We collected teacher ratings for 25 courses taught by an instructor over a sis-yearperiod. The students ratings of the instructor are on a scale of 1 to 9. We found thatThe linear regression equation is Anerape Rating 7.88-0.068 Numer ofStudenta1interpret the slope of this model including units The average rating decreases* per each additional student2. The predicted teacher rating for a class size of 15 students using the givemprediction equation6.866.18Tiext pageCS Scanned with CamScanner

From the given output, the regression equation is, Average rating=7.88-0.068Number of students 1.…

We collected tacher ratings for 25 courses taught by an instructor over a s-year period. The students ratings of the instructor are on a scale of 1 to 9. We found that The linear regression equation is Anerape Rating 788-0.068 Numeraf Studenta 1 nterpret the slope of this model incuding

We collected tacher ratings for 25 courses taught by an instructor over a s-year period. The students ratings of the instructor are on a scale of 1 to 9. We found that The linear regression equation is Anerape Rating 788-0.068 Numeraf Studenta 1 nterpret the slope of this model incuding

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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A regression analysis was performed to determine if there is a relationship between hours of TV watched per day () and number of sit ups a person can do (y). The results of the regression were: y=ax+b a3D-0.838 b=28.783 es 72-0.784996 r=-0.886 rences borations Use this to predict the number of sit ups a person who watches 1 hours of TV can do, and please round your answer to a whole number. gle Drive ce 365 Submit Question adent Course aluations ere to search Chp ins prt sc delete f12 f9 f10 fa f5 f6 +1 f4 backspa & $4 8. 6 3 O E R T II 5
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The y-intercept in a linear regression model is always relevant and of interest to investigators for every model constructed. True False
Biochemical oxygen demand (BOD) measures organic pollutants in water by measuring the amount of oxygen consumed by microorganisms that break down these compounds. BOD is hard to measure accurately. Total organic carbon (TOC) is easy to measure, so it is common to measure TOC and use regression to predict BOD. A typical regression equation for water entering a municipal treatment plant is BOD = -55.47 + 1.505 TOC Both BOD and TOC are measured in milligrams per liter of water. (a) What does the slope of this line say about the relationship between BOD and TOC? TOC rises (falls) by 1.505 mg/l for every 55.47 mg/l increase (decrease) in BOD BOD rises (falls) by 55.47 mg/l for every 1 mg/l increase (decrease) in TOC TOC rises (falls) by 1.505 mg/l for every 1 mg/l increase (decrease) in BOD BOD rises (falls) by 1.505 mg/l for every 1 mg/l increase (decrease) in TOC
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We collected teacher ratings for 25 courses taught by an instructor over a six-vear period. The students' ratings of the instructor are on a scale of 1 to 9. We found that The linear regression equation is: Average Rating = 7,88 -0.068 Numher of Students 1. Interpret the slope of this model including units: The average rating decreases per each additional student 0.068 ed teacher rating for a class size of 15 students using the given 7.88 prediction equation is Next page CS Scanned with CamScanner
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