Simple Linear Regression Concept | Statistics Tutorial #32 | MarinStatsLectures
55.4 هزار بار بازدید -
6 سال پیش
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Simple Linear Regression: Introduction to
Simple Linear Regression: Introduction to Simple Linear Regression and Regression Models with Examples.
Linear Regression Concept and with R Lectures https://bit.ly/2z8fXg1; More Statistics and R Programming Tutorials: https://goo.gl/4vDQzT ;
►► Like to support us? You can Donate (https://bit.ly/2CWxnP2), Share our Videos, Leave us a Comment and Give us a Thumbs up! Either way We Thank You!
► In this Statistics video lecture you will learn about the important concepts and terminology of simple linear regression, and its many uses. You will also learn about the interpretation of the regression model slope and the regression model intercept.
► Here you will also learn about errors or residuals, predicted vs observed y values, slope of the regression line, and y intercept of the regression line.
► We will discuss the difference between thinking of the model as modelling individual y values versus modelling the mean value of y. This sets up the difference between a prediction interval and a confidence interval, a topic covered in a separate video.
► We describe the method of least squares (also the method of maximum likelihood), although we leave details to be covered in a separate video
► We briefly mention the concept of centering the X variable, in order to give the model intercept a more meaningful interpretation
► We discuss the difference between fitting a linear regression model to predict a Y value (a predictive model) versus fitting a linear regression model to try and estimate the effect of X on Y (an effect size model)
► We show how to use the model equation to predict a Y value for a given X value
► This statistics video tutorial is one in the series that explains the foundation for Regression Models and what they are in general. It will serve as a foundation for understanding multiple linear regression, as well as more advanced regression models (such as logistic regression, survival analysis, mixed effects models, etc)
► While we focus on the concept of regression model, you will also see the formulas for the slope and intercept, which give some insight into their interpretations, specifically how the slope of the regression line is related to Pearson's correlation coefficient
◼︎ Table on Content:
0:00:08 When to use simple linear regression for our data
0:00:27 Introducing the example to work through simple linear regression
0:00:48 What is Pearson's correlation
0:01:44 How to model Y as a linear function of X
0:02:19 George Box and His infamous quote!
0:03:36 What is linear regression, its interpretations, and limitations
0:04:01 Terminology and concepts around simple linear regression
0:05:33 Error or the Residual in linear regression model
0:06:39 Labelling the fitted regression line
0:07:24 Introducing the y-intercept and Slope of the regression line in a simple linear regression model
0:11:43 Slope of the regression line and correlation
0:13:14 What is centering the X variable in the regression model
0:15:41 Effect size model vs Predictive model
►►More to Watch:
Individual Video:
► Simple Linear Regression in R Simple Linear Regression in R | R Tut...
► Checking Linear Regression Assumptions in R Checking Linear Regression Assumption...
► Multiple Linear Regression in R: Multiple Linear Regression in R | R T...
Lecture Series:
►Linear Regression Concept and with R Lectures https://bit.ly/2z8fXg1
► Intro to Statistics Course: https://bit.ly/2SQOxDH
►Data Science with R https://bit.ly/1A1Pixc
►Getting Started with R (Series 1): https://bit.ly/2PkTneg
►Graphs and Descriptive Statistics in R (Series 2): https://bit.ly/2PkTneg
►Probability distributions in R (Series 3): https://bit.ly/2AT3wpI
►Bivariate analysis in R (Series 4): https://bit.ly/2SXvcRi
►Linear Regression in R (Series 5): https://bit.ly/1iytAtm
►ANOVA Concept and with R https://bit.ly/2zBwjgL
►Hypothesis Testing: https://bit.ly/2Ff3J9e
Follow MarinStatsLectures
Subscribe: https://goo.gl/4vDQzT
website: https://statslectures.com
Facebook:https://goo.gl/qYQavS
Twitter:https://goo.gl/393AQG
Instagram: https://goo.gl/fdPiDn
Our Team:
Content Creator: Mike Marin (B.Sc., MSc.) Senior Instructor at UBC.
Producer and Creative Manager: Ladan Hamadani (B.Sc., BA., MPH)
These videos are created by #marinstatslectures to support some courses at The University of British Columbia (UBC) (#IntroductoryStatistics and #RVideoTutorials for Health Science Research), although we make all videos available to the everyone everywhere for free.
