The number of kilograms ofsteam used per month by achemical plant is thought to berelated to the average ambienttemperature for that month.Given the following (n=11):Show calculationsy=129.662+7.5955xs= 2.19595R2 = 90.50%R2 adj= 90.0%(1) Calculate the sum of squares forregression.(2) Calculate Sxy(3)Calculate the 95% Cl on theslope.

Given: n=11s=2.19595R2=0.9050Radj2=0.90 Regression equation:…

Answered: The number of kilograms of steam used…

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 was run to determine if there is a relationship between hours of TV watched per day (x) and number of situps a person can do (y). The results of the regression were: y=ax+b a=-1.307 b=32.392 r²=0.675684 r=-0.822 Use this to predict the number of situps a person who watches 12.5 hours of TV can do (to one decimal place)
The following are the summations of the Age(X) and Blood Pressure (Y). n = 10 ∑X = 345 ∑Y = 819 ∑XY = 47,634 ∑X2 = 20,399 a. What is the intercept of the regression formula? b. What is the slope of the regression formula? c. What is the value of Y when X = 60
A researcher is interested in finding out the factors which determined the yearly spending on family outings last year (Y, measured in dollars). She compiles data on the number of members in a family (X1), the annual income of the family (X2), and the number of times the family went out on an outing in the last year (X3). She collects data from 196 families and estimates the following regression: Y=120.45+1.54X1+2.12X2+2.12X3. Suppose β1, β2, β3, denote the population slope coefficients of X1, X2, and X3, respectively. The researcher wants to check if neither X1 nor X2 have a significant effect on Y or at least one of them has a significant effect, keeping X3 constant. She calculates the value of the F-statistic for the test with the two restrictions (H0: β1=0, β2=0 vs. H1: β1≠0 and/or β2≠0) to be 3.00. The p-value for the test will be enter your response here?
A regression was run to determine if there is a relationship between hours of TV watched per day (x) and number of situps a person can do (y). The results of the regression were: y=ax+b a=-1.328 b=37.715 r?=0.788544 r=-0.888 Use this to predict the number of situps a person who watches 0.5 hours of TV can do (to one decimal place)
A regression was run to determine if there is a relationship between hours of TV watched per day (x) and number of situps a person can do (y). The results of the regression were: ý=ax+b a=-0.873 b=26.236 r2=0.527076 r=-0.726 Use this to predict the number of situps a person who watches 11 hours of TV can do (to one decimal place)
A regression was run to determine if there is a relationship between hours of TV watched per day (x) and number of situps a person can do (y). The results of the regression were: y=ax+b a=-0.889 b=31.442 r²=0.3721 r=-0.61 Use this to predict the number of situps a person who watches 4 hours of TV can do (to one decimal place)
A regression was run to determine if there is a relationship between hours of TV watched per day (x) and number of situps a person can do (y). The results of the regression were: y=ax+b a=-0.794 b=31.311 r²=0.9801 r=-0.99 Use this to predict the number of situps a person who watches 8 hours of TV can do (to one decimal place)
A regression was run to determine if there is a relationship between hours of TV watched per day (x) and number of situps a person can do (y). The results of the regression were: y = 26.699 -0.639*x R² = 0.5 r = -0.704 Predict the number of situps a person who watches 6 hours of TV can do (round your answer to three decimal places).
A regression was run to determine if there is a relationship between hours of TV watched per day (x) and number of situps a person can do (y).The results of the regression were y=ax+b a=-1.324 b=23.586 r2=0.887364 r=-0.942 Use this to predict the number of situps a person who watches 11.5 hours of TV can do (to one decimal place)
Use B = -15.812961, and ₁ = 0.096300 to determine the equation of the estimated regression line. (Round your numerical answers to four decimal places.) ŷ =

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