This video directly follows part 1 in the StatQuest series on General Linear Models (GLMs) on Linear Regression https://youtu.be/nk2CQITm_eo . Here I show how the exact same principles from "simple" linear regression also apply multiple regression. At the end, I show how to test if a multiple regression is better than a simple regression. To see how to do multiple regression in R, check out https://youtu.be/hokALdIst8k
For a complete index of all the StatQuest videos, check out:
https://statquest.org/video-index/
If you'd like to support StatQuest, please consider a StatQuest t-shirt or sweatshirt...
https://teespring.com/stores/statquest
...or buying one or two of my songs (or go large and get a whole album!)
https://joshuastarmer.bandcamp.com/

Views: 17577
StatQuest with Josh Starmer

Overview of multiple regression including the selection of predictor variables, multicollinearity, adjusted R-squared, and dummy variables.
If you find these videos useful, I hope that you will consider signing up for my online statistics workshop on Udemy, which contains additional videos and lots of problems to help you apply and reinforce the important concepts: https://www.udemy.com/statshelp/?couponCode=coefficient

Views: 179394
George Ingersoll

This video demonstrates how to interpret multiple regression output in SPSS. This example includes two predictor variables and one outcome variable. Unstandardized and standardized coefficients are reviewed.

Views: 142132
Dr. Todd Grande

It covers in details the meaning of Multiple Regression, various methods of framing Multiple Regression Equations and Standard Error of Estimate in Multiple Regression. Lecture by: Prof. Rajinder Kumar Arora, Head of Department (Commerce & Management)

Views: 36019
Dr. B. R. Ambedkar Govt. College Kaithal

In this video, I will be talking about a parametric regression method called “Linear Regression” and it's extension for multiple features/ covariates, "Multiple Regression". You will gain an understanding of how to estimate coefficients using the least squares approach (scalar and matrix form) - fundamental for many other statistical learning methods.
If you thought this content was useful, SHARE it with your friend – you know, the one with the stats exam tomorrow and trying to binge watch YouTube tutorials. SUBSCRIBE to my channel for more amazing content!
More on Matrix Calculus: https://atmos.washington.edu/~dennis/MatrixCalculus.pdf

Views: 46939
CodeEmporium

I demonstrate how to perform a multiple regression in SPSS. This is the in-depth video series. I cover all of the main elements of a multiple regression analysis, including multiple R, R squared, model development (via stepwise method), intercept, unstandardized beta weights, standardized beta weights, semi-partial correlation, standard errors, as well as basic heteroscedasticity tests.

Views: 493993
how2stats

The most simple and easiest intuitive explanation of regression analysis. Check out this step-by-step explanation of the key concepts of regression analysis.
It is assumed the viewer has little background in statistics. This lecture is suitable for tertiary students struggling with statistics.
**** DID YOU LIKE THIS VIDEO? ****
Check out my full video on Simple Regressions: https://www.youtube.com/watch?v=38iNlkzF1sE
***
Come and check out my complete and comprehensive course on HYPOTHESIS TESTING! Click on the link below for a FREE PREVIEW and a MASSIVE discount (only for my Youtube students):
https://www.udemy.com/simplestats/?couponCode=123
This is a complete course that covers all the topics (such as the central limit theorem, p-values, hypothesis tests using proportions, and so much more) in a structured, step-by-step manner, coupled with a bucket load of practice exam questions and video worked solutions. I assume the viewer has zero background in statistics. You also get to post questions in the discussion forum and I will answer them right away!
https://www.udemy.com/simplestats/?couponCode=123
****
SUBSCRIBE at: https://www.youtube.com/subscription_center?add_user=quantconceptsedu
**** Check out some of our other mini-lectures:
Check out my 30 min lecture on Hypothesis Testing!
https://www.youtube.com/watch?v=lCuB2nEaBwM
Simple Introduction to Hypothesis Testing:
http://www.youtube.com/watch?v=yTczWL7qJ-Y
A Simple Rule to Correctly Setting Up the Null and Alternate Hypotheses:
https://www.youtube.com/watch?v=R2hxisYFKxM&feature=youtu.be
The Easiest Introduction to Regression Analysis:
http://www.youtube.com/watch?v=k_OB1tWX9PM
Super Easy Tutorial on Calculating the Probability of a Type 2 Error:
https://www.youtube.com/watch?v=L9rX8kTd8PI&feature=youtu.be
Check out my latest Youtube video on the Endogeneity Bias and the 2-Stage Least Squares regression: https://www.youtube.com/watch?v=HBr3376ttOg
**
Keywords: statistics, statistics help, statistics tutor, statistics tuition, hypothesis testing, regression analysis, university help, stats help, simple regression, multiple regression, econometrics