Thanks for watching! Have fun and remember that statistics is almost as beautiful as a unicorn!
Linear Regression Concept and with R Lectures https://bit.ly/2z8fXg1; More Statistics and R Programming Tutorials: https://goo.gl/4vDQzT ;
►► Like to support us? You can Donate (https://bit.ly/2CWxnP2), Share our Videos, Leave us a Comment and Give us a Thumbs up! Either way We Thank You!
► In this Statistics video lecture you will learn about the important concepts and terminology of simple linear regression, and its many uses. You will also learn about the interpretation of the regression model slope and the regression model intercept.
► Here you will also learn about errors or residuals, predicted vs observed y values, slope of the regression line, and y intercept of the regression line.
► We will discuss the difference between thinking of the model as modelling individual y values versus modelling the mean value of y. This sets up the difference between a prediction interval and a confidence interval, a topic covered in a separate video.
► We describe the method of least squares (also the method of maximum likelihood), although we leave details to be covered in a separate video
► We briefly mention the concept of centering the X variable, in order to give the model intercept a more meaningful interpretation
► We discuss the difference between fitting a linear regression model to predict a Y value (a predictive model) versus fitting a linear regression model to try and estimate the effect of X on Y (an effect size model)
► We show how to use the model equation to predict a Y value for a given X value
► This statistics video tutorial is one in the series that explains the foundation for Regression Models and what they are in general. It will serve as a foundation for understanding multiple linear regression, as well as more advanced regression models (such as logistic regression, survival analysis, mixed effects models, etc)
► While we focus on the concept of regression model, you will also see the formulas for the slope and intercept, which give some insight into their interpretations, specifically how the slope of the regression line is related to Pearson's correlation coefficient
◼︎ Table on Content:
0:00:08 When to use simple linear regression for our data
0:00:27 Introducing the example to work through simple linear regression
0:00:48 What is Pearson's correlation
0:01:44 How to model Y as a linear function of X
0:02:19 George Box and His infamous quote!
0:03:36 What is linear regression, its interpretations, and limitations
0:04:01 Terminology and concepts around simple linear regression
0:05:33 Error or the Residual in linear regression model
0:06:39 Labelling the fitted regression line
0:07:24 Introducing the y-intercept and Slope of the regression line in a simple linear regression model
0:11:43 Slope of the regression line and correlation
0:13:14 What is centering the X variable in the regression model
0:15:41 Effect size model vs Predictive model
►►More to Watch:
Individual Video:
► Simple Linear Regression in R Simple Linear Regression in R | R Tut...
► Checking Linear Regression Assumptions in R Checking Linear Regression Assumption...
► Multiple Linear Regression in R: Multiple Linear Regression in R | R T...
Lecture Series:
►Linear Regression Concept and with R Lectures https://bit.ly/2z8fXg1
► Intro to Statistics Course: https://bit.ly/2SQOxDH
►Data Science with R https://bit.ly/1A1Pixc
►Getting Started with R (Series 1): https://bit.ly/2PkTneg
►Graphs and Descriptive Statistics in R (Series 2): https://bit.ly/2PkTneg
►Probability distributions in R (Series 3): https://bit.ly/2AT3wpI
►Bivariate analysis in R (Series 4): https://bit.ly/2SXvcRi
►Linear Regression in R (Series 5): https://bit.ly/1iytAtm
►ANOVA Concept and with R https://bit.ly/2zBwjgL
►Hypothesis Testing: https://bit.ly/2Ff3J9e
Follow MarinStatsLectures
Subscribe: https://goo.gl/4vDQzT
website: https://statslectures.com
Facebook:https://goo.gl/qYQavS
Twitter:https://goo.gl/393AQG
Instagram: https://goo.gl/fdPiDn
Our Team:
Content Creator: Mike Marin (B.Sc., MSc.) Senior Instructor at UBC.
Producer and Creative Manager: Ladan Hamadani (B.Sc., BA., MPH)
These videos are created by #marinstatslectures to support some courses at The University of British Columbia (UBC) (#IntroductoryStatistics and #RVideoTutorials for Health Science Research), although we make all videos available to the everyone everywhere for free.
Thanks for watching! Have fun and remember that statistics is almost as beautiful as a unicorn!
6 سال پیش
در تاریخ 1397/08/03 منتشر شده
است.
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