-
Linear Regression in 2 minutes
Linear Regression in 2 minutes.
---------------
Credit:
🐍 Manim and Python : https://github.com/3b1b/manim
🐵 Blender3D: https://www.blender.org/
🗒️ Emacs: https://www.gnu.org/software/emacs/
Music/Sound: www.bensound.com
This video would not have been possible without the help of Gökçe Dayanıklı.
published: 28 Nov 2021
-
An Introduction to Linear Regression Analysis
Tutorial introducing the idea of linear regression analysis and the least square method. Typically used in a statistics class.
Playlist on Linear Regression
http://www.youtube.com/course?list=ECF596A4043DBEAE9C
Like us on: http://www.facebook.com/PartyMoreStudyLess
Created by David Longstreet, Professor of the Universe, MyBookSucks
http://www.linkedin.com/in/davidlongstreet
published: 05 Feb 2012
-
Linear Regression, Clearly Explained!!!
The concepts behind linear regression, fitting a line to data with least squares and R-squared, are pretty darn simple, so let's get down to it! NOTE: This StatQuest comes with a companion video for how to do linear regression in R: https://youtu.be/u1cc1r_Y7M0
You can also find example code at the StatQuest github: https://github.com/StatQuest/linear_regression_demo
If you'd like to support StatQuest, please consider...
Patreon: https://www.patreon.com/statquest
...or...
YouTube Membership: https://www.youtube.com/channel/UCtYLUTtgS3k1Fg4y5tAhLbw/join
...buying my book, a study guide, a t-shirt or hoodie, or a song from the StatQuest store...
https://statquest.org/statquest-store/
...or just donating to StatQuest!
https://www.paypal.me/statquest
Lastly, if you want to keep up with me ...
published: 18 Nov 2022
-
Why Linear regression for Machine Learning?
Discover IBM watsonx → https://ibm.biz/learn-more-IBM-watsonx
What is linear regression? → https://ibm.biz/Bdv8x2
Regression in Machine Learning → https://ibm.biz/Bdv8xz
In the world of Artificial Intelligence, Large Language Models [LLMs] and chatbots may have the current spotlight and global attention, but for supervised learning, coders should not forget about lower compute methods for prediction. Linear Regression is a great tool for some Machine Learning tasks. In this video, IBM AI Engineer, Diarra Bell, summarizes linear regression and where it has predictive potential.
Get started for free on IBM Cloud → https://ibm.biz/sign-up-now
Subscribe to see more videos like this in the future → http://ibm.biz/subscribe-now
#ibm #ai #ml #cloud
published: 14 Feb 2024
-
Video 1: Introduction to Simple Linear Regression
We review what the main goals of regression models are, see how the linear regression models tie to the concept of linear equations, and learn to interpret the coefficients of a simple linear regression model with an example.
TABLE OF CONTENTS:
00:00 Simple Linear Regression
00:17 Objectives of Regressions
02:54 Variable’s Roles
03:30 The Magic: A Linear Equation
04:21 Linear Equation Example
05:24 Changing the Intercept
06:02 Changing the Slope
07:00 But the world is not linear!
07:44 Simple Linear Regression Model
08:25 Linear Regression Example
09:16 Data for Example
09:46 Simple Linear Regression Model
10:17 Regression Result
11:02 Interpreting the Coefficients
12:38 Estimated vs. Actual Values
published: 30 Aug 2015
-
Regression Analysis: An introduction to Linear and Logistic Regression
Regression analysis in statistics makes it possible to estimate relationships between variables. Regressions are calculated if, starting from one or more variables, a conclusion is to be drawn about another variable. The variable to which the conclusion is to be drawn is called a dependent variable (criterion). The variables used for prediction are called independent variables (predictors). This results in two areas of application for regression:
Measuring the influence of one or more variables on another variable
Prediction of a variable by one or more other variables.
Types of regression analysis
Regression analyses are divided into simple linear regression, multiple linear regression and logistic regression. Which regression analysis is used depends on the number of independent variab...
published: 02 Feb 2021
-
Linear Regression Using Least Squares Method - Line of Best Fit Equation
This statistics video tutorial explains how to find the equation of the line that best fits the observed data using the least squares method of linear regression.
Correlation Coefficient: https://www.youtube.com/watch?v=11c9cs6WpJU
Covariance and Correlation Coefficient:
https://www.youtube.com/watch?v=5wIiOFnXSYs
How To Calculate Covariance:
https://www.youtube.com/watch?v=rb2nU7YZV_I
Least Squares Method:
https://www.youtube.com/watch?v=P8hT5nDai6A
Averages and Uncertainty:
https://www.youtube.com/watch?v=CSJewgNUZKw
_____________________________
Introduction to Statistics:
https://www.youtube.com/watch?v=XZo4xyJXCak
Mean, Median...
published: 14 Jul 2020
-
Explaining linear regression
An explainer for the linear regression model and how to interpret its parameters in real-world terms.
OTHER CHANNEL LINKS
🗞️ Substack: https://verynormal.substack.com
🏪 My digital products: https://very-normal.sellfy.store
☕ Buy me a Ko-fi: https://ko-fi.com/verynormal
published: 07 Oct 2024
-
Feature Selection in Linear Regression | #datascience #facts #science #maths #fun #trending #data
Welcome to our Data Science Basics series! In this short video, we explore the importance of feature selection in linear regression. Learn how selecting the right variables helps reduce noise, prevent overfitting, and improve model performance and interpretability. Discover why focusing on the most relevant features can make a big difference in building effective linear regression models.
Key Points Covered:
What is Feature Selection?
Importance of Choosing Relevant Variables
Benefits: Reduced Noise, Prevention of Overfitting, Better Accuracy
Don’t forget to like, comment, and subscribe for more data science insights!
published: 03 Nov 2024
-
Regression: Crash Course Statistics #32
Today we're going to introduce one of the most flexible statistical tools - the General Linear Model (or GLM). GLMs allow us to create many different models to help describe the world - you see them a lot in science, economics, and politics. Today we're going to build a hypothetical model to look at the relationship between likes and comments on a trending YouTube video using the Regression Model. We'll be introducing other popular models over the next few episodes.
Crash Course is on Patreon! You can support us directly by signing up at http://www.patreon.com/crashcourse
Thanks to the following Patrons for their generous monthly contributions that help keep Crash Course free for everyone forever:
Mark Brouwer, Kenneth F Penttinen, Trevin Beattie, Satya Ridhima Parvathaneni, Erika & Al...
published: 03 Oct 2018
2:34
Linear Regression in 2 minutes
Linear Regression in 2 minutes.
---------------
Credit:
🐍 Manim and Python : https://github.com/3b1b/manim
🐵 Blender3D: https://www.blender.org/
🗒️ Emacs: ht...
Linear Regression in 2 minutes.
---------------
Credit:
🐍 Manim and Python : https://github.com/3b1b/manim
🐵 Blender3D: https://www.blender.org/
🗒️ Emacs: https://www.gnu.org/software/emacs/
Music/Sound: www.bensound.com
This video would not have been possible without the help of Gökçe Dayanıklı.
https://wn.com/Linear_Regression_In_2_Minutes
Linear Regression in 2 minutes.
---------------
Credit:
🐍 Manim and Python : https://github.com/3b1b/manim
🐵 Blender3D: https://www.blender.org/
🗒️ Emacs: https://www.gnu.org/software/emacs/
Music/Sound: www.bensound.com
This video would not have been possible without the help of Gökçe Dayanıklı.
- published: 28 Nov 2021
- views: 391836
5:18
An Introduction to Linear Regression Analysis
Tutorial introducing the idea of linear regression analysis and the least square method. Typically used in a statistics class.
