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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) #[1] "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: 355223 Christoph Scherber

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Introduction to multiple regression in r. The data set is discussed and exploratory data analysis is performed here using correlation matrix and scatterplot matrix.

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Views: 12118 Simplilearn

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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!

01:20:45
This R tutorial gives an introduction to Linear Regression in R tool. This R tutorial is specially designed to help beginners. View upcoming batches schedule: http://goo.gl/BJJn0B This video helps you understand: • What is Data Mining? • What is Business Analytics? • Stages of Analytics / data mining • What is R? • Overview of Machine Learning • What is Linear Regression? • Case Study The topics related to ‘Data Analytics with R’ have been widely covered in our course. For more information, please write back to us at [email protected] or call us at IND: 9606058406 / US: 18338555775 (toll-free).
Views: 37179 edureka!

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This video describes how to do Logistic Regression in R, step-by-step. We start by importing a dataset and cleaning it up, then we perform logistic regression on a very simple model, followed by a fancy model. Lastly we draw a graph of the predicted probabilities that came from the Logistic Regression. The code that I use in this video can be found on the StatQuest website: https://statquest.org/2018/07/23/statquest-logistic-regression-in-r/#code For more details on what's going on, check out the following StatQuests: For a general overview of Logistic Regression: https://youtu.be/yIYKR4sgzI8 The odds and log(odds), clearly explained: https://youtu.be/ARfXDSkQf1Y The odds ratio and log(odds ratio), clearly explained: https://youtu.be/8nm0G-1uJzA Logistic Regression, Details Part 1, Coefficients: https://youtu.be/vN5cNN2-HWE Logistic Regression, Details Part 2, Fitting a line with Maximum Likelihood: https://youtu.be/BfKanl1aSG0 Logistic Regression Details Part 3, R-squared and its p-value: https://youtu.be/xxFYro8QuXA Saturated Models and Deviance Statistics, Clearly Explained: https://youtu.be/9T0wlKdew6I Deviance Residuals, Clearly Explained: https://youtu.be/JC56jS2gVUE 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/ ...or just donating to StatQuest! https://www.paypal.me/statquest

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R Programming - Linear Regression Watch More Videos at https://www.tutorialspoint.com/videotutorials/index.htm Lecture By: Mr. Ashish Sharma, Tutorials Point India Private Limited.

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This video, which walks you through a simple regression in R, is meant to be a companion to the StatQuest on Linear Regression https://youtu.be/nk2CQITm_eo If you want to just copy and paste the R code, you can get it from the StatQuest website: https://statquest.org/2017/07/25/statquest-linear-regression-aka-glms-part-1/ If you'd like to support StatQuest, please consider buying one or two of my songs (or go large and get a whole album!) https://joshuastarmer.bandcamp.com/

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This clip demonstrates how to use R to run a regression. This clip is a companion to the following website which gives an introduction to R programming for econometricians. The dataset used is also available from that website: http://eclr.humanities.manchester.ac.uk/index.php/R Table of Contents: 00:00 - Introduction 04:01 - Regression Output 06:53 - Accessing Regression Results 10:09 - no dataframe 12:53 - no constant 13:37 - Subsets/Subsamples
Views: 23743 Ralf Becker

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See Part 2 of this topic here! https://www.youtube.com/watch?v=sKW2umonEvY
Views: 33384 Jonathan Brown

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Association between two numerical variables with R
Views: 2043 Gilles Lamothe

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How to calculate Linear Regression using R. http://www.MyBookSucks.Com/R/Linear_Regression.R http://www.MyBookSucks.Com/R Playlist http://www.youtube.com/playlist?list=PLF596A4043DBEAE9C
Views: 23397 statisticsfun

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See here for the course website, including a transcript of the code and an interactive quiz for this segment: http://dgrtwo.github.io/RData/lessons/lesson3/segment3/

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Views: 2204 Katie Ann Jager

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This video provides a simple example of doing multiple linear regression analysis in R. Data file: https://drive.google.com/open?id=0B5W8CO0Gb2GGUVNyZ1JqMW1NZjA 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: 36383 Bharatendra Rai

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When we have one numeric dependent variable (target) and one independent variable where a scatterplot shows a linear pattern we can employ simple linear regression (SLR) from the Regression family of techniques.