Views: 378817
Dave Your Tutor

This updated vidcast discusses the conceptual underpinnings of different types of model building in multiple analysis: simultaneous regression, hierarchical regression and stepwise regression. This improved vidcast offers clearer exposition of the concepts and a more readable Powerpoint slide.

Views: 10393
Ray Cooksey

This tutorial covers the basic concepts of Multiple Regression. Before watching this tutorial please make sure you are familiar with the basic concepts of simple linear regression, if you need a review, go to https://youtu.be/BLRjywb0mes. In this tutorial we discuss introduce the multiple regression model and discuss what its parameters mean and how to test the significance of the slopes of each independent variable using a t test. This tutorial also shows how to conduct regression analysis using Excel, and the output for multiple regression hypothesis tests for individual slopes is examined using the t tests statistic and the p value approach. The significance of r and the adjusted r squared values is also discussed. The ANOVA table and F test are not covered in this tutorial.

Views: 45279
Learn Something

From an existing multiple regression output produced with Excel 2007, I show you how to make point predictions and approximate 95% prediction intervals. The basic package of Excel does not have a routine for making predictions intervals, so I suggest a method of inflating the residual standard deviation statistic by 10% to get an approximate standard error of prediction.

Views: 128859
ProfTDub

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

Views: 740794
statisticsfun

The Multiple Linear Regression video series is available for FREE as an iTune book for download on the iPad. The ISBN number is 978-1-62407-066-6. The title is "Multiple Linear Regression." Waller and Lumadue are the authors. The iTune text provides accompanying narrative and the SPSS readouts used in the video series.
The textbook can be obtained from:
https://itunes.apple.com/us/book/multiple-linear-regression/id657084933?ls=1
This video introduces the concept of multiple linear regression

Views: 34368
Lee Rusty Waller

A brief explanation of the output of regression analysis. For more information visit www.calgarybusinessblog.com

Views: 465880
Matt Kermode

This video provides an example of interpreting multiple regression output in excel. The data set comes from Andy Field's "Discovering Statistics Using SPSS" (2009, 3rd Edition).

Views: 326595
TheWoundedDoctor

Multiple Regression in Excel in a nutshell. Focusing on Excel functionality more than presentation of regression theory.

Views: 88118
Jalayer Academy

Multiple regression concept explained in simple words along with solution of a numerical question.
FOR ALL VIDEOS PLEASE CLICK
youtube.com/kokabmanzoor/videos

Views: 3732
Kokab Manzoor

This video covers standard statistical tests for multiple regression. I cover:
- assumptions placed on the error term
- the F test of overall or joint significance
- hypotheses
- F test statistic
- p-value and its interpretatoin
- the F test of overall or joint significance
- hypotheses
- F test statistic
- p-value and its interpretation
- the t test of individual significance
- hypotheses
- F test statistic
- p-value and its interpretation

Views: 121149
Jason Delaney

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

Views: 346619
dataminingincae

I demonstrate how to create a scatter plot to depict the model R results associated with a multiple regression/correlation analysis.