Playlist on Linear Regression
h...
Tutorial introducing the idea of linear regression analysis and the least square method. Typically used in a statistics class.
Playlist on Linear Regression
http://www.youtube.com/course?list=ECF596A4043DBEAE9C
Like us on: http://www.facebook.com/PartyMoreStudyLess
Created by David Longstreet, Professor of the Universe, MyBookSucks
http://www.linkedin.com/in/davidlongstreet
https://wn.com/An_Introduction_To_Linear_Regression_Analysis
Tutorial introducing the idea of linear regression analysis and the least square method. Typically used in a statistics class.
Playlist on Linear Regression
http://www.youtube.com/course?list=ECF596A4043DBEAE9C
Like us on: http://www.facebook.com/PartyMoreStudyLess
Created by David Longstreet, Professor of the Universe, MyBookSucks
http://www.linkedin.com/in/davidlongstreet
- published: 05 Feb 2012
- views: 2150176
27:27
Linear Regression, Clearly Explained!!!
The concepts behind linear regression, fitting a line to data with least squares and R-squared, are pretty darn simple, so let's get down to it! NOTE: This Stat...
The concepts behind linear regression, fitting a line to data with least squares and R-squared, are pretty darn simple, so let's get down to it! NOTE: This StatQuest comes with a companion video for how to do linear regression in R: https://youtu.be/u1cc1r_Y7M0
You can also find example code at the StatQuest github: https://github.com/StatQuest/linear_regression_demo
If you'd like to support StatQuest, please consider...
Patreon: https://www.patreon.com/statquest
...or...
YouTube Membership: https://www.youtube.com/channel/UCtYLUTtgS3k1Fg4y5tAhLbw/join
...buying my book, a study guide, a t-shirt or hoodie, or a song from the StatQuest store...
https://statquest.org/statquest-store/
...or just donating to StatQuest!
https://www.paypal.me/statquest
Lastly, if you want to keep up with me as I research and create new StatQuests, follow me on twitter:
https://twitter.com/joshuastarmer
0:00 Awesome song and introduction
0:37 The Main Ideas!!!
1:12 Review of fitting a line to data
4:00 Review of R-squared
12:13 R-squared for a multivariable model
14:16 Why adding variables will never reduce R-squared
16:08 Calculating a p-value for R-squared
25:26 The F-distribution
Correction:
25:39 I should have (Pfit - Pmean) instead of the other way around.
#statquest #regression
https://wn.com/Linear_Regression,_Clearly_Explained
The concepts behind linear regression, fitting a line to data with least squares and R-squared, are pretty darn simple, so let's get down to it! NOTE: This StatQuest comes with a companion video for how to do linear regression in R: https://youtu.be/u1cc1r_Y7M0
You can also find example code at the StatQuest github: https://github.com/StatQuest/linear_regression_demo
If you'd like to support StatQuest, please consider...
Patreon: https://www.patreon.com/statquest
...or...
YouTube Membership: https://www.youtube.com/channel/UCtYLUTtgS3k1Fg4y5tAhLbw/join
...buying my book, a study guide, a t-shirt or hoodie, or a song from the StatQuest store...
https://statquest.org/statquest-store/
...or just donating to StatQuest!
https://www.paypal.me/statquest
Lastly, if you want to keep up with me as I research and create new StatQuests, follow me on twitter:
https://twitter.com/joshuastarmer
0:00 Awesome song and introduction
0:37 The Main Ideas!!!
1:12 Review of fitting a line to data
4:00 Review of R-squared
12:13 R-squared for a multivariable model
14:16 Why adding variables will never reduce R-squared
16:08 Calculating a p-value for R-squared
25:26 The F-distribution
Correction:
25:39 I should have (Pfit - Pmean) instead of the other way around.
#statquest #regression
- published: 18 Nov 2022
- views: 304109
3:59
Why Linear regression for Machine Learning?
Discover IBM watsonx → https://ibm.biz/learn-more-IBM-watsonx
What is linear regression? → https://ibm.biz/Bdv8x2
Regression in Machine Learning → https://ibm.b...
Discover IBM watsonx → https://ibm.biz/learn-more-IBM-watsonx
What is linear regression? → https://ibm.biz/Bdv8x2
Regression in Machine Learning → https://ibm.biz/Bdv8xz
In the world of Artificial Intelligence, Large Language Models [LLMs] and chatbots may have the current spotlight and global attention, but for supervised learning, coders should not forget about lower compute methods for prediction. Linear Regression is a great tool for some Machine Learning tasks. In this video, IBM AI Engineer, Diarra Bell, summarizes linear regression and where it has predictive potential.
Get started for free on IBM Cloud → https://ibm.biz/sign-up-now
Subscribe to see more videos like this in the future → http://ibm.biz/subscribe-now
#ibm #ai #ml #cloud
https://wn.com/Why_Linear_Regression_For_Machine_Learning
Discover IBM watsonx → https://ibm.biz/learn-more-IBM-watsonx
What is linear regression? → https://ibm.biz/Bdv8x2
Regression in Machine Learning → https://ibm.biz/Bdv8xz
In the world of Artificial Intelligence, Large Language Models [LLMs] and chatbots may have the current spotlight and global attention, but for supervised learning, coders should not forget about lower compute methods for prediction. Linear Regression is a great tool for some Machine Learning tasks. In this video, IBM AI Engineer, Diarra Bell, summarizes linear regression and where it has predictive potential.
Get started for free on IBM Cloud → https://ibm.biz/sign-up-now
Subscribe to see more videos like this in the future → http://ibm.biz/subscribe-now
#ibm #ai #ml #cloud
- published: 14 Feb 2024
- views: 31853
13:29
Video 1: Introduction to Simple Linear Regression
We review what the main goals of regression models are, see how the linear regression models tie to the concept of linear equations, and learn to interpret the ...
We review what the main goals of regression models are, see how the linear regression models tie to the concept of linear equations, and learn to interpret the coefficients of a simple linear regression model with an example.
TABLE OF CONTENTS:
00:00 Simple Linear Regression
00:17 Objectives of Regressions
02:54 Variable’s Roles
03:30 The Magic: A Linear Equation
04:21 Linear Equation Example
05:24 Changing the Intercept
06:02 Changing the Slope
07:00 But the world is not linear!
07:44 Simple Linear Regression Model
08:25 Linear Regression Example
09:16 Data for Example
09:46 Simple Linear Regression Model
10:17 Regression Result
11:02 Interpreting the Coefficients
12:38 Estimated vs. Actual Values
https://wn.com/Video_1_Introduction_To_Simple_Linear_Regression
We review what the main goals of regression models are, see how the linear regression models tie to the concept of linear equations, and learn to interpret the coefficients of a simple linear regression model with an example.
TABLE OF CONTENTS:
00:00 Simple Linear Regression
00:17 Objectives of Regressions
02:54 Variable’s Roles
03:30 The Magic: A Linear Equation
04:21 Linear Equation Example
05:24 Changing the Intercept
06:02 Changing the Slope
07:00 But the world is not linear!
07:44 Simple Linear Regression Model
08:25 Linear Regression Example
09:16 Data for Example
09:46 Simple Linear Regression Model
10:17 Regression Result
11:02 Interpreting the Coefficients
12:38 Estimated vs. Actual Values
- published: 30 Aug 2015
- views: 1782972
9:38
Regression Analysis: An introduction to Linear and Logistic Regression
Regression analysis in statistics makes it possible to estimate relationships between variables. Regressions are calculated if, starting from one or more variab...