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Views: 72741 edureka!

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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: 64634 statisticsfun

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In this video you'll learn the hierarchical representation of Regression Models. Regression models are primarily classified into 2 categories: - Univariate - Multivariate Univariate Regression model is the simplest form of statistical analysis Multivariate Regression model is where the response variable is affected by more than one predictor variable. They can be further classified as Liner and Non-Linear models. You will also learn about "Simple Linear Regression" Click Here For More Details: www.simplilearn.com/big-data-and-analytics/business-analytics-foundation-r-tools-training
Views: 5316 Simplilearn

07:39
Part 10 of my series about the statistical programming language R! In this video I show how a linear regression line can be added to your data-plot. Also I show how you can add lines to your plot manually. Finally you will learn how to generate normal-distributed random values and a line will be generated that fits those random numbers best.
Views: 186511 Tutorlol

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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/

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Views: 16790 Simple Learning Pro

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In this lesson, we learn how to run a categorical regression model in R.
Views: 6653 Shokoufeh Mirzaei

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Simple linear regression method is demonstrated in R Studio which is an integrated development environment for R. R Studio is freely available.
Views: 21736 kartikeya bolar

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Analytics Free Tutorials - Learn what is Linear Regression, How Linear Regression is applied to solve analytics problems, Learn how Linear Regression is performaed in R. Learn more about Ivy Professional School's popular Business Analytics certification course at http://ivyproschool.com/our-courses/big-data-and-analytics/
Views: 10946 IvyProSchool

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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: 51546 ironfrown

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In this video, we learn how ro run a multiple linear regression model in R.
Views: 1038 Shokoufeh Mirzaei

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This tutorial shows how to make a scatterplot in R. We also add a regression line to the graph. We also make a scatterplot with a third variable to add extra insight into our graph. Thank you for watching this video. Make sure to like the video if you found it helpful and subscribe if you want to see more videos like this one!
Views: 16936 thatRnerd

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Hello friends, It will help in running regression and extracting all the required outputs from the results.
Views: 12044 Sarveshwar Inani

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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: 392556 statisticsfun

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In this video, I show how to use R to fit a multiple regression model including a two-way interaction term. I show how to produce fitted lines when there is an interaction between two continuous (!) variables. The code used in this video is: data(airquality) names(airquality) #[1] -Ozone- -Solar.R- -Wind- -Temp- -Month- -Day- # Produce plots for some explanatory variables plot(Ozone~Solar.R,airquality) plot(Ozone~Wind,airquality) coplot(Ozone~Solar.R|Wind,panel=panel.smooth,airquality) model2=lm(Ozone~Solar.R*Wind,airquality) plot(model2) summary(model2) termplot(model2) summary(airquality\$Solar.R) # Min. 1st Qu. Median Mean 3rd Qu. Max. NA's # 7.0 115.8 205.0 185.9 258.8 334.0 7 summary(airquality\$Wind) Min. 1st Qu. Median Mean 3rd Qu. Max. 1.700 7.400 9.700 9.958 11.500 20.700 Solar1=mean(airquality\$Solar.R,na.rm=T) Solar2=100 Solar3=300 predict(model2,data.frame(Solar.R=100,Wind=10)) p1=predict(model2,data.frame(Solar.R=Solar1,Wind=1:20)) p2=predict(model2,data.frame(Solar.R=Solar2,Wind=1:20)) p3=predict(model2,data.frame(Solar.R=Solar3,Wind=1:20)) plot(Ozone~Wind,airquality) lines(1:20,p1) lines(1:20,p2) lines(1:20,p3)
Views: 100794 Christoph Scherber

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In this video, we learn how to setup a simple linear regression model using R
Views: 2159 Shokoufeh Mirzaei

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Predictions with the simple/bivariate regression model -scatterplot -how to run a simple regression - ways to obtain predictions - difference between predictive interval and confidence interval - prediction and extrapolation
Views: 9302 Phil Chan

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R programming for beginners - This video is an introduction to R programming. I have another channel dedicated to R teaching: https://www.youtube.com/c/rprogramming101 In this video I provide a tutorial on some statistical analysis (specifically using the t-test and linear regression). I also demonstrate how to use dplyr and ggplot to do data manipulation and data visualisation. Its R programming for beginners really and is filled with graphics, quantitative analysis and some explanations as to how statistics work. If you’re a statistician, into data science or perhaps someone learning bio-stats and thinking about learning to use R for quantitative analysis, then you’ll find this video useful. Importantly, R is free. If you learn R programming you’ll have it for life. This video was sponsored by the University of Edinburgh. Find out more about their programmes at http://edin.ac/2pTfis2 This channel focusses on global health and public health - so please consider subscribing if you’re someone wanting to make the world a better place – I’d love to you join this community. I have videos on epidemiology, study design, ethics and many more.

