Search results “Regression analysis in r”

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: 318907
Christoph Scherber

Simple Linear Regression in R ; For more Statistics and R Programming Tutorials: https://goo.gl/4vDQzT; Simple Linear Regression Concept and Terminology: https://goo.gl/VhWmVD ;Dataset: https://goo.gl/tJj5XG
How to fit a Linear Regression Model in R, Produce Summaries and ANOVA table for it.
◼︎ What to Expect in this R Tutorial:
►In this R video tutorial you will learn When to use a regression model, and how to use the “lm” command in R to fit a linear regression model for your data
► Here you will also learn to produce summaries for your regression model using “summary” command in R statistics software; these summaries can include intercept, test statistic, p value, and estimates of the slope for your linear regression model
► in this tutorial, you will also become familiar with the Residual Error: a measure of the variation of observations in regression line
► You will also learn to ask R programming software for the attributes of the simple linear regression model using "attributes" command, extract certain attributes from the regression model using the dollar sign ($), add a regression line to a plot in R using "abline" command and change the color or width of the regression line.
► this R tutorial will show you how to get the simple linear regression model's coefficient using the "coef" command or produce confidence intervals for the regression model using "confint" commands; moreover, you will learn to change the level of confidence using the "level" argument within the "confint" command.
►You will also learn to produce the ANOVA table for the linear regression model using the "anova" command, explore the relationship between ANOVA table and the f-test of the regression summary, and explore the relationship between the residual standard error of the linear regression summary and the square root of the mean squared error or mean squared residual from the ANOVA table.
► ►You can access and download the dataset here:
https://statslectures.com/r-stats-datasets
► ► Watch this Statistics Tutorial on the concept and terminology for Simple Linear Regression Model https://youtu.be/vblX9JVpHE8
◼︎ Table of Content:
0:00:07 When to fit a simple linear regression model?
0:01:11 How to fit a linear regression model in R using the "lm" command
0:01:14 How to access the help menu in R for any command
0:01:36 How to let R know which variable is X and which one is Y when fitting a regression model
0:01:45 How to ask for the summary of the simple linear regression model in R including estimates for intercept, test statistic, p-values and estimates of the slope.
0:02:27 Residual standard error (residual error) in R
0:02:53 How to ask for the attributes of the simple linear regression model in R
0:03:06 How to extract certain attributes from the simple linear regression model in R
0:03:40 How to add a regression line to a plot in R
0:03:52 How to change the color or width of the regression line in R
0:04:07 How to get the simple linear regression model's coefficient in R
0:04:11 How to produce confidence intervals for model's coefficients in R
0:04:21 How to change the level of confidence for model's coefficients in R
0:04:38 How to produce the ANOVA table for the linear regression in R
0:04:47 Explore the relationship between ANOVA table and the f-test of the linear regression summary
0:04:55 Explore the relationship between the residual standard error of the linear regression summary and the square root of the mean squared error or mean squared residual from the ANOVA table
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These #RTutorial are created by #marinstatslectures to support the statistics course (#SPPH400) at The University of British Columbia(UBC) although we make all videos available to the public for free.

Views: 189259
MarinStatsLectures-R Programming & Statistics

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: 33823
edureka!

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: 38219
Jalayer Academy

( Data Science Training - https://www.edureka.co/data-science )
This Edureka Linear Regression tutorial will help you understand all the basics of linear regression machine learning algorithm along with examples. This tutorial is ideal for both beginners as well as professionals who want to learn or brush up their Data Science concepts. Below are the topics covered in this tutorial:
1) Introduction to Machine Learning
2) What is Regression?
3) Types of Regression
4) Linear Regression Examples
5) Linear Regression Use Cases
6) Demo in R: Real Estate Use Case
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#LinearRegression #Datasciencetutorial #Datasciencecourse #datascience
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Views: 63093
edureka!

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" commands. You will also learn the "plot" command for producing residual and QQ plots. It will be helpful to first review our video on simple linear regression. The video provides a tutorial for programming in R Statistical Software for beginners.
You can access and download the "LungCapData" dataset here:
Excel format: https://bit.ly/LungCapDataxls
Tab Delimited Text File: https://bit.ly/LungCapData
Here is a quick overview of the topic addressed in this video:
0:00:07 why use Multiple Linear Regression Model
0:00:32 using the "lm" command to fit a linear model
0:00:36 how to access the help menu in R for multiple linear regression by typing "help"
0:01:06 fitting a linear regression model using Age and Height as the explanatory or X variables
0:01:19 producing and interpreting the summary of linear regression model fit in R
0:03:16 how to calculate Pearson's correlation between the two variables
0:03:26 how to interpret the collinearity between two variables
0:03:49 how to create a confidence interval for the model coefficients using the "confint" command
0:03:57 interpreting the confidence interval for our model's coefficients
0:04:13 fitting a linear model using all of the X variables
0:04:27 how to check the model assumptions by examining plots of the residuals or errors using the "plot(model)" command

Views: 210169
MarinStatsLectures-R Programming & Statistics

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: 19666
Ralf Becker

R programming for beginners - This video is an introduction to R programming in which 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.

Views: 306337
Global Health with Greg Martin

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: 4247
Phil Chan

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.

Views: 1745
Tutorials Point (India) Pvt. Ltd.

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.