Regression analysis in statistics makes it possible to estimate relationships between variables. Regressions are calculated if, starting from one or more variables, a conclusion is to be drawn about another variable. The variable to which the conclusion is to be drawn is called a dependent variable (criterion). The variables used for prediction are called independent variables (predictors). This results in two areas of application for regression:
Measuring the influence of one or more variables on another variable
Prediction of a variable by one or more other variables.
Types of regression analysis
Regression analyses are divided into simple linear regression, multiple linear regression and logistic regression. Which regression analysis is used depends on the number of independent variables and the scale of measurement of the dependent variable.
If you only want to use one variable for prediction, a simple regression is used. If you use more than one variable, this is multiple regression. If the dependent variable is nominally scaled, a logistic regression must be used. If it is metrically scaled, a linear regression is used. Whether linear or non-linear regression is used depends on whether or not there is a linear relationship between the independent variables and the dependent variable.
More Information about Regression:
https://datatab.net/tutorial/regression
Regression online calculator:
https://datatab.net/statistics-calculator/regression
Regression Analysis: An introduction to Linear and Logistic Regression
https://youtu.be/FLJ0yYetywE
Simple and Multiple Linear Regression
https://youtu.be/29rjWClT_3U
Assumptions of Linear Regression
https://youtu.be/sDrAoR17pNM
Logistic Regression: An Introduction
https://youtu.be/3tq4t41MsPc
Dummy Variables in Multiple Regression
https://youtu.be/bnjPzHQ04Ac
Regression with categorical independent variables
https://youtu.be/xVBwXqnWPyE
Multicollinearity
https://youtu.be/G1WX5GiFSWQ
Causality, Correlation and Regression
https://youtu.be/dhCnAO4UoiM
https://wn.com/Regression_Analysis_An_Introduction_To_Linear_And_Logistic_Regression
Regression analysis in statistics makes it possible to estimate relationships between variables. Regressions are calculated if, starting from one or more variables, a conclusion is to be drawn about another variable. The variable to which the conclusion is to be drawn is called a dependent variable (criterion). The variables used for prediction are called independent variables (predictors). This results in two areas of application for regression:
Measuring the influence of one or more variables on another variable
Prediction of a variable by one or more other variables.
Types of regression analysis
Regression analyses are divided into simple linear regression, multiple linear regression and logistic regression. Which regression analysis is used depends on the number of independent variables and the scale of measurement of the dependent variable.
If you only want to use one variable for prediction, a simple regression is used. If you use more than one variable, this is multiple regression. If the dependent variable is nominally scaled, a logistic regression must be used. If it is metrically scaled, a linear regression is used. Whether linear or non-linear regression is used depends on whether or not there is a linear relationship between the independent variables and the dependent variable.
More Information about Regression:
https://datatab.net/tutorial/regression
Regression online calculator:
https://datatab.net/statistics-calculator/regression
Regression Analysis: An introduction to Linear and Logistic Regression
https://youtu.be/FLJ0yYetywE
Simple and Multiple Linear Regression
https://youtu.be/29rjWClT_3U
Assumptions of Linear Regression
https://youtu.be/sDrAoR17pNM
Logistic Regression: An Introduction
https://youtu.be/3tq4t41MsPc
Dummy Variables in Multiple Regression
https://youtu.be/bnjPzHQ04Ac
Regression with categorical independent variables
https://youtu.be/xVBwXqnWPyE
Multicollinearity
https://youtu.be/G1WX5GiFSWQ
Causality, Correlation and Regression
https://youtu.be/dhCnAO4UoiM
- published: 02 Feb 2021
- views: 270415
15:05
Linear Regression Using Least Squares Method - Line of Best Fit Equation
This statistics video tutorial explains how to find the equation of the line that best fits the observed data using the least squares method of linear regressio...
This statistics video tutorial explains how to find the equation of the line that best fits the observed data using the least squares method of linear regression.
Correlation Coefficient: https://www.youtube.com/watch?v=11c9cs6WpJU
Covariance and Correlation Coefficient:
https://www.youtube.com/watch?v=5wIiOFnXSYs
How To Calculate Covariance:
https://www.youtube.com/watch?v=rb2nU7YZV_I
Least Squares Method:
https://www.youtube.com/watch?v=P8hT5nDai6A
Averages and Uncertainty:
https://www.youtube.com/watch?v=CSJewgNUZKw
_____________________________
Introduction to Statistics:
https://www.youtube.com/watch?v=XZo4xyJXCak
Mean, Median, Mode, & Range:
https://www.youtube.com/watch?v=A1mQ9kD-i9I
Arithmetic, Geometric, & Harmonic Mean:
https://www.youtube.com/watch?v=6G6i8vSa8Zs
Interquartile Range & Outliers:
https://www.youtube.com/watch?v=STSP8gTSdT8
Standard Deviation:
https://www.youtube.com/watch?v=IaTFpp-uzp0
_______________________________
Introduction to Probability:
https://www.youtube.com/watch?v=SkidyDQuupA
Probability Formulas:
https://www.youtube.com/watch?v=bddckR734aM
Conditional Probability:
https://www.youtube.com/watch?v=sqDVrXq_eh0
Probability Tree Diagrams:
https://www.youtube.com/watch?v=w4wKXVwtGac
Probability - Standard Normal Distributions:
https://www.youtube.com/watch?v=CjF_yQ2N638
https://wn.com/Linear_Regression_Using_Least_Squares_Method_Line_Of_Best_Fit_Equation
This statistics video tutorial explains how to find the equation of the line that best fits the observed data using the least squares method of linear regression.
Correlation Coefficient: https://www.youtube.com/watch?v=11c9cs6WpJU
Covariance and Correlation Coefficient:
https://www.youtube.com/watch?v=5wIiOFnXSYs
How To Calculate Covariance:
https://www.youtube.com/watch?v=rb2nU7YZV_I
Least Squares Method:
https://www.youtube.com/watch?v=P8hT5nDai6A
Averages and Uncertainty:
https://www.youtube.com/watch?v=CSJewgNUZKw
_____________________________
Introduction to Statistics:
https://www.youtube.com/watch?v=XZo4xyJXCak
Mean, Median, Mode, & Range:
https://www.youtube.com/watch?v=A1mQ9kD-i9I
Arithmetic, Geometric, & Harmonic Mean:
https://www.youtube.com/watch?v=6G6i8vSa8Zs
Interquartile Range & Outliers:
https://www.youtube.com/watch?v=STSP8gTSdT8
Standard Deviation:
https://www.youtube.com/watch?v=IaTFpp-uzp0
_______________________________
Introduction to Probability:
https://www.youtube.com/watch?v=SkidyDQuupA
Probability Formulas:
https://www.youtube.com/watch?v=bddckR734aM
Conditional Probability:
https://www.youtube.com/watch?v=sqDVrXq_eh0
Probability Tree Diagrams:
https://www.youtube.com/watch?v=w4wKXVwtGac
Probability - Standard Normal Distributions:
https://www.youtube.com/watch?v=CjF_yQ2N638
- published: 14 Jul 2020
- views: 1540485
15:30
Explaining linear regression
An explainer for the linear regression model and how to interpret its parameters in real-world terms.
OTHER CHANNEL LINKS
🗞️ Substack: https://verynormal.subst...
An explainer for the linear regression model and how to interpret its parameters in real-world terms.
OTHER CHANNEL LINKS
🗞️ Substack: https://verynormal.substack.com
🏪 My digital products: https://very-normal.sellfy.store
☕ Buy me a Ko-fi: https://ko-fi.com/verynormal
https://wn.com/Explaining_Linear_Regression
An explainer for the linear regression model and how to interpret its parameters in real-world terms.
