)4000(f) Interpret the slope. Select the correct choice below and, if necessary, fill in the answer box to complete yourchoice.OD. No-a house of square feet is outside the scope of the model(Type an integer or a simplified fraction.)O E. More information about the houses is necessary before deciding.OOC. Yes-a house of square feet is possible and within the scope of the model.(Type an integer or a simplified fraction.)D.4000-B. No a house of square feet is not possible and outside the scope of the model.(Type an integer or a simplified fraction.)1000-Ауthousand dollars.(g) Is it reasonable to interpret the y-intercept? Why? Select the correct choice below and, if necessary, fill in theanswer box to complete your choice.1000thousand dollars, on average.(h) One home that is 1477 square feet is sold for $225 thousand. Is this home's price above or below average for ahome of this size?4000square feet, onthe average price. The average price of a home that is 1477 square feet is $

Answered: Image /qna-images/answer/7dbbc368-03e3-48c8-b6ea-2ca59d9abc03.jpg

Answered: (a) Which variable is the explanatory…

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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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?
Determine the type of direction of correlation that you would expect of the given case / situation. Speed of wind turbines and amount of electricity generated. no correlation positive correlation zero correlation negative correlation
PLEASE SHOW AN EXPLANATION FOR EACH PART
Match the correlation coefficients with their scatterplots. Select the letter of the scatterplot below which corresponds to the correlation coefficient. (Click on image for a larger view.) B v 1. r = -0.74 с A ✓ 2. r = -0.49 3. r = -0.97 4. r = 0.76 A B D
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Let x be the average number of employees in a group health insurance plan, and let y be the average administrative cost as a percentage of claims. x 3 7 15 32 75 y 40 35 30 26 18 (a) Make a scatter diagram of the data and visualize the line you think best fits the data. Flash Player version 10 or higher is required for this question. You can get Flash Player free from Adobe's website. (b) Would you say the correlation is low, moderate, or strong? positive or negative? strong and negative moderate and negative strong and positive low and negative low and positive moderate and positive (c) Use a calculator to verify that Σx = 132, Σx2 = 6932, Σy = 149, Σy2 = 4725, and Σxy = 2997. Compute r. (Round your answer to three decimal places.)r = As x increases, does the value of r imply that y should tend to increase or decrease? Explain. Given our value of r, we cannot draw any conclusions for the behavior of y as x increases. Given our value of r, y should tend to…
An aerobic exercise instructor remembers the data given in the following table, which shows the recommended maximum exercise heart rates for individuals of the given ages. Age (x years) 20 30 40 50 60 Maximum heart rate 170 168 153 147 136 (y beats per minute) Find the linear correlation coefficient for the data.
Match the correlation coefficients with their scatterplots. Select the letter of the scatterplot below which corresponds to the correlation coefficient. (Click on image for a larger view.) B < 1. r = -0.49 A 2. r = 0.97 D ✓ 3. r = 0.22 4. r = -0.74 A B D
Find R, R^2 and P Value
Plot the scatter plot defined by the points on the left. Then match it to the correct correlation value. {(1, 3), (1, 8), (5, 9), (8, 3), (8, 8)} {(1, 4), (3, 3), (5, 7), (7, 5), (9, 8)} r = 0.03 r = -1
Give an example of two variables that you would expect to have a correlation close to or equal to -1. Give an example of two variables that you would expect to have a correlation close to or equal to 0. Remember that survey question you wrote? Take the class responses to your survey as data related to the one variable of your question. Now, take the list of responses from the survey question that one of your classmates wrote. Write down both lists of data. (If one sample size happens to be different from the other, cut the larger sample size down to that of the smaller sample size.) Calculate the correlation between the two variables. Describe the linear relationship between these two variables. (data for number 3) How many cars have you had so far in your lifetime? 0,1,1,1,2,2,2,2,3,3,3,3,4,4,5,6,6,7,10. How many days per week do you exercise? 0,0,0,1,2,3,3,3,3,4,4,4,4,5,5,5,5,6,6,6
a. Make a scatterplot b. Describe the correlation c. Approximate the rate of change, pick any two points on the line. d. build a model

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