Views: 87178
how2stats

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
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CC Kids: http://www.youtube.com/crashcoursekids

Views: 59313
CrashCourse

https://github.com/codebasics/py/tree/master/ML/2_linear_reg_multivariate
(Exercise is at the end of the ipynb notebook so just open that file and read through)
In this machine learning tutorial with python, we will write python code to predict home prices using multivariate linear regression in python (using sklearn linear_model). Home prices are dependant on 3 independant variables: area, bedrooms and age. Pandas dataframe is used to fill missing values first and then use that dataset to train a multivariate regression model.You can use exercise at the end to consolidate your understanding on whatever you have learnt in this machine learning tutorial.
Website: http://codebasicshub.com/
Facebook: https://www.facebook.com/codebasicshub
Twitter: https://twitter.com/codebasicshub
Google +: https://plus.google.com/106698781833798756600

Views: 21924
codebasics

Learn how to make predictions using Simple Linear Regression. To do this you need to use the Linear Regression Function (y = a + bx) where "y" is the dependent variable, "a" is the y intercept, "b" is the slope of the regression line, and "x" is the independent variable.
This video also shows you how to determine the slope (b) of the regression line, and the y intercept (a).
In order to determine the slope of a line you will need to first determine the Pearson Correlation Coefficient - this is described in a separate video (https://www.youtube.com/watch?v=2SCg8Kuh0tE).

Views: 463730
Eugene O'Loughlin

Using multiple predictor variables to predict a single quantitative outcome.

Views: 249866
TheRMUoHP Biostatistics Resource Channel

This video is a companion to the StatQuest on Multiple Regression https://youtu.be/zITIFTsivN8 It starts with a simple regression in R and then shows how multiple regression can be used to determine which parameters are the most valuable. If you want the code, you can get it from the StatQuest website, here: https://statquest.org/2017/10/30/statquest-multiple-regression-in-r/
For a complete index of all the StatQuest videos, check out:
https://statquest.org/video-index/
If you'd like to support StatQuest, please consider a StatQuest t-shirt or sweatshirt...
https://teespring.com/stores/statquest
...or buying one or two of my songs (or go large and get a whole album!)
https://joshuastarmer.bandcamp.com/

Views: 9191
StatQuest with Josh Starmer

It's easy to run a regression in Excel. The output contains a ton of information but you only need to understand a few key data points to make sense of your regression.
You need the Analysis Toolpak add-in to run regressions. It comes with Excel but you may need to load it if you don't see Data Analysis under the Data toolbar.
Produced by Sara Silverstein
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Views: 10067
BI Excel

Interpretation of the coefficients on the predictors in multiple linear regression made easy.

Views: 383186
Phil Chan

I demonstrate how to perform and interpret a hierarchical multiple regression in SPSS. I pay particular attention to the different blocks associated with a hierarchical multiple regression, as well as R squared change and F change.

Views: 119773
how2stats

Applied Multivariate Statistical Modeling by Dr J Maiti,Department of Management, IIT Kharagpur.For more details on NPTEL visit http://nptel.ac.in

Views: 9808
nptelhrd

Regression Analysis by Dr. Soumen Maity,Department of Mathematics,IIT Kharagpur.For more details on NPTEL visit http://nptel.ac.in