OTHER CHANNEL LINKS
🗞️ Substack: https://verynormal.substack.com
🏪 My digital products: https://very-normal.sellfy.store
☕ Buy me a Ko-fi: https://ko-fi.com/verynormal
- published: 07 Oct 2024
- views: 20259
0:26
Feature Selection in Linear Regression | #datascience #facts #science #maths #fun #trending #data
Welcome to our Data Science Basics series! In this short video, we explore the importance of feature selection in linear regression. Learn how selecting the rig...
Welcome to our Data Science Basics series! In this short video, we explore the importance of feature selection in linear regression. Learn how selecting the right variables helps reduce noise, prevent overfitting, and improve model performance and interpretability. Discover why focusing on the most relevant features can make a big difference in building effective linear regression models.
Key Points Covered:
What is Feature Selection?
Importance of Choosing Relevant Variables
Benefits: Reduced Noise, Prevention of Overfitting, Better Accuracy
Don’t forget to like, comment, and subscribe for more data science insights!
https://wn.com/Feature_Selection_In_Linear_Regression_|_Datascience_Facts_Science_Maths_Fun_Trending_Data
Welcome to our Data Science Basics series! In this short video, we explore the importance of feature selection in linear regression. Learn how selecting the right variables helps reduce noise, prevent overfitting, and improve model performance and interpretability. Discover why focusing on the most relevant features can make a big difference in building effective linear regression models.
Key Points Covered:
What is Feature Selection?
Importance of Choosing Relevant Variables
Benefits: Reduced Noise, Prevention of Overfitting, Better Accuracy
Don’t forget to like, comment, and subscribe for more data science insights!
- published: 03 Nov 2024
- views: 422
12:40
Regression: Crash Course Statistics #32
Today we're going to introduce one of the most flexible statistical tools - the General Linear Model (or GLM). GLMs allow us to create many different models to ...
Today we're going to introduce one of the most flexible statistical tools - the General Linear Model (or GLM). GLMs allow us to create many different models to help describe the world - you see them a lot in science, economics, and politics. Today we're going to build a hypothetical model to look at the relationship between likes and comments on a trending YouTube video using the Regression Model. We'll be introducing other popular models over the next few episodes.
Crash Course is on Patreon! You can support us directly by signing up at http://www.patreon.com/crashcourse
Thanks to the following Patrons for their generous monthly contributions that help keep Crash Course free for everyone forever:
Mark Brouwer, Kenneth F Penttinen, Trevin Beattie, Satya Ridhima Parvathaneni, Erika & Alexa Saur, Glenn Elliott, Justin Zingsheim, Jessica Wode, Eric Prestemon, Kathrin Benoit, Tom Trval, Jason Saslow, Nathan Taylor, Brian Thomas Gossett, Khaled El Shalakany, Indika Siriwardena, SR Foxley, Sam Ferguson, Yasenia Cruz, Eric Koslow, Caleb Weeks, D.A. Noe, Shawn Arnold, Malcolm Callis, Advait Shinde, William McGraw, Andrei Krishkevich, Rachel Bright, Mayumi Maeda, Kathy & Tim Philip, Jirat, Ian Dundore
--
Want to find Crash Course elsewhere on the internet?
Facebook - http://www.facebook.com/YouTubeCrashCourse
Twitter - http://www.twitter.com/TheCrashCourse
Tumblr - http://thecrashcourse.tumblr.com
Support Crash Course on Patreon: http://patreon.com/crashcourse
CC Kids: http://www.youtube.com/crashcoursekids
https://wn.com/Regression_Crash_Course_Statistics_32
Today we're going to introduce one of the most flexible statistical tools - the General Linear Model (or GLM). GLMs allow us to create many different models to help describe the world - you see them a lot in science, economics, and politics. Today we're going to build a hypothetical model to look at the relationship between likes and comments on a trending YouTube video using the Regression Model. We'll be introducing other popular models over the next few episodes.
Crash Course is on Patreon! You can support us directly by signing up at http://www.patreon.com/crashcourse
Thanks to the following Patrons for their generous monthly contributions that help keep Crash Course free for everyone forever:
Mark Brouwer, Kenneth F Penttinen, Trevin Beattie, Satya Ridhima Parvathaneni, Erika & Alexa Saur, Glenn Elliott, Justin Zingsheim, Jessica Wode, Eric Prestemon, Kathrin Benoit, Tom Trval, Jason Saslow, Nathan Taylor, Brian Thomas Gossett, Khaled El Shalakany, Indika Siriwardena, SR Foxley, Sam Ferguson, Yasenia Cruz, Eric Koslow, Caleb Weeks, D.A. Noe, Shawn Arnold, Malcolm Callis, Advait Shinde, William McGraw, Andrei Krishkevich, Rachel Bright, Mayumi Maeda, Kathy & Tim Philip, Jirat, Ian Dundore
--
Want to find Crash Course elsewhere on the internet?
Facebook - http://www.facebook.com/YouTubeCrashCourse
Twitter - http://www.twitter.com/TheCrashCourse
Tumblr - http://thecrashcourse.tumblr.com
Support Crash Course on Patreon: http://patreon.com/crashcourse
CC Kids: http://www.youtube.com/crashcoursekids
- published: 03 Oct 2018
- views: 717883
-
Regression analysis
This animation provides an explanation for how regression analysis can be used to examine the relationship between two or more variables.
This video is part of a series of CLOSER animations introducing different statistical methods and methodological approaches to analyse longitudinal data. The four animations in this series focus on the following topics:
- Introduction to analysing longitudinal data
- Regression analysis
- Multi-level and growth curve analysis
- Survival analysis
CLOSER Learning Hub
These animations are featured on the CLOSER Learning Hub (https://learning.closer.ac.uk), our online educational resource which aims to help those new to longitudinal studies better understand the value of these studies and how to use the data.
About CLOSER
CLOSER (https://closer.ac.uk) ...
published: 16 Feb 2021
-
Regression Analysis: An introduction to Linear and Logistic Regression
Regression analysis in statistics makes it possible to estimate relationships between variables. Regressions are calculated if, starting from one or more variables, a conclusion is to be drawn about another variable. The variable to which the conclusion is to be drawn is called a dependent variable (criterion). The variables used for prediction are called independent variables (predictors). This results in two areas of application for regression:
Measuring the influence of one or more variables on another variable
Prediction of a variable by one or more other variables.
Types of regression analysis
Regression analyses are divided into simple linear regression, multiple linear regression and logistic regression. Which regression analysis is used depends on the number of independent variab...
published: 02 Feb 2021
-
Regression Analysis | Full Course
After watching this full lecture about Regression, you will know what regression analysis is and what the difference between simple and multiple linear regression is. Further you will be able to interpret your results, understand the assumptions for a linear regression and the use of Dummy variables.
And at the end I explain to you the basics of logistic regression. And of course, I'll also show you how to calculate a regression easily online!
With a Regression analysis you can measure the influence of one variable or several variables on another variable, or you can predict a variable based on other variables.
► Load Example Dataset: Calculate the example directly with DATAtab for free
https://datatab.net/statistics-calculator/regression?example=linear_regression
► Online Regression C...
published: 05 Nov 2021
-
An Introduction to Linear Regression Analysis
Tutorial introducing the idea of linear regression analysis and the least square method. Typically used in a statistics class.
Playlist on Linear Regression
http://www.youtube.com/course?list=ECF596A4043DBEAE9C
Like us on: http://www.facebook.com/PartyMoreStudyLess
Created by David Longstreet, Professor of the Universe, MyBookSucks
http://www.linkedin.com/in/davidlongstreet
published: 05 Feb 2012
-
Regression: Crash Course Statistics #32
Today we're going to introduce one of the most flexible statistical tools - the General Linear Model (or GLM). GLMs allow us to create many different models to help describe the world - you see them a lot in science, economics, and politics. Today we're going to build a hypothetical model to look at the relationship between likes and comments on a trending YouTube video using the Regression Model. We'll be introducing other popular models over the next few episodes.