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Views: 13783 LiveLessons

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Note: Please interpret "Degrees of freedom" to "Confidence level" during the explanation of 'confint' function. Simple linear regression is quick and easy way to predict the value on one variable based on another variable. In this video I've talked about a real life example where simple linear regression can be useful. And then talked about how you can achieve simple linear regression within R.
Views: 6696 Abhishek Agarrwal

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Multiple Linear Regression with Interaction in R: How to include interaction or effect modification in a regression model in R. Free Practice Dataset (LungCapData):(https://bit.ly/2rOfgEJ) More Statistics & R Programming Videos: https://goo.gl/4vDQzT ►► Like to support us? You can Donate (https://bit.ly/2CWxnP2) or Share our videos with your friends! In this R video tutorial, we will learn how to include interaction or effect modification in a regression model and how to interpret the model coefficients. In statistics, an interaction may arise when considering the relationship among three or more variables, and describes a situation in which the effect of one causal variable on an outcome depends on the state of a second causal variable (that is, when effects of the two causes are not additive). Although commonly thought of in terms of causal relationships, the concept of an interaction can also describe non-causal associations. Interactions are often considered in the context of regression analyses or factorial experiments. These video tutorials are useful for anyone interested in learning data science and statistics with R programming language using RStudio.. ► ► Watch More: ► Intro to Statistics Course: https://bit.ly/2SQOxDH ►Data Science with R https://bit.ly/1A1Pixc ►Getting Started with R (Series 1): https://bit.ly/2PkTneg ►Graphs and Descriptive Statistics in R (Series 2): https://bit.ly/2PkTneg ►Probability distributions in R (Series 3): https://bit.ly/2AT3wpI ►Bivariate analysis in R (Series 4): https://bit.ly/2SXvcRi ►Linear Regression in R (Series 5): https://bit.ly/1iytAtm ►ANOVA Concept and with R https://bit.ly/2zBwjgL ►Hypothesis Testing: https://bit.ly/2Ff3J9e ►Linear Regression Concept and with R Lectures https://bit.ly/2z8fXg1 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!

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Multiple Linear Regression Analysis, Evaluating Estimated Linear Regression Function (Looking at a single Independent Variable), basic approach to test relationships, (1) 𝐑^𝟐 Correlation between X (Independent Variable) & Y (Dependent Variable), F-Test, (2) Regression Analysis: If there is a significant relationship between X (Independent Variable) & Y (Dependent Variable), T-Test, (3) Explaining how to calculate the Degrees Of Freedom for the F-Test & T-Test, detailed discussion comparing two different regression equations to see which best predicts the dependent variable by Allen Mursau
Views: 204567 Allen Mursau

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Interpreting Interaction in Linear Regression with R: How to interpret interaction or effect modification in a linear regression model, between two factors with example. How to fit an interaction term in a linear regression model in R Video (https://youtu.be/8YuuIsoYqsg); More Statistics & R Programming Videos: https://goo.gl/4vDQzT ►► Like to support us? You can Donate (https://bit.ly/2CWxnP2) or Share our videos with your friends! In this R video tutorial, we will learn to interpret interaction or effect modification in a linear regression model, between two factors or two categorical variables. This video does not discuss fitting the model using R, but only discusses how interacting variables are interpreted in a regression model. The previous video (Tutorial 5.9) in the series describes how to fit an interaction term in a linear regression model in R (https://youtu.be/8YuuIsoYqsg) Table of Content: 0:00:16 An introduction to our data that includes one dependent variable and 2 explanatory or independent variables 0:00:43 the visual representation of the data by using a plot 0:01:22 explaining the concept of interaction on the plot with an example 0:02:05 different ways of stating interaction in the data 0:02:25 examining interaction numerically by examining the fitted regression model 0:05:29 examining a model with no interaction 0:06:03 terms for including an interaction term in our model ► ► Watch More: ► Intro to Statistics Course: https://bit.ly/2SQOxDH ►Data Science with R https://bit.ly/1A1Pixc ►Getting Started with R (Series 1): https://bit.ly/2PkTneg ►Graphs and Descriptive Statistics in R (Series 2): https://bit.ly/2PkTneg ►Probability distributions in R (Series 3): https://bit.ly/2AT3wpI ►Bivariate analysis in R (Series 4): https://bit.ly/2SXvcRi ►Linear Regression in R (Series 5): https://bit.ly/1iytAtm ►ANOVA Concept and with R https://bit.ly/2zBwjgL ►Hypothesis Testing: https://bit.ly/2Ff3J9e ►Linear Regression Concept and with R Lectures https://bit.ly/2z8fXg1 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!