Views: 43962
nptelhrd

Multiple Linear Regression Model in R; Fitting the model and interpreting the outcomes!
Practice Dataset: (https://bit.ly/2rOfgEJ); Linear Regression Concept and with R (https://bit.ly/2z8fXg1)
More Statistics and R Programming Tutorial (https://goo.gl/4vDQzT)
Learn how to fit and interpret output from a multiple linear regression model in R and produce summaries.
▶︎ You will learn to use "lm", "summary", "cor", "confint" functions.
▶︎ You will also learn to use "plot" function for producing residual and QQ plots in R.
▶︎ We recommend that you first watch our video on simple linear regression concept (https://youtu.be/vblX9JVpHE8) and in R (https://youtu.be/66z_MRwtFJM)
▶︎▶︎Download the dataset here: https://statslectures.com/r-scripts-datasets
▶︎▶︎Like to support us? You can Donate https://statslectures.com/support-us or Share our Videos and help us reach more people!
◼︎ Table of Content:
0:00:07 Multiple Linear Regression Model
0:00:32 How to fit a linear model in R? using the "lm" function
0:00:36 How to access the help menu in R for multiple linear regression
0:01:06 How to fit a linear regression model in R with two explanatory or X variables
0:01:19 How to produce and interpret the summary of linear regression model fit in R
0:03:16 How to calculate Pearson's correlation between the two variables in R
0:03:26 How to interpret the collinearity between two variables in R
0:03:49 How to create a confidence interval for the model coefficients in R? using the "confint" function
0:03:57 How to interpret the confidence interval for our model's coefficients in R
0:04:13 How to fit a linear model using all of the X variables in R
0:04:27 how to check the linear regression model assumptions in R? by examining plots of the residuals or errors using the "plot(model)" function
►► Watch More:
►Linear Regression Concept and with R https://bit.ly/2z8fXg1
►R Tutorials for Data Science 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
►Linear Regression Concept and with R https://bit.ly/2z8fXg1
► Intro to Statistics Course: https://bit.ly/2SQOxDH
►Statistics & R Tutorials: Step by Step https://bit.ly/2Qt075y
This video is a tutorial for programming in R Statistical Software for beginners, using RStudio.
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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!

Views: 218258
MarinStatsLectures- R Programming & Statistics

FinTree website link: http://www.fintreeindia.com
FB Page link :http://www.facebook.com/Fin...
This series of videos covers the following key areas:
Multiple Regression Basics,
Multiple Regression Equation,
Estimated regression coefficients and their p-values
Alternative Hypothesis,
Hypothesis tests of regression coefficients
Confidence interval for the population value of regression
Predicted value for the depedendent variable
F-statistic,
R2 and adjusted R2 in Multiple Regression
ANOVA table
Types of heterosketdasticity
Multicollinearity,
We love what we do, and we make awesome video lectures for CFA and FRM exams. Our Video Lectures are comprehensive, easy to understand and most importantly, fun to study with!
This Video lecture was recorded by our popular trainer for CFA, Mr. Utkarsh Jain, during one of his live CFA Level II Classes in Pune (India).

Views: 4526
FinTree

This video illustrates how to perform and interpret a multiple regression statistical analysis in SPSS.
Multiple Regression
Regression
R-Squared
ANOVA table
Regression Weight
Beta Weight
Predicted Value
YouTube Channel (Quantitative Specialists): https://www.youtube.com/user/statisticsinstructor
Subscribe today!
Inferential course: https://www.udemy.com/inferential-statistics-spss
Descriptives course: https://www.udemy.com/descriptive-statistics-spss
Questionnaire/Survey & Likert Course: https://www.udemy.com/survey-data
ANOVA course: https://www.udemy.com/anova-spss
MANOVA course: https://www.udemy.com/manova-spss
Video Transcript: In this video, we'll take a look at how to run a multiple regression in SPSS. And on your screen as an example we have four variables SAT score, social support, gender, and college GPA. And in this example we're using the first three variables SAT score, social support, and gender, to predict first year college GPA. And here SAT score was taken in high school, social support is a measure of how much support a student felt that they received from others, where higher scores indicate greater support, and that was taken in the first year in college, and then gender, our dichotomous variable, where 1 is male and 2 is female, and the variable, college GPA, was the GPA after the first year in college. And in regression what we're trying to predict in this case, college GPA, is known as our criterion variable. It's also known as the dependent variable (DV). And then the variables that we're using to predict the criterion variable, SAT score, social support, and gender, those are known as are predictors or predictor variables, and we also refer to those as independent variables (IV). And those once again are SAT score, social support, and gender. Now in multiple regression you always have one criterion or dependent variable, and for it to be multiple regression you have to have two or more predictors or independent variables. if you just had one predictor or independent variable, such as SAT score, then that would be simple regression. But since we have two or more, in this case we have three once again, we're doing multiple regression. OK so to run multiple regression SPSS we want to go to Analyze, and then Regression and then go ahead and select Linear. And here we want to move college GPA to our Dependent box and then we want to select all the predictors and move those to our Independent(s) box. And then go ahead and click OK. And our output opens here and the first table, Variables Entered/Removed, this confirms that we had the variables gender, SAT score, and social support as our predictors, and then our dependent variable, or criterion variable, was college GPA, so that looks good. OK our next two tables, Model Summary and ANOVA, these two tables, they're looking at whether are predictors, once again, SAT score, social support, and gender, when those are taken together as a set or as a group, do they predict college GPA. And the Model Summary and ANOVA table are getting that slightly different things, but they're very closely related. So let's go ahead and start with Model Summary and take a look at that. So for Model Summary in this video we're going to focus on R square and then in another video we'll talk about these measures in more detail. But for this general overview the most commonly reported value in the Model Summary table is the R square value. And R squared, if I round this to two decimal places and then convert it to a percentage, so this would round two .50 or 50%, I could interpret R squared as follows. R squared once again is equal to .50 and then taken as a set the predictors SAT score, social support, and gender, account for 50% of the variance in college GPA. OK so R squared is a measure of the amount of variance in the dependent variable that the independent variables or predictors account for when taken as a group. And that's very important, it doesn't measure how much a given individual predictor accounts for, but only when we take them all as a group, this Model Summary table says overall, the regression model, which is what is referred to sometimes as a model, these three predictors predicting college GPA, that overall model accounts for 50% of the variance. Which is pretty good in practice. OK next we have our ANOVA table

