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Lets assume that the COVID-19 new case report data were collected from S.A Ministry of Health.30th of December 2019 was the first date that COVID-19 wa confirmed in S.A. The time period of data was from the 20th of January to the 27th of January 2020 (for the last 7 days). The data included the total number of new cases, date of recorded, number of new total COVID-19 cases. In this study, a medical researcher used Pearson's correlation analysis and the linear regression model to predict COVID-19 new cases based on the available data. (e.g., that Cases of infection rises linearly with number of people tested). The best-known types of regression analysis are the following: Date 2020/01/20 2020/01/21 2020/01/22 2020/01/23 2020/01/24 2020/01/25 2020/01/26 2020/01/27 Day Sunday Monday Tuesday Wednesday Thursday Friday Saturday Sunday Test No. Case Counts 1 9. 33 41 17 1 18 13 44 Find the ordinary least squares regression equation. OA. OABB,x : y-0.9649+0.0129x : y=0.0129+0.9649.x 7-1.3518+0.9649x T=0 1356+0.0598X

Lets assume that the COVID-19 new case report data were collected from S.A Ministry of Health.30th of December 2019 was the first date that COVID-19 wa confirmed in S.A. The time period of data was from the 20th of January to the 27th of January 2020 (for the last 7 days). The data included the total number of new cases, date of recorded, number of new total COVID-19 cases. In this study, a medical researcher used Pearson's correlation analysis and the linear regression model to predict COVID-19 new cases based on the available data. (e.g., that Cases of infection rises linearly with number of people tested). The best-known types of regression analysis are the following: Date 2020/01/20 2020/01/21 2020/01/22 2020/01/23 2020/01/24 2020/01/25 2020/01/26 2020/01/27 Day Sunday Monday Tuesday Wednesday Thursday Friday Saturday Sunday Test No. Case Counts 1 9. 33 41 17 1 18 13 44 Find the ordinary least squares regression equation. OA. OABB,x : y-0.9649+0.0129x : y=0.0129+0.9649.x 7-1.3518+0.9649x T=0 1356+0.0598X

MATLAB: An Introduction with Applications
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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Let's assume that the COVID-19 new case report data were collected from S.A Ministry of Health.30th of December 2019 was the first date that COVID-19 was confirmed in S.A. The time period of data was from the 20th of January to the 27th of January 2020 (for the last 7 days). The data included the total number of new cases, date of recorded, number of new total COVID-19 cases. In this study, a medical researcher used Pearson's correlation analysis and the linear regression model to predict COVID-19 new cases based on the available data. (e.g., that Cases of infection rises linearly with number of people tested). The best-known types of regression analysis are the following: Date 2020/01/20 2020/01/21 2020/01/22 2020/01/23 2020/01/24 2020/01/25 2020/01/26 2020/01/27 Day Sunday Monday Tuesday Wednesday Thursday Friday Saturday Test No. Case Counts 1. 6. 0. 0. 33 41 0. 17 1 18 13 Sunday 44 Find the ordinary least squares regression equation. A. =B +B, x i O A. y%3D0.9649+0.0129X OB. y-B +B x; §=0.0129+0.9649.x В. y=1.3518+0.9649x D.y-B T=0.1356+0.05981 1. 133
Transcribed Image Text:Let's assume that the COVID-19 new case report data were collected from S.A Ministry of Health.30th of December 2019 was the first date that COVID-19 was confirmed in S.A. The time period of data was from the 20th of January to the 27th of January 2020 (for the last 7 days). The data included the total number of new cases, date of recorded, number of new total COVID-19 cases. In this study, a medical researcher used Pearson's correlation analysis and the linear regression model to predict COVID-19 new cases based on the available data. (e.g., that Cases of infection rises linearly with number of people tested). The best-known types of regression analysis are the following: Date 2020/01/20 2020/01/21 2020/01/22 2020/01/23 2020/01/24 2020/01/25 2020/01/26 2020/01/27 Day Sunday Monday Tuesday Wednesday Thursday Friday Saturday Test No. Case Counts 1. 6. 0. 0. 33 41 0. 17 1 18 13 Sunday 44 Find the ordinary least squares regression equation. A. =B +B, x i O A. y%3D0.9649+0.0129X OB. y-B +B x; §=0.0129+0.9649.x В. y=1.3518+0.9649x D.y-B T=0.1356+0.05981 1. 133
Expert Solution
Step 1

Introduction -

Regression equation 

y=a+bx

where , 

a=y-intercept 

b=slope 

 

a=(∑y)(∑x2)-(∑x)(∑xy)n(∑x2)-(∑x)2b=n(∑xy)-(∑x)(∑y)n(∑x2)-(∑x)2

Least square regression line equation 

y^=β0+β1xwhere ,β1=slope β0=y-intercept 

 

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