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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: 179377 George Ingersoll
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: 463690 Eugene O'Loughlin
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: 46930 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: 493971 how2stats
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: 142109 Dr. Todd Grande
A guide to solving Anderson Sweeney & Williams 11e Chapter 15 Problem 7, using Microsoft Excel. The dataset is titled "Laptop.xlsx".
Views: 36198 Jason Delaney
Applied Multivariate Statistical Modeling by Dr J Maiti,Department of Management, IIT Kharagpur.For more details on NPTEL visit http://nptel.ac.in
Views: 15752 nptelhrd
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
stepwise multiple regression example
Views: 53109 Math Guy Zero
Views: 10067 BI Excel
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: 326589 TheWoundedDoctor
In this video, I present an example of a multiple regression analysis of website visit duration data using both quantitative and qualitative variables. Variables used include gender, browser, mobile/non-mobile, and years of education. Gender and mobile each require a single dummy variable, while browser requires several dummy variables. I also present models that include interactions between the dummy variables and years of education to analyze intercept effects, slope effects, and fully interacted models. In short, I cover: - multiple category qualitative variables - dummy variables - intercept effects - slope effects - dummy interactions I hope you find it useful! Please let me know if you have any questions! --Dr. D.
Views: 241500 Jason Delaney
I address the issue of what sample size you need to conduct a multiple regression analysis.
Views: 15911 how2stats
Check out our new Excel Data Analysis text: https://www.amazon.com/dp/B076FNTZCV This video illustrates how to perform a multiple regression statistical analysis in Microsoft Excel using the Data Analysis Toolpak. 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! Video Transcript: and if you recall, if we use an alpha .05, which is what we typically use and we'll also use in this example. If this p-value is less than .05, then that indicates the test is significant. So this value is significant because .0004 is definitely less than .05. So this indicates that the R-squared of .50 is significantly greater than zero. So in other words, the variables SAT score, social support, and gender, once again taken as a group, predict a significant amount of variance in college GPA. And we could write that up as follows. We could say the overall regression model was significant, and then we have F 3, 26 and that comes from right here, 3 and 26, = 8.51, which is the F value here reported in the table, p is less than .001, and I said that because this value is smaller than .001. And I also put the R-squared here. R-squared = .50, and that of course came from right here. So you'll often see results written up like this, in a research article or what have you. So this is one way to express the results of the ANOVA table. So if you're reading a research article on multiple regression and you see this information here, most likely, this first part here is corresponding to the results of the ANOVA table. OK so these first two tables, as I had said earlier, they assess how well our three predictors, taken as a set, did at predicting first-year college GPA. Moving to our last table, this is where we look at the individual predictors. Whether SAT score, on its own, social support, on its own, and gender, once again on its own, are these three variables significant predictors of college GPA. Now it may be that one of them is significant, two of them are, or all three of them are significant, but that's what this table assesses. So as we did before, we'll use alpha .05, once again. So we're going to assess each of these values against .05. And notice that SAT score, this p-value definitely is less than .05, so SAT is significant. Social support, this p-value, while fairly close, is also less than .05, so social support is significant as well. But notice gender, .66, that's definitely not less than .05, so gender is not significant. And that's really not that surprising because males and females don't typically differ significantly in their college GPA, in their first year, or in all four years for that matter. But I wanted to include this variable gender in this model as well, so you can see an example of a non-significant result. So once again this table is looking at the predictors individually, so this indicates here that SAT score is a significant predictor of college GPA, social support is also a significant predictor of college GPA, but gender is not a significant predictor. Now in this table here what we're assessing is whether these predictors account for a significant amount of unique variance in college GPA. So in other words what that means is that SAT scores significantly predicts college GPA, so it accounts for a separate, significant part of college GPA than social support, which is also significant, but it accounts for a unique part of college GPA that SAT does not account for. So if a test is significant here, that means that the variable accounts for a significant amount of variance in college GPA uniquely to itself. And that's an important point to note here, and that's frequently confused with multiple regression. So, a scenario, if these two predictors were completely and perfectly correlated at 1.0, in other words they're really getting at the exact same thing in college GPA, then neither of these would be significant if that was the case, because neither of them would be accounting for any unique information in college GPA whatsoever. They would be totally redundant and they would both not be significant. So if a predictor is significant here, as these both are, then that tells us that they account for a significant amount of unique variance in college GPA. So to wrap it all up here, to summarize, our regression overall was significant as we see that in the ANOVA table, and the amount of variance that was accounted for, when the three predictors were taken as a group, was 50% of the variance, or half of the variance, which was pretty good. When we looked at the predictors individually, SAT score was a significant predictor of college GPA, as was social support, but gender was not significant. This concludes the video on multiple regression in Microsoft Excel. Thanks for watching.