Views: 67766
Quantitative Specialists

The data set used in this video is taken from my book 'Six Sigma Statistics using Minitab 17'. You can work along with the video by downloading the data set from www.rmksixsigma.com.

Views: 8447
RMK Six Sigma

For introductory statistics. Apologies for the background music, and for the fact that I will never have time to re-record this.
The dataset can be found here: https://drive.google.com/file/d/12wk1zlFiJmw6d1gUm3Dh4TORkE2cWt97/view?usp=sharing
It orginates from "Introduction to Linear Regression Analysis" by Montgomery, et. al.

Views: 52956
Matthias Kullowatz

Predicting a quantittive outcome from 2+ predictior variables while controlling for potential confounding-covariate variables.

Views: 114502
TheRMUoHP Biostatistics Resource Channel

This video can be used in conjunction with the "Multiple Regression - The Basics" video (http://youtu.be/rKQzjjWHm_A).
In this video, I show you how to check multiple regression assumptions in a few steps using IBM SPSS.
Although it is not exactly the same as SPSS, you can download a free program, PSPP, that is similar to SPSS: https://www.gnu.org/software/pspp/get.html. It is close enough to SPSS that you should be able to follow along with this video using PSPP.
I used materials from the following books for this video:
a. Lind, D, Marchal, W, & Wathen, S. (2012). Statistical Techniques in Business and Economics (15th Edition). Boston: McGraw-Hill. ISBN-13: 978-0-07-340180-5 (Textbook web resources: http://www.mhhe.com/lind15e)
b. Field, A. (2013). Discovering Statistics Using IBM SPSS Statistics (4th Edition). London: Sage Publications Ltd. ISBN-13: 978-1446249185
To add the ability to increase the playback speed of YouTube videos, go to the link below and click on the link to request the HTML5 viewer. It will allow you to change the speed of playback by clicking on the gear icon in the bottom right of your YouTube video screen (the same gear you use to change the quality). You should do this - playing my videos at 1.5 speed makes them seem better. :+)
https://www.youtube.com/html5

Views: 85390
weislearners

Multiple Regression Intro video series: (4 parts)
part 1: https://www.youtube.com/watch?v=e0o7oINrWuI&feature=youtu.be
part 2: https://www.youtube.com/watch?v=bTUrTObthug&feature=youtu.be
part 3: https://www.youtube.com/watch?v=46biqSoZxqY&feature=youtu.be
part 4: https://www.youtube.com/watch?v=JhMrgo97YHA&feature=youtu.be
Performing Multiple Regression in Microsoft Excel:
https://www.youtube.com/watch?v=cXiZ_t2NK1k&index=18&list=PLjPbBibKHH1_FxwPpGxBeUAu_o87fFYoQ
Performing a Multiple Regression in Google Sheets with XL Miner:
https://www.youtube.com/watch?v=YhBU92eyNRo&feature=youtu.be