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An introduction to multiple regression using the mtcars data frame and then application to improvement of OPS to predict batting performance. We also use multiple regression to determine the value of different types of hits, walks, stolen bases and outs (Linear Weights).
Views: 7214 R at Colby

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Including Categorical Variables or Factors in Linear Regression with R, Part I: how to include a categorical variable in a regression model and interpret the model coefficient with example in R. Free Practice Dataset (LungCapData):(https://bit.ly/2rOfgEJ); More Statistics and R Programming Tutorials: (https://goo.gl/4vDQzT) In this R video tutorial, we will learn to include a categorical variable (a factor or qualitative variable) in a regression model in R. We will also learn to interpret the model coefficients. We will work through an example to learn these concepts step by step. These video tutorials are useful for anyone interested in learning data science and statistics with R programming language using RStudio.. ► ► Watch More: ► Intro to Statistics Course: https://bit.ly/2SQOxDH ►Data Science with R https://bit.ly/1A1Pixc ►Getting Started with R (Series 1): https://bit.ly/2PkTneg ►Graphs and Descriptive Statistics in R (Series 2): https://bit.ly/2PkTneg ►Probability distributions in R (Series 3): https://bit.ly/2AT3wpI ►Bivariate analysis in R (Series 4): https://bit.ly/2SXvcRi ►Linear Regression in R (Series 5): https://bit.ly/1iytAtm ►ANOVA Concept and with R https://bit.ly/2zBwjgL ►Hypothesis Testing: https://bit.ly/2Ff3J9e ►Linear Regression Concept and with R Lectures https://bit.ly/2z8fXg1 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!

06:48
Polynomial Regression in R: How to fit polynomial regression model in R; Free Dataset & R Script (https://goo.gl/tJj5XG); More Statistics and R Programming Tutorials (https://goo.gl/4vDQzT) ►► Like to support us? You can Donate (https://bit.ly/2CWxnP2), Share our Videos, Leave us a Comment and Give us a Thumbs up! Either way We Thank You! In this R video tutorial we will learn how to fit polynomial regression model and assess polynomial regression models using the partial F-test with R. Polynomial regression is a form of regression analysis in which the relationship between the independent variable X and the dependent variable Y is modelled as an nth degree polynomial in x. Polynomial regression models are useful when the relationship between the independent variables(X) and the dependent variables(Y) is not linear. These video tutorials are useful for anyone interested in learning data science and statistics with R programming language using RStudio.. ► ► Watch More: ► Intro to Statistics Course: https://bit.ly/2SQOxDH ►Data Science with R https://bit.ly/1A1Pixc ►Getting Started with R (Series 1): https://bit.ly/2PkTneg ►Graphs and Descriptive Statistics in R (Series 2): https://bit.ly/2PkTneg ►Probability distributions in R (Series 3): https://bit.ly/2AT3wpI ►Bivariate analysis in R (Series 4): https://bit.ly/2SXvcRi ►Linear Regression in R (Series 5): https://bit.ly/1iytAtm ►ANOVA Concept and with R https://bit.ly/2zBwjgL ►Hypothesis Testing: https://bit.ly/2Ff3J9e ►Linear Regression Concept and with R Lectures https://bit.ly/2z8fXg1 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!

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In this lesson, we learn how to develop a piecewise linear regression model in R.
Views: 4479 Shokoufeh Mirzaei

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