Tutorial on how to calculate Multiple Linear Regression using SPSS. I show you how to calculate a regression equation with two independent variables. I also show you how to create a Pearson r correlation matrix using output from SPSS. Playlist on Using SPSS For Multiple Linear Regression http://www.youtube.com/playlist?list=PLWtoq-EhUJe2Z8wz0jnmrbc6S3IwoUPgL Like MyBookSucks on Facebook at http://www.MyBookSucks.Com/PartyMoreStudyLess Created by David Longstreet, Professor of the Universe, MyBookSucks http://www.linkedin.com/in/davidlongstreet
Views: 198186 statisticsfun
We take the model that was presented in the first video and run the linear regression in excel. We look at the p values and do hypothesis testing. We then extend the model to a multiple regression model.
Views: 112883 pomscm
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: 740737 statisticsfun
Regression Analysis by Dr. Soumen Maity,Department of Mathematics,IIT Kharagpur.For more details on NPTEL visit http://nptel.ac.in
Views: 43962 nptelhrd
Course web page: http://web2.slc.qc.ca/pcamire/
Views: 505995 [email protected]
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: 346585 dataminingincae
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. Follow MarinStatsLectures Subscribe: https://goo.gl/4vDQzT website: https://statslectures.com Facebook:https://goo.gl/qYQavS Twitter:https://goo.gl/393AQG Instagram: https://goo.gl/fdPiDn Our Team: Content Creator: Mike Marin (B.Sc., MSc.) Senior Instructor at UBC. Producer and Creative Manager: Ladan Hamadani (B.Sc., BA., MPH) These videos are created by #marinstatslectures to support some courses at The University of British Columbia (UBC) (#IntroductoryStatistics and #RVideoTutorials for Health Science Research), although we make all videos available to the everyone everywhere for free. Thanks for watching! Have fun and remember that statistics is almost as beautiful as a unicorn!
Learn via an example how to regress data to a straight line. For more videos and resources on this topic, please visit http://nm.mathforcollege.com/topics/linear_regression.html
Views: 48121 numericalmethodsguy
I address the issue of what sample size you need to conduct a multiple regression analysis. (Part 2)
Views: 8313 how2stats
Introduction to multiple regression in r. The data set is discussed and exploratory data analysis is performed here using correlation matrix and scatterplot matrix.
Views: 41457 Jalayer Academy
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: 21911 codebasics
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: 52955 Matthias Kullowatz
Views: 51678 Simplilearn
This video provides a simple example of doing multiple linear regression analysis in R and includes, - developing a linear model - comparing full and reduced model using ANOVA - Prediction - Confidence interval R is a free software environment for statistical computing and graphics, and is widely used by both academia and industry. R software works on both Windows and Mac-OS. It was ranked no. 1 in a KDnuggets poll on top languages for analytics, data mining, and data science. RStudio is a user friendly environment for R that has become popular.
Views: 31159 Bharatendra Rai
Introduction to Quality Science : Minitab Video Tutorials
Views: 32863 Dragonfly Statistics
Views: 7962 Statistics
Views: 677237 Khan Academy
Views: 8473 ProfThacker
A short tutorial on calculating a multiple regression in SPSS (also known as PASW) using the simple defaults. Also compares results with bivariate correlations in earlier tutorial.
