Suppose you are interested in studying the relationship between the number of visitors a museumgets and the revenue of their gift shop. You take a sample of 27 different days and record thenumber of visitors and the revenue of the gift shop in dollars.Also suppose on the 27 days you sampled, the number of visitors ranged from 100 people to 600people.You get the following regression line:ŷ = 5.6x419, where ŷ is the revenue in dollars and x is the number of visitors.We also get that r≈.57.(a) Give the value of the slope of this regression line. Interpret this value in the context of thesituation.(b) Is there enough evidence for a correlation? Why or why not?(c) What does our model predict the revenue will be on a day where the museum gets 1000 visitors?Would you trust this prediction? Why or why not?(d) Suppose that the first day we visited the museum it had 220 visitors and made a revenueof 780 dollars. Calculate the residual for this data point. Make sure to include units.(e) According to our model how many visitors does the museum need to get for the gift shopto make 2000 dollars in revenue?

Regression line: A regression line is a line that best describes the behavior of a set of data. In…

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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 table below gives the age and bone density for five randomly selected women. Using this data, consider the equation of the regression line, y = bo + bjx, for predicting a woman's bone density based on her age. Keep in mind, the correlation coefficient may or may not be statistically significant for the data given. Remember, in practice, it would not be appropriate to use the regression line to make a prediction if the correlation coefficient is not statistically significant. Age 47 49 50 51 58 Bone Density 360 353 336 333 310 Table Copy Data Step 1 of 6: Find the estimated slope. Round your answer to three decimal places.
The table below gives the number of hours seven randomly selected students spent studying and their corresponding midterm exam grades. Using this data, consider the equation of the regression line, yˆ=b0+b1x, for predicting the midterm exam grade that a student will earn based on the number of hours spent studying. Keep in mind, the correlation coefficient may or may not be statistically significant for the data given. Remember, in practice, it would not be appropriate to use the regression line to make a prediction if the correlation coefficient is not statistically significant. Hours Studying 0.5 1 1.5 2 3 3.5 4.5 Midterm Grades 63 66 68 72 74 93 94 Table Step 4 of 6 : Determine if the statement "Not all points predicted by the linear model fall on the same line" is true or false.
The table below gives the number of weeks of gestation and the birth weight (in pounds) for a sample of five randomly selected babies. Using this data, consider the equation of the regression line, y based on the number of weeks of gestation. Keep in mind, the correlation coefficient may or may not be statistically significant for the data given. Remember, in practice, it would not be appropriate to use the regression line to make a prediction if the correlation coefficient is not statistically significant. bo + bjx, for predicting the birth weight of a baby Weeks of Gestation 33 35 37 39 40 Weight (in pounds) 5 6.8 7.9 8.5 9.3 Table Copy Data Step 5 of 6: Find the error prediction when x = 39. Round your answer to three decimal places.
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The table below gives the number of hours spent unsupervised each day as well as the overall grade averages for seven randomly selected middle school students. Using this data, consider the equation of the regression line, yˆ=b0+b1x, for predicting the overall grade average for a middle school student based on the number of hours spent unsupervised each day. Keep in mind, the correlation coefficient may or may not be statistically significant for the data given. Remember, in practice, it would not be appropriate to use the regression line to make a prediction if the correlation coefficient is not statistically significant. Hours Unsupervised 0.5 1 2.5 3 4 5 5.5 Overall Grades 98 95 90 79 75 69 66 Step 1 of 6: Find the estimated slope. Round your answer to three decimal places. Step 2 of 6: Find the estimated y-intercept. Round your answer to three decimal places. Step 3 of 6: Determine if the statement "Not all points predicted by the linear model fall on the…
Restaurant Digest, a famous restaurant magazine writes that the amount of tips servers get in Florida is affected by several factors including the amount of bill, number of adult diners as well as kids and the income of the diners. Use the data below to answer the following questions: Find the multiple regression model. At a level of significance of 0.03, is there a significance relationship between the amount of tip and at least one of the independent variables? What is the likely amount of tip if a group of diners spend $40.33 and had an annual income of $81,000. The diners included 4 adults and 3 kids? What percent of the variation in amount of tip is accounted for by amount of bill, annual income, number of adults and kids? Customer Amount of Tip Amount of Bill Number of adult Diners Number of kids Annual Income 1 $ 7.00 $ 48.97 5 3 100000 2 $ 4.50 $ 28.23 4 3 80000 3…
The table below gives the age and bone density for five randomly selected women. Using this data, consider the equation of the regression line, ý = bo + bjx, for predicting a woman's bone density based on her age. Keep in mind, the correlation coefficient may or may not be statistically significant for the data given. Remember, in practice, it would not be appropriate to use the regression line to make a prediction if the correlation coefficient is not statistically significant. Age 38 43 53 59 63 Bone Density 357 351 326 317 313 Table Copy Data Step 2 of 6: Find the estimated y-intercept. Round your answer to three decimal places. O Tables E Keypad Answer How to enter your answer Keyboard Shortcuts Previous step answers Submit Answer O 2020 Hawkes Learning MacBook Air DO 888 SC 80 F3 F6 FS FI & %23 2$ 6 7 8 9 2 3 Y P Q W E D F G H J K A S > C V N M command option option command ンレ 5
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The table below gives the age and bone density for five randomly selected women. Using this data, consider the equation of the regression line, yˆ=b0+b1x, for predicting a woman's bone density based on her age. Keep in mind, the correlation coefficient may or may not be statistically significant for the data given. Remember, in practice, it would not be appropriate to use the regression line to make a prediction if the correlation coefficient is not statistically significant. Age Bone Density34 35745 34148 33160 32965 325 Step 3 of 6: Determine the value of the dependent variable yˆ at x=0.
The table below gives the number of hours seven randomly selected students spent studying and their corresponding midterm exam grades. Using this data, consider the equation of the regression line, yˆ=b0+b1x, for predicting the midterm exam grade that a student will earn based on the number of hours spent studying. Keep in mind, the correlation coefficient may or may not be statistically significant for the data given. Remember, in practice, it would not be appropriate to use the regression line to make a prediction if the correlation coefficient is not statistically significant. Hours Studying 2 2.5 3 3.5 4 5 5.5 Midterm Grades 63 67 76 78 84 85 90 Table Step 6 of 6 : Find the value of the coefficient of determination. Round your answer to three decimal places.
The table below gives the age and bone density for five randomly selected women. Using this data, consider the equation of the regression line, yˆ=b0+b1x, for predicting a woman's bone density based on her age. Keep in mind, the correlation coefficient may or may not be statistically significant for the data given. Remember, in practice, it would not be appropriate to use the regression line to make a prediction if the correlation coefficient is not statistically significant. Age Bone Density34 35745 34148 33160 32965 325 Step 2 of 6: Find the estimated y-intercept. Round your answer to three decimal places.

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