Crash Course is on Patreon! You can support us directly by signing up at http://www.patreon.com/crashcourse
Thanks to the following Patrons for their generous monthly contributions that help keep Crash Course free for everyone forever:
Mark Brouwer, Kenneth F Penttinen, Trevin Beattie, Satya Ridhima Parvathaneni, Erika & Al...
published: 03 Oct 2018
-
Regression Analysis | Data Science Tutorial | Simplilearn
🔥 Post Graduate Program In Data Science: https://www.simplilearn.com/post-graduate-program-data-science?utm_campaign=RegressionAnalysis-DtOYBxi4AIE&utm_medium=DescriptionFirstFold&utm_source=youtube
🔥 Data Science Bootcamp (US Only): https://www.simplilearn.com/data-science-bootcamp?utm_campaign=RegressionAnalysis-DtOYBxi4AIE&utm_medium=DescriptionFirstFold&utm_source=youtube
🟡 Caltech AI & Machine Learning Bootcamp (For US Learners Only) - https://www.simplilearn.com/ai-machine-learning-bootcamp?utm_campaign=RegressionAnalysis-DtOYBxi4AIE&utm_medium=DescriptionFirstFold&utm_source=youtube
In statistical modeling, regression analysis is a statistical process for estimating the relationships among variables. It includes many techniques for modeling and analyzing several variables when the ...
published: 11 Aug 2017
-
Video 1: Introduction to Simple Linear Regression
We review what the main goals of regression models are, see how the linear regression models tie to the concept of linear equations, and learn to interpret the coefficients of a simple linear regression model with an example.
TABLE OF CONTENTS:
00:00 Simple Linear Regression
00:17 Objectives of Regressions
02:54 Variable’s Roles
03:30 The Magic: A Linear Equation
04:21 Linear Equation Example
05:24 Changing the Intercept
06:02 Changing the Slope
07:00 But the world is not linear!
07:44 Simple Linear Regression Model
08:25 Linear Regression Example
09:16 Data for Example
09:46 Simple Linear Regression Model
10:17 Regression Result
11:02 Interpreting the Coefficients
12:38 Estimated vs. Actual Values
published: 30 Aug 2015
-
6. Regression Analysis
MIT 18.S096 Topics in Mathematics with Applications in Finance, Fall 2013
View the complete course: http://ocw.mit.edu/18-S096F13
Instructor: Peter Kempthorne
This lecture introduces the mathematical and statistical foundations of regression analysis, particularly linear regression.
License: Creative Commons BY-NC-SA
More information at http://ocw.mit.edu/terms
More courses at http://ocw.mit.edu
published: 06 Jan 2015
-
Lecture 3 - Understanding Regression Analysis, Diagnostics and SEM Mediation & Moderation in R
Regression Analysis and SEM
Welcome to the 3rd session of R Hands-on Training. In this session we will be covering following activities
• Background Materials
o Concepts of #regression analysis - https://youtu.be/kgHRuFKA0HY
o Theoretical discussion on simple regression - https://youtu.be/eI9vVBDItNE
o regression in decision making - https://youtu.be/-GpCWo1Lfk0
o Statistical reason to do regression – experimental design - https://youtu.be/BS_aDBH3mMY
o simple regression in excel - https://youtu.be/VsLYmrGfnes
o Simple regression in Stata - https://youtu.be/IN3QPwoN0eU
o Simple regression in SPSS - https://youtu.be/SOLqcTor5FQ
o simple regression in EViews - https://youtu.be/_1nGxDaELXI
o Simple regression in Python https://youtu.be/enwHAkpU3ek
• Use of R notebook and chunks
o...
published: 20 Aug 2023
-
Learn Statistical Regression in 40 mins! My best video ever. Legit.
See all my videos at: https://www.zstatistics.com/videos
0:00 Introduction
2:46 Objectives of regression
4:43 Population regression equation
9:34 Sample regression line
18:51 SSR/SSE/SST
26:25 R-squared
29:00 Degrees of freedom and adjusted R-squared
published: 22 May 2023
3:51
Regression analysis
This animation provides an explanation for how regression analysis can be used to examine the relationship between two or more variables.
This video is part o...
This animation provides an explanation for how regression analysis can be used to examine the relationship between two or more variables.
This video is part of a series of CLOSER animations introducing different statistical methods and methodological approaches to analyse longitudinal data. The four animations in this series focus on the following topics:
- Introduction to analysing longitudinal data
- Regression analysis
- Multi-level and growth curve analysis
- Survival analysis
CLOSER Learning Hub
These animations are featured on the CLOSER Learning Hub (https://learning.closer.ac.uk), our online educational resource which aims to help those new to longitudinal studies better understand the value of these studies and how to use the data.
About CLOSER
CLOSER (https://closer.ac.uk) is a consortium of world-leading longitudinal studies following participants born throughout the 20th and 21st centuries. Our work maximises the use, value and impact of these, and other, longitudinal studies to help improve our understanding of key social and biomedical challenges.
https://wn.com/Regression_Analysis
This animation provides an explanation for how regression analysis can be used to examine the relationship between two or more variables.
This video is part of a series of CLOSER animations introducing different statistical methods and methodological approaches to analyse longitudinal data. The four animations in this series focus on the following topics:
- Introduction to analysing longitudinal data
- Regression analysis
- Multi-level and growth curve analysis
- Survival analysis
CLOSER Learning Hub
These animations are featured on the CLOSER Learning Hub (https://learning.closer.ac.uk), our online educational resource which aims to help those new to longitudinal studies better understand the value of these studies and how to use the data.
About CLOSER
CLOSER (https://closer.ac.uk) is a consortium of world-leading longitudinal studies following participants born throughout the 20th and 21st centuries. Our work maximises the use, value and impact of these, and other, longitudinal studies to help improve our understanding of key social and biomedical challenges.
- published: 16 Feb 2021
- views: 84977
9:38
Regression Analysis: An introduction to Linear and Logistic Regression
Regression analysis in statistics makes it possible to estimate relationships between variables. Regressions are calculated if, starting from one or more variab...
Regression analysis in statistics makes it possible to estimate relationships between variables. Regressions are calculated if, starting from one or more variables, a conclusion is to be drawn about another variable. The variable to which the conclusion is to be drawn is called a dependent variable (criterion). The variables used for prediction are called independent variables (predictors). This results in two areas of application for regression:
Measuring the influence of one or more variables on another variable
Prediction of a variable by one or more other variables.
Types of regression analysis
Regression analyses are divided into simple linear regression, multiple linear regression and logistic regression. Which regression analysis is used depends on the number of independent variables and the scale of measurement of the dependent variable.
If you only want to use one variable for prediction, a simple regression is used. If you use more than one variable, this is multiple regression. If the dependent variable is nominally scaled, a logistic regression must be used. If it is metrically scaled, a linear regression is used. Whether linear or non-linear regression is used depends on whether or not there is a linear relationship between the independent variables and the dependent variable.