Views: 324826 Barton Poulson
This video documents how to perform a multivariate regression in Excel.
Views: 22751 GSBS 6002
Views: 4776 The Data Science Show
In this video, I show how to use R to fit a linear regression model using the lm() command. I also introduce how to plot the regression line and the overall arithmetic mean of the response variable, and I briefly explain the use of diagnostic plots to inspect the residuals. Basic features of the R interface (script window, console window) are introduced. The R code used in this video is: data(airquality) names(airquality) # "Ozone" "Solar.R" "Wind" "Temp" "Month" "Day" plot(Ozone~Solar.R,data=airquality) #calculate mean ozone concentration (na´s removed) mean.Ozone=mean(airquality\$Ozone,na.rm=T) abline(h=mean.Ozone) #use lm to fit a regression line through these data: model1=lm(Ozone~Solar.R,data=airquality) model1 abline(model1,col="red") plot(model1) termplot(model1) summary(model1)
Views: 332625 Christoph Scherber
This video demonstrates how to conduct and interpret a multiple linear regression (multiple regression) using Microsoft Excel data analysis tools. Multiple regressions return the contribution of multiple predictor variables on one outcome variable. Predicted values for the outcome variable are calculated using the estimated regression equation.
Views: 28028 Dr. Todd Grande
Links of Data set and case study used in the above video. 1.https://drive.google.com/open?id=1nGSNNDfc7iNvHjCS_K5hhWKa1sWdJciX 2.https://drive.google.com/open?id=1ItFLk_EDQly6eVbqACEP7z8mTgMLmlUO 3.https://drive.google.com/open?id=1w-s5obJ0Dpxvz7gLKgbOTB6kcwiA7ccy
Views: 3726 Dr. Shailesh Kaushal
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: 128857 ProfTDub
This video explains how hypothesis testing works in practice, using a particular example. Check out https://ben-lambert.com/econometrics-course-problem-sets-and-data/ for course materials, and information regarding updates on each of the courses. Quite excitingly (for me at least), I am about to publish a whole series of new videos on Bayesian statistics on youtube. See here for information: https://ben-lambert.com/bayesian/ Accompanying this series, there will be a book: https://www.amazon.co.uk/gp/product/1473916364/ref=pe_3140701_247401851_em_1p_0_ti
Views: 67429 Ben Lambert
WHAT IS REGRESSION ANALYSIS WITH EXAMPLES IN HINDI
Views: 24484 LearnEveryone
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
Views: 56865 statisticsfun
An example on how to calculate R squared typically used in linear regression analysis and least square method. Like us on: http://www.facebook.com/PartyMoreStudyLess Link to Playlist on Linear Regression: http://www.youtube.com/course?list=ECF596A4043DBEAE9C Link to Playlist on SPSS Multiple Linear Regression: http://www.youtube.com/playlist?list=PLWtoq-EhUJe2Z8wz0jnmrbc6S3IwoUPgL Created by David Longstreet, Professor of the Universe, MyBookSucks http://www.linkedin.com/in/davidlongstreet
Views: 365457 statisticsfun
http://thedoctoraljourney.com/ This tutorial defines a bivariate linear regression, provides examples for when this analysis might be used by a researcher, walks through the process of conducting this analysis, and discusses how to set up an SPSS file and write an APA results section for this analysis. For more statistics, research and SPSS tools, visit http://thedoctoraljourney.com/.
Views: 13882 The Doctoral Journey
Linear Regression Analysis, (ANOVA) Analysis Of Variance, R-Squared & F-Test, applying to a regression example, understanding the variance testing between total squared error, explained squared error & residuals squared which is not explained, explaining how to calculate the degrees of freedom, calculating F test based on R-Squared value, etc., detailed discussion by Allen Mursau
Views: 52472 Allen Mursau
Views: 34244 James Donald