Views: 15026
Jalayer Academy

I demonstrate how to perform a linear regression analysis in SPSS. The data consist of two variables: (1) independent variable (years of education), and (2) dependent variable (weekly earnings). It was hypothesized that years of education would be positively associated with weekly earnings. Additionally, the slope (unstandardized beta weight) and intercept (value of Y when X is 0) were identified and interpreted.

Views: 509378
how2stats

Currell: Scientific Data Analysis. Analysis for Fig 9.10(a) http://ukcatalogue.oup.com/product/9780198712541.do
© Oxford University Press

Views: 47220
Oxford Academic (Oxford University Press)

This video gives a quick overview of constructing a multiple regression model using R to estimate vehicles price based on their characteristics. The video focuses on how to employ a method of improving a linear model, and thus its linear equation, by stepwise regression with backward elimination of variables. It will demonstrate the process of building a model by starting with all candidate predictors and eliminating them one by one to optimize the model. The lesson also explains how to guide this optimization process by relying on the measures of model quality, such as R-Squared and Adjusted R-Squared statistics, and how to assess the variables usefulness to the model by judging their p-values, which represent the confidence in their coefficients which are to be used in the linear equation. The final model will be evaluated by calculating the correlation between the predicted and actual vehicle price for both the training and validation data sets. The explanation will be quite informal and will avoid the more complex statistical concepts. Note that a more complex process of building a multiple linear model, with details of variables transformation, checking for their multiple collinearity and extreme values, will be explained in the next lesson.
The data for this lesson can be obtained from the well-known UCI Machine Learning archives:
* https://archive.ics.uci.edu/ml/datasets/automobile
The R source code for this video can be found here (some small discrepancies are possible):
* http://visanalytics.org/youtube-rsrc/r-stats/Demo-D1-Multiple-Reg-Var-Selection.r
Videos in data analytics and data visualization by Jacob Cybulski, visanalytics.org.

Views: 48491
ironfrown

Dummy coding for multiple regression in SPSS
For more instructional videos and other materials on various statistics topics, be sure to my webpages at the links below:
Introductory statistics:
https://sites.google.com/view/statisticsfortherealworldagent/home
Multivariate statistics:
https://sites.google.com/view/statistics-for-the-real-world/home

Views: 23558
Mike Crowson

Learn how to conduct multiple linear regression using Excel data analysis toolpak

Views: 111337
KnowledgeVarsity

Simple Linear Regression using Microsoft Excel

Views: 276710
Jalayer Academy

This video moves us from simple linear regression to multiple regression. I discuss the differences introduced by increasing the number of regressors, and we cover:
- the multiple regression model
- the regression equation and estimated regression equation
- the least-squares approach
- the SST, SSE, and SSR
- the R-squared and adjusted R-squared

Views: 128742
Jason Delaney

Views: 7962
Statistics

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 website: https://statquest.org/2017/07/25/statquest-linear-regression-aka-glms-part-1/
For a complete index of all the StatQuest videos, check out:
https://statquest.org/video-index/
If you'd like to support StatQuest, please consider a StatQuest t-shirt or sweatshirt...
https://teespring.com/stores/statquest
...or buying one or two of my songs (or go large and get a whole album!)
https://joshuastarmer.bandcamp.com/

Views: 46779
StatQuest with Josh Starmer

How to use R to calculate multiple linear regression.
http://www.MyBookSucks.Com/R/Multiple_Linear_Regression.R
http://www.MyBookSucks.Com/R
Playlist on on Understanding Multiple Linear Regression Results (Watch videos 2 - 4)
http://www.youtube.com/playlist?list=PLWtoq-EhUJe2Z8wz0jnmrbc6S3IwoUPgL

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