More Information about Regression:
https://datatab.net/tutorial/regression
Regression online calculator:
https://datatab.net/statistics-calculator/regression
Regression Analysis: An introduction to Linear and Logistic Regression
https://youtu.be/FLJ0yYetywE
Simple and Multiple Linear Regression
https://youtu.be/29rjWClT_3U
Assumptions of Linear Regression
https://youtu.be/sDrAoR17pNM
Logistic Regression: An Introduction
https://youtu.be/3tq4t41MsPc
Dummy Variables in Multiple Regression
https://youtu.be/bnjPzHQ04Ac
Regression with categorical independent variables
https://youtu.be/xVBwXqnWPyE
Multicollinearity
https://youtu.be/G1WX5GiFSWQ
Causality, Correlation and Regression
https://youtu.be/dhCnAO4UoiM
https://wn.com/Regression_Analysis_An_Introduction_To_Linear_And_Logistic_Regression
Regression analysis in statistics makes it possible to estimate relationships between variables. Regressions are calculated if, starting from one or more variables, a conclusion is to be drawn about another variable. The variable to which the conclusion is to be drawn is called a dependent variable (criterion). The variables used for prediction are called independent variables (predictors). This results in two areas of application for regression:
Measuring the influence of one or more variables on another variable
Prediction of a variable by one or more other variables.
Types of regression analysis
Regression analyses are divided into simple linear regression, multiple linear regression and logistic regression. Which regression analysis is used depends on the number of independent variables and the scale of measurement of the dependent variable.
If you only want to use one variable for prediction, a simple regression is used. If you use more than one variable, this is multiple regression. If the dependent variable is nominally scaled, a logistic regression must be used. If it is metrically scaled, a linear regression is used. Whether linear or non-linear regression is used depends on whether or not there is a linear relationship between the independent variables and the dependent variable.
More Information about Regression:
https://datatab.net/tutorial/regression
Regression online calculator:
https://datatab.net/statistics-calculator/regression
Regression Analysis: An introduction to Linear and Logistic Regression
https://youtu.be/FLJ0yYetywE
Simple and Multiple Linear Regression
https://youtu.be/29rjWClT_3U
Assumptions of Linear Regression
https://youtu.be/sDrAoR17pNM
Logistic Regression: An Introduction
https://youtu.be/3tq4t41MsPc
Dummy Variables in Multiple Regression
https://youtu.be/bnjPzHQ04Ac
Regression with categorical independent variables
https://youtu.be/xVBwXqnWPyE
Multicollinearity
https://youtu.be/G1WX5GiFSWQ
Causality, Correlation and Regression
https://youtu.be/dhCnAO4UoiM
- published: 02 Feb 2021
- views: 270415
45:17
Regression Analysis | Full Course
After watching this full lecture about Regression, you will know what regression analysis is and what the difference between simple and multiple linear regressi...
After watching this full lecture about Regression, you will know what regression analysis is and what the difference between simple and multiple linear regression is. Further you will be able to interpret your results, understand the assumptions for a linear regression and the use of Dummy variables.
And at the end I explain to you the basics of logistic regression. And of course, I'll also show you how to calculate a regression easily online!
With a Regression analysis you can measure the influence of one variable or several variables on another variable, or you can predict a variable based on other variables.
► Load Example Dataset: Calculate the example directly with DATAtab for free
https://datatab.net/statistics-calculator/regression?example=linear_regression
► Online Regression Calculator
https://datatab.net/statistics-calculator/regression
► E-BOOK
https://datatab.net/statistics-book
► Tutorial Regression
https://datatab.net/tutorial/regression
► Tutorial Linear Regression
https://datatab.net/tutorial/linear-regression
► Tutorial Logistic Regression
https://datatab.net/tutorial/logistic-regression
0:00 Introduction
0:41 What is a Regression?
8:10 Linear Regression
16:48 Interpret the results of linear Regession
23:01 Assumptions for a linear regression
31:30 Dummy variables
37:47 Logistic Regression
https://wn.com/Regression_Analysis_|_Full_Course
After watching this full lecture about Regression, you will know what regression analysis is and what the difference between simple and multiple linear regression is. Further you will be able to interpret your results, understand the assumptions for a linear regression and the use of Dummy variables.
And at the end I explain to you the basics of logistic regression. And of course, I'll also show you how to calculate a regression easily online!
With a Regression analysis you can measure the influence of one variable or several variables on another variable, or you can predict a variable based on other variables.
► Load Example Dataset: Calculate the example directly with DATAtab for free
https://datatab.net/statistics-calculator/regression?example=linear_regression
► Online Regression Calculator
https://datatab.net/statistics-calculator/regression
► E-BOOK
https://datatab.net/statistics-book
► Tutorial Regression
https://datatab.net/tutorial/regression
► Tutorial Linear Regression
https://datatab.net/tutorial/linear-regression
► Tutorial Logistic Regression
https://datatab.net/tutorial/logistic-regression
0:00 Introduction
0:41 What is a Regression?
8:10 Linear Regression
16:48 Interpret the results of linear Regession
23:01 Assumptions for a linear regression
31:30 Dummy variables
37:47 Logistic Regression
- published: 05 Nov 2021
- views: 789142
5:18
An Introduction to Linear Regression Analysis
Tutorial introducing the idea of linear regression analysis and the least square method. Typically used in a statistics class.
Playlist on Linear Regression
h...
Tutorial introducing the idea of linear regression analysis and the least square method. Typically used in a statistics class.
Playlist on Linear Regression
http://www.youtube.com/course?list=ECF596A4043DBEAE9C
Like us on: http://www.facebook.com/PartyMoreStudyLess
Created by David Longstreet, Professor of the Universe, MyBookSucks
http://www.linkedin.com/in/davidlongstreet
https://wn.com/An_Introduction_To_Linear_Regression_Analysis
Tutorial introducing the idea of linear regression analysis and the least square method. Typically used in a statistics class.
Playlist on Linear Regression
http://www.youtube.com/course?list=ECF596A4043DBEAE9C
Like us on: http://www.facebook.com/PartyMoreStudyLess
Created by David Longstreet, Professor of the Universe, MyBookSucks
http://www.linkedin.com/in/davidlongstreet
- published: 05 Feb 2012
- views: 2150176
12:40
Regression: Crash Course Statistics #32
Today we're going to introduce one of the most flexible statistical tools - the General Linear Model (or GLM). GLMs allow us to create many different models to ...
Today we're going to introduce one of the most flexible statistical tools - the General Linear Model (or GLM). GLMs allow us to create many different models to help describe the world - you see them a lot in science, economics, and politics. Today we're going to build a hypothetical model to look at the relationship between likes and comments on a trending YouTube video using the Regression Model. We'll be introducing other popular models over the next few episodes.
Crash Course is on Patreon! You can support us directly by signing up at http://www.patreon.com/crashcourse
Thanks to the following Patrons for their generous monthly contributions that help keep Crash Course free for everyone forever:
Mark Brouwer, Kenneth F Penttinen, Trevin Beattie, Satya Ridhima Parvathaneni, Erika & Alexa Saur, Glenn Elliott, Justin Zingsheim, Jessica Wode, Eric Prestemon, Kathrin Benoit, Tom Trval, Jason Saslow, Nathan Taylor, Brian Thomas Gossett, Khaled El Shalakany, Indika Siriwardena, SR Foxley, Sam Ferguson, Yasenia Cruz, Eric Koslow, Caleb Weeks, D.A. Noe, Shawn Arnold, Malcolm Callis, Advait Shinde, William McGraw, Andrei Krishkevich, Rachel Bright, Mayumi Maeda, Kathy & Tim Philip, Jirat, Ian Dundore
--
Want to find Crash Course elsewhere on the internet?
Facebook - http://www.facebook.com/YouTubeCrashCourse
Twitter - http://www.twitter.com/TheCrashCourse
Tumblr - http://thecrashcourse.tumblr.com
Support Crash Course on Patreon: http://patreon.com/crashcourse
CC Kids: http://www.youtube.com/crashcoursekids
https://wn.com/Regression_Crash_Course_Statistics_32
Today we're going to introduce one of the most flexible statistical tools - the General Linear Model (or GLM). GLMs allow us to create many different models to help describe the world - you see them a lot in science, economics, and politics. Today we're going to build a hypothetical model to look at the relationship between likes and comments on a trending YouTube video using the Regression Model. We'll be introducing other popular models over the next few episodes.
