The data below shows the cooling temperatures of a freshly brewed cup of coffee after it is poured from thebrewing pot into a serving cup. Plot the data then determine the regression to use to answer question #27and 28. Round to 3 decimal places.Minutes, t015 811151822303438Temperature, F 179.5 168.7 158.1 149.2 141.7. 134.6 125.4 116.3 113.2 109.1How long does it for the coffee to cool down to 100°? (round to 2 decimal places)What is the temperature after 20 minutes? (round to 2 decimal places)Blank # 1Blank # 2

Answered: Image /qna-images/answer/1d84de91-1e89-44f9-9b0f-ffe75a11c17b.jpg

Answered: The data below shows the cooling…

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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Using the data, run a regression where you control for “Promotion,” and test the effect of “Wins” on “Attendance”. What should be the attendance if the number of wins is 10? Wins Promotion Attendance 4 29500 36300 6 55700 40100 6 71300 41200 8 87000 53000 6 75000 44000 7 72000 45600 5 55300 39000 7 81600 47500
The number of Master’s degrees granted at a particular university is given for selected years in the table below. Year 2005 2010 2015 2020 Number of Master’s Degrees 58 69 76 83 Use the linear regression equation for this data to predict the number of Master’s degrees that this university will grant in the year 2021. Blank 1. Calculate the answer by read surrounding text. Round to the nearest whole number of Master's degrees.
An instructor asked a random sample of eight students to record their study times at the beginning of a course. She then made a table for total hours studied (x) over 2 weeks and test score (y) at the end of the 2 weeks. The table is given below. Complete parts (a) through (f). x 10 13 10 18 6 15 16 21 y 93 79 81 74 85 81 85 80 a. Find the regression equation for the data points. b. Graph the regresson equation c. Describe the apparent relationship between the two variables. d. Identify the predictor and response variables. e. Identify outliers and potential influential observations. f.Predict the score for a student that studies for 17 hours.
The data show the chest size and weight of several bears. Find the regression equation, letting chest size be the independent (x) variable. Then find the best predicted weight of a bear with a chest size of 63 inches. Is the result close to the actual weight of 522 pounds? Use a significance level of 0.05. Chest size (inches) 58 50 65 59 59 48 D 414 312 499 450 456 260 Weight (pounds) Click the icon to view the critical values of the Pearson correlation coefficient r. What is the regression equation? y=+x (Round to one decimal place as needed.) What is the best predicted weight of a bear with a chest size of 63 inches? The best predicted weight for a bear with a chest size of 63 inches is pounds. (Round to one decimal place as needed.) Is the result close to the actual weight of 522 pounds? O A. This result is not very close to the actual weight of the bear. O B. This result is exactly the same as the actual weight of the bear. O C. This result is close to the actual weight of the…
We run the following regression using a sample of 148 women living in the western states. The variable wage is the hourly wage ( in USD) and educ is the number of years of education completed. wage = -3.07 + 1.52 educ Which statement best described the estimated slope coefficient? Group of answer choices A 1.52 extra years of education is predicted to increase wage by 1 USD An extra year of education is predicted to increase hourly wage by 1.52 USD A 1 percent increase in education is predicted to increase wage by 1.52 percent A 1.52 percent increase in education is predicted to increase wage by 1
Using the data, run a regression where you control for “Promotion,” and test the effect of “Wins” on “Attendance”. Does “Promotion” have a significant effect on “Attendance”? Wins Promotion Attendance 4 29500 36300 6 55700 40100 6 71300 41200 8 87000 53000 6 75000 44000 7 72000 45600 5 55300 39000 7 81600 47500
Use linear regression to find the equation for the linear function that best fits this data. Click Read Button For online regression calculator. Round both numbers to two decimal places. Write your final answer in a form of an equation y=mx+by=mx+b x 1 2 3 4 5 6 y 74 90 109 123 143 155

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