Annual high temperatures in a certain location have been tracked for several years. Let X represent theyear and Y the high temperature. Based on the data shown below, calculate the regression line (each valueto two decimal places).y =X +y28.25627.72730.49833.369.32.231036.71137.771237.241341.511443.581545.751646.021747.291849.261951.532051.6oIN olo

Answered: Image /qna-images/answer/08cd10fd-73f0-46de-9f62-c5fc622d275b.jpg

Answered: Annual high temperatures in a certain…

MATLAB: An Introduction with Applications

6th Edition

ISBN:9781119256830

Author:Amos Gilat

Publisher:Amos Gilat

Chapter1: Starting With Matlab

Section: Chapter Questions

Problem 1P

See similar textbooks

Similar questions

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.
Calculate two lines of regression and calculate a linear regression equation to model the data given below: years (1980,1985,1990,1995,2000) Enrolment ( 21,25,29,39,47)
The data provided give the number of standby hours based on total staff present, X₁, and remote hours, X₂. Perform a multiple regression analysis using the data provided and determine the VIF for each independent variable in the model. Is there reason to suspect the existence of collinearity? Click the icon to view the data. Determine the VIF for each independent variable in the model. | VIF ₂2 = VIF₁ = (Round to three decimal places as needed.) Is there reason to suspect the existence of collinearity? OA. No. The VIF for each independent variable is less than 5. B. Yes. The VIF for each independent variable is greater than 5. OC. No. The VIF for each independent variable is greater than 5. OD. Yes. The VIF for each independent variable is less than 5. wwwwwww Table of Data Standby Total Staff Hours Present 245 330 274 358 195 197 117 153 115 275 200 236 336 339 321 303 286 329 352 323 Remote Hours 417 655 524 385 349 350 388 153 278 479 Print Done
In simple linear regression, the coefficient of determination measures: a.The amount of variability of the y variable that is explained by the x variable b.The amount of variability of the x variable that is explained by the y variable c.The amount of variability between the treatments d.The amount of variability within the treatments
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=ax+b a=-1.34 b=36.954 r2=0.966289 r=-0.983 Use this to predict the number of situps a person who watches 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 =
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=-1.075 b=27.958 r2=0.919681 r=-0.959 Use this to predict the number of situps a person who watches 12 hours of TV can do (to one decimal place)

Recommended textbooks for you

MATLAB: An Introduction with Applications

Statistics

ISBN:

9781119256830

Author:

Amos Gilat

Publisher:

John Wiley & Sons Inc

Probability and Statistics for Engineering and th…

Statistics

ISBN:

9781305251809

Author:

Jay L. Devore

Publisher:

Cengage Learning

Statistics for The Behavioral Sciences (MindTap C…

Statistics

ISBN:

9781305504912

Author:

Frederick J Gravetter, Larry B. Wallnau

Publisher:

Cengage Learning

MATLAB: An Introduction with Applications

Statistics

ISBN:

9781119256830

Author:

Amos Gilat

Publisher:

John Wiley & Sons Inc

Probability and Statistics for Engineering and th…

Statistics

ISBN:

9781305251809

Author:

Jay L. Devore

Publisher:

Cengage Learning

Statistics for The Behavioral Sciences (MindTap C…

Statistics

ISBN:

9781305504912

Author:

Frederick J Gravetter, Larry B. Wallnau

Publisher:

Cengage Learning

Elementary Statistics: Picturing the World (7th E…

Statistics

ISBN:

9780134683416

Author:

Ron Larson, Betsy Farber

Publisher:

PEARSON

The Basic Practice of Statistics

Statistics

ISBN:

9781319042578

Author:

David S. Moore, William I. Notz, Michael A. Fligner

Publisher:

W. H. Freeman

Introduction to the Practice of Statistics

Statistics

ISBN:

9781319013387

Author:

David S. Moore, George P. McCabe, Bruce A. Craig

Publisher:

W. H. Freeman

SEE MORE TEXTBOOKS

Annual high temperatures in a certain location have been tracked for several years. Let X represent the year and Y the high temperature. Based on the data shown below, calculate the regression line (each value to two decimal places). y =