Crash Course is on Patreon! You can support us directly by signing up at http://www.patreon.com/crashcourse
Thanks to the following Patrons for their generous monthly contributions that help keep Crash Course free for everyone forever:
Mark Brouwer, Kenneth F Penttinen, Trevin Beattie, Satya Ridhima Parvathaneni, Erika & Alexa Saur, Glenn Elliott, Justin Zingsheim, Jessica Wode, Eric Prestemon, Kathrin Benoit, Tom Trval, Jason Saslow, Nathan Taylor, Brian Thomas Gossett, Khaled El Shalakany, Indika Siriwardena, SR Foxley, Sam Ferguson, Yasenia Cruz, Eric Koslow, Caleb Weeks, D.A. Noe, Shawn Arnold, Malcolm Callis, Advait Shinde, William McGraw, Andrei Krishkevich, Rachel Bright, Mayumi Maeda, Kathy & Tim Philip, Jirat, Ian Dundore
--
Want to find Crash Course elsewhere on the internet?
Facebook - http://www.facebook.com/YouTubeCrashCourse
Twitter - http://www.twitter.com/TheCrashCourse
Tumblr - http://thecrashcourse.tumblr.com
Support Crash Course on Patreon: http://patreon.com/crashcourse
CC Kids: http://www.youtube.com/crashcoursekids
- published: 03 Oct 2018
- views: 717883
6:50
Regression Analysis | Data Science Tutorial | Simplilearn
🔥 Post Graduate Program In Data Science: https://www.simplilearn.com/post-graduate-program-data-science?utm_campaign=RegressionAnalysis-DtOYBxi4AIE&utm_medium=D...
🔥 Post Graduate Program In Data Science: https://www.simplilearn.com/post-graduate-program-data-science?utm_campaign=RegressionAnalysis-DtOYBxi4AIE&utm_medium=DescriptionFirstFold&utm_source=youtube
🔥 Data Science Bootcamp (US Only): https://www.simplilearn.com/data-science-bootcamp?utm_campaign=RegressionAnalysis-DtOYBxi4AIE&utm_medium=DescriptionFirstFold&utm_source=youtube
🟡 Caltech AI & Machine Learning Bootcamp (For US Learners Only) - https://www.simplilearn.com/ai-machine-learning-bootcamp?utm_campaign=RegressionAnalysis-DtOYBxi4AIE&utm_medium=DescriptionFirstFold&utm_source=youtube
In statistical modeling, regression analysis is a statistical process for estimating the relationships among variables. It includes many techniques for modeling and analyzing several variables when the focus is on the relationship between a dependent variable and one or more independent variables (or 'predictors').
#datascience #datasciencetutorial #datascienceforbeginners #datasciencewithr #datasciencetutorialforbeginners #datasciencecourse
➡️ About Caltech Post Graduate Program In Data Science
This Post Graduation in Data Science leverages the superiority of Caltech's academic eminence. The Data Science program covers critical Data Science topics like Python programming, R programming, Machine Learning, Deep Learning, and Data Visualization tools through an interactive learning model with live sessions by global practitioners and practical labs.
✅ Key Features
- Simplilearn's JobAssist helps you get noticed by top hiring companies
- Caltech PG program in Data Science completion certificate
- Earn up to 14 CEUs from Caltech CTME
- Masterclasses delivered by distinguished Caltech faculty and IBM experts
- Caltech CTME Circle membership
- Online convocation by Caltech CTME Program Director
- IBM certificates for IBM courses
- Access to hackathons and Ask Me Anything sessions from IBM
- 25+ hands-on projects from the likes of Amazon, Walmart, Uber, and many more
- Seamless access to integrated labs
- Capstone projects in 3 domains
- Simplilearn’s Career Assistance to help you get noticed by top hiring companies
- 8X higher interaction in live online classes by industry experts
✅ Skills Covered
- Exploratory Data Analysis
- Descriptive Statistics
- Inferential Statistics
- Model Building and Fine Tuning
- Supervised and Unsupervised Learning
- Ensemble Learning
- Deep Learning
- Data Visualization
Data Science Certification Training - R Programming: https://www.simplilearn.com/big-data-and-analytics/data-scientist-certification-sas-r-excel-training?utm_campaign=Regression-Data-Science-DtOYBxi4AIE&utm_medium=SC&utm_source=youtube
🔥🔥 Interested in Attending Live Classes? Call Us: IN - 18002127688 / US - +18445327688
https://wn.com/Regression_Analysis_|_Data_Science_Tutorial_|_Simplilearn
🔥 Post Graduate Program In Data Science: https://www.simplilearn.com/post-graduate-program-data-science?utm_campaign=RegressionAnalysis-DtOYBxi4AIE&utm_medium=DescriptionFirstFold&utm_source=youtube
🔥 Data Science Bootcamp (US Only): https://www.simplilearn.com/data-science-bootcamp?utm_campaign=RegressionAnalysis-DtOYBxi4AIE&utm_medium=DescriptionFirstFold&utm_source=youtube
🟡 Caltech AI & Machine Learning Bootcamp (For US Learners Only) - https://www.simplilearn.com/ai-machine-learning-bootcamp?utm_campaign=RegressionAnalysis-DtOYBxi4AIE&utm_medium=DescriptionFirstFold&utm_source=youtube
In statistical modeling, regression analysis is a statistical process for estimating the relationships among variables. It includes many techniques for modeling and analyzing several variables when the focus is on the relationship between a dependent variable and one or more independent variables (or 'predictors').
#datascience #datasciencetutorial #datascienceforbeginners #datasciencewithr #datasciencetutorialforbeginners #datasciencecourse
➡️ About Caltech Post Graduate Program In Data Science
This Post Graduation in Data Science leverages the superiority of Caltech's academic eminence. The Data Science program covers critical Data Science topics like Python programming, R programming, Machine Learning, Deep Learning, and Data Visualization tools through an interactive learning model with live sessions by global practitioners and practical labs.
✅ Key Features
- Simplilearn's JobAssist helps you get noticed by top hiring companies
- Caltech PG program in Data Science completion certificate
- Earn up to 14 CEUs from Caltech CTME
- Masterclasses delivered by distinguished Caltech faculty and IBM experts
- Caltech CTME Circle membership
- Online convocation by Caltech CTME Program Director
- IBM certificates for IBM courses
- Access to hackathons and Ask Me Anything sessions from IBM
- 25+ hands-on projects from the likes of Amazon, Walmart, Uber, and many more
- Seamless access to integrated labs
- Capstone projects in 3 domains
- Simplilearn’s Career Assistance to help you get noticed by top hiring companies
- 8X higher interaction in live online classes by industry experts
✅ Skills Covered
- Exploratory Data Analysis
- Descriptive Statistics
- Inferential Statistics
- Model Building and Fine Tuning
- Supervised and Unsupervised Learning
- Ensemble Learning
- Deep Learning
- Data Visualization
Data Science Certification Training - R Programming: https://www.simplilearn.com/big-data-and-analytics/data-scientist-certification-sas-r-excel-training?utm_campaign=Regression-Data-Science-DtOYBxi4AIE&utm_medium=SC&utm_source=youtube
🔥🔥 Interested in Attending Live Classes? Call Us: IN - 18002127688 / US - +18445327688
- published: 11 Aug 2017
- views: 119966
13:29
Video 1: Introduction to Simple Linear Regression
We review what the main goals of regression models are, see how the linear regression models tie to the concept of linear equations, and learn to interpret the ...
We review what the main goals of regression models are, see how the linear regression models tie to the concept of linear equations, and learn to interpret the coefficients of a simple linear regression model with an example.
TABLE OF CONTENTS:
00:00 Simple Linear Regression
00:17 Objectives of Regressions
02:54 Variable’s Roles
03:30 The Magic: A Linear Equation
04:21 Linear Equation Example
05:24 Changing the Intercept
06:02 Changing the Slope
07:00 But the world is not linear!
07:44 Simple Linear Regression Model
08:25 Linear Regression Example
09:16 Data for Example
09:46 Simple Linear Regression Model
10:17 Regression Result
11:02 Interpreting the Coefficients
12:38 Estimated vs. Actual Values
https://wn.com/Video_1_Introduction_To_Simple_Linear_Regression
We review what the main goals of regression models are, see how the linear regression models tie to the concept of linear equations, and learn to interpret the coefficients of a simple linear regression model with an example.
TABLE OF CONTENTS:
00:00 Simple Linear Regression
00:17 Objectives of Regressions
02:54 Variable’s Roles
03:30 The Magic: A Linear Equation
04:21 Linear Equation Example
05:24 Changing the Intercept
06:02 Changing the Slope
07:00 But the world is not linear!
07:44 Simple Linear Regression Model
08:25 Linear Regression Example
09:16 Data for Example
09:46 Simple Linear Regression Model
10:17 Regression Result
11:02 Interpreting the Coefficients
12:38 Estimated vs. Actual Values
- published: 30 Aug 2015
- views: 1782972
1:22:13
6. Regression Analysis
MIT 18.S096 Topics in Mathematics with Applications in Finance, Fall 2013
View the complete course: http://ocw.mit.edu/18-S096F13
Instructor: Peter Kempthorne
...
MIT 18.S096 Topics in Mathematics with Applications in Finance, Fall 2013
View the complete course: http://ocw.mit.edu/18-S096F13
Instructor: Peter Kempthorne
This lecture introduces the mathematical and statistical foundations of regression analysis, particularly linear regression.
License: Creative Commons BY-NC-SA
More information at http://ocw.mit.edu/terms
More courses at http://ocw.mit.edu
https://wn.com/6._Regression_Analysis
MIT 18.S096 Topics in Mathematics with Applications in Finance, Fall 2013
View the complete course: http://ocw.mit.edu/18-S096F13
Instructor: Peter Kempthorne
This lecture introduces the mathematical and statistical foundations of regression analysis, particularly linear regression.
License: Creative Commons BY-NC-SA
More information at http://ocw.mit.edu/terms
More courses at http://ocw.mit.edu
- published: 06 Jan 2015
- views: 186026
1:17:19
Lecture 3 - Understanding Regression Analysis, Diagnostics and SEM Mediation & Moderation in R
Regression Analysis and SEM
Welcome to the 3rd session of R Hands-on Training. In this session we will be covering following activities
• Background Materials
...
Regression Analysis and SEM
Welcome to the 3rd session of R Hands-on Training. In this session we will be covering following activities
• Background Materials
o Concepts of #regression analysis - https://youtu.be/kgHRuFKA0HY
o Theoretical discussion on simple regression - https://youtu.be/eI9vVBDItNE
o regression in decision making - https://youtu.be/-GpCWo1Lfk0
o Statistical reason to do regression – experimental design - https://youtu.be/BS_aDBH3mMY
o simple regression in excel - https://youtu.be/VsLYmrGfnes
o Simple regression in Stata - https://youtu.be/IN3QPwoN0eU
o Simple regression in SPSS - https://youtu.be/SOLqcTor5FQ
o simple regression in EViews - https://youtu.be/_1nGxDaELXI
o Simple regression in Python https://youtu.be/enwHAkpU3ek
• Use of R notebook and chunks
o https://youtu.be/jkhX2wMyJEU
• Performing Simple Cross-Sectional Regression in R
o https://youtu.be/g4mwbExrHO0
o https://youtu.be/3dIqxQ4L0uk
• Regression #Diagnostics
o Checking #multicollinearity - https://youtu.be/GeY8mvUt06g
o Use of R commander - https://youtu.be/zUHucTyE4cE
• Relevant Links to be explored in R like Stepwise, Logit, Probit, Poisson, 3SLS, Multinomial Logit
o #Stepwise regression - https://youtu.be/CNH9yeoYRfs
o 3sls - https://youtu.be/w7aMKkA0c7g
o Poisson regression - https://youtu.be/DPdlmxXpw5A
o Tobit regression - https://youtu.be/G9PJ3E_EErc
o #Ridge and lasso regression in R - https://youtu.be/MEZ0eB-1wLc
• Simple #SEM
• #Mediation Analysis
• #Moderation Analysis
• Exporting the report on Word, PDF or HTML
https://wn.com/Lecture_3_Understanding_Regression_Analysis,_Diagnostics_And_Sem_Mediation_Moderation_In_R
Regression Analysis and SEM
Welcome to the 3rd session of R Hands-on Training. In this session we will be covering following activities
• Background Materials
o Concepts of #regression analysis - https://youtu.be/kgHRuFKA0HY
o Theoretical discussion on simple regression - https://youtu.be/eI9vVBDItNE
o regression in decision making - https://youtu.be/-GpCWo1Lfk0
o Statistical reason to do regression – experimental design - https://youtu.be/BS_aDBH3mMY
o simple regression in excel - https://youtu.be/VsLYmrGfnes
o Simple regression in Stata - https://youtu.be/IN3QPwoN0eU
o Simple regression in SPSS - https://youtu.be/SOLqcTor5FQ
o simple regression in EViews - https://youtu.be/_1nGxDaELXI
o Simple regression in Python https://youtu.be/enwHAkpU3ek
• Use of R notebook and chunks
o https://youtu.be/jkhX2wMyJEU
• Performing Simple Cross-Sectional Regression in R
o https://youtu.be/g4mwbExrHO0
o https://youtu.be/3dIqxQ4L0uk
• Regression #Diagnostics
o Checking #multicollinearity - https://youtu.be/GeY8mvUt06g
o Use of R commander - https://youtu.be/zUHucTyE4cE
• Relevant Links to be explored in R like Stepwise, Logit, Probit, Poisson, 3SLS, Multinomial Logit
o #Stepwise regression - https://youtu.be/CNH9yeoYRfs
o 3sls - https://youtu.be/w7aMKkA0c7g
o Poisson regression - https://youtu.be/DPdlmxXpw5A
o Tobit regression - https://youtu.be/G9PJ3E_EErc
o #Ridge and lasso regression in R - https://youtu.be/MEZ0eB-1wLc
• Simple #SEM
• #Mediation Analysis
• #Moderation Analysis
• Exporting the report on Word, PDF or HTML
- published: 20 Aug 2023
- views: 40
40:25
Learn Statistical Regression in 40 mins! My best video ever. Legit.
See all my videos at: https://www.zstatistics.com/videos
0:00 Introduction
2:46 Objectives of regression
4:43 Population regression equation
9:34 Sample regres...
See all my videos at: https://www.zstatistics.com/videos
0:00 Introduction
2:46 Objectives of regression
4:43 Population regression equation
9:34 Sample regression line
18:51 SSR/SSE/SST
26:25 R-squared
29:00 Degrees of freedom and adjusted R-squared
https://wn.com/Learn_Statistical_Regression_In_40_Mins_My_Best_Video_Ever._Legit.
See all my videos at: https://www.zstatistics.com/videos
0:00 Introduction
2:46 Objectives of regression
4:43 Population regression equation
9:34 Sample regression line
18:51 SSR/SSE/SST
26:25 R-squared
29:00 Degrees of freedom and adjusted R-squared
- published: 22 May 2023
- views: 16099