Views: 24669
Jalayer Academy

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

Views: 26791
Jonathan Brown

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/

Views: 21508
Data Analysis and Visualization Using R

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: 28119
Bharatendra Rai

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/

Views: 20433
StatQuest with Josh Starmer

This "Linear regression in R" video will help you understand what is linear regression, why linear regression, you will see how linear regression works using a simple example and you will also see a use case predicting the revenue of a company using linear regression. Linear Regression is the statistical model used to predict the relationship between independent and dependent variables by examining two factors. The first one is which variables, in particular, are significant predictors of the outcome variable and the second one is how significant is the regression line to make predictions with the highest possible accuracy. Now, lets deep dive into this video and understand what is linear regression.
Below topics are explained in this "Linear Regression in R" video:
1. Why linear regression? ( 00:28 )
2. What is linear regression? ( 03:09 )
3. How linear regression works? ( 03:48 )
4. Use case - Predicting the revenue using linear regression (10:05)
To learn more about Data Science, subscribe to our YouTube channel: https://www.youtube.com/user/Simplilearn?sub_confirmation=1
You can also go through the Slides here: https://goo.gl/HBso29
Watch more videos on Data Science: https://www.youtube.com/watch?v=0gf5iLTbiQM&list=PLEiEAq2VkUUIEQ7ENKU5Gv0HpRDtOphC6
#DataScienceWithR #DataScienceCourse #DataScience #DataScientist #BusinessAnalytics #MachineLearning
Become an expert in data analytics using the R programming language in this data science certification training course. You’ll master data exploration, data visualization, predictive analytics and descriptive analytics techniques with the R language. With this data science course, you’ll get hands-on practice on R CloudLab by implementing various real-life, industry-based projects in the domains of healthcare, retail, insurance, finance, airlines, music industry, and unemployment.
Why learn Data Science with R?
1. This course forms an ideal package for aspiring data analysts aspiring to build a successful career in analytics/data science. By the end of this training, participants will acquire a 360-degree overview of business analytics and R by mastering concepts like data exploration, data visualization, predictive analytics, etc
2. According to marketsandmarkets.com, the advanced analytics market will be worth $29.53 Billion by 2019
3. Wired.com points to a report by Glassdoor that the average salary of a data scientist is $118,709
4. Randstad reports that pay hikes in the analytics industry are 50% higher than IT
The Data Science Certification with R has been designed to give you in-depth knowledge of the various data analytics techniques that can be performed using R. The data science course is packed with real-life projects and case studies, and includes R CloudLab for practice.
1. Mastering R language: The data science course provides an in-depth understanding of the R language, R-studio, and R packages. You will learn the various types of apply functions including DPYR, gain an understanding of data structure in R, and perform data visualizations using the various graphics available in R.
2. Mastering advanced statistical concepts: The data science training course also includes various statistical concepts such as linear and logistic regression, cluster analysis and forecasting. You will also learn hypothesis testing.
3. As a part of the data science with R training course, you will be required to execute real-life projects using CloudLab. The compulsory projects are spread over four case studies in the domains of healthcare, retail, and the Internet. Four additional projects are also available for further practice.
The Data Science with R is recommended for:
1. IT professionals looking for a career switch into data science and analytics
2. Software developers looking for a career switch into data science and analytics
3. Professionals working in data and business analytics
4. Graduates looking to build a career in analytics and data science
5. Anyone with a genuine interest in the data science field
6. Experienced professionals who would like to harness data science in their fields
Learn more at: https://www.simplilearn.com/big-data-and-analytics/data-scientist-certification-sas-r-excel-training?utm_campaign=Linear-Regression-in-R-2Sb1Gvo5si8&utm_medium=Tutorials&utm_source=youtube
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Views: 3163
Simplilearn

In this video, we learn how to setup a simple linear regression model using R

Views: 1827
Shokoufeh Mirzaei

In this video you will learn how to do simple linear Regression Analysis using R
Get all videos on our website : http://analyticuniversity.com/
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Views: 13064
Analytics University

Simple and Multiple Linear Regression in R
https://sites.google.com/site/econometricsacademy/econometrics-models/linear-regression

Views: 26681
econometricsacademy

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: 6569
StatQuest with Josh Starmer

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: 177910
Tutorlol

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: 10043
IvyProSchool

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: 46210
ironfrown

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: 16758
statisticsfun

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: 191936
Allen Mursau

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: 52015
statisticsfun

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/

Views: 4924
StatQuest with Josh Starmer

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: 5457
Abhishek Agarrwal

Simple linear regression method is demonstrated in R Studio which is an integrated development environment for R. R Studio is freely available.

Views: 17525
kartikeya bolar

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: 5824
R at Colby

Hello friends,
It will help in running regression and extracting all the required outputs from the results.

Views: 7765
Sarveshwar Inani

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: 346117
statisticsfun

Association between two numerical variables with R

Views: 1403
Gilles Lamothe

This video offers an overview of Linear Regression Analysis using R. Linear regression is a common technique to determine how one variable of interest is affected by another. It is used to predict values of other variables from those that have more data for corroboration, and also for correcting the linear dependence of one variable on the other.
Get your Data Scientist Certification Training Here - http://www.simplilearn.com/big-data-and-analytics/data-scientist-certification-sas-r-excel-training?utm_campaign=Linear-Regression-Regression-Analysis-OsJ-ho-IQsY&utm_medium=SC&utm_source=youtube
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Views: 5957
Simplilearn

In this lesson, we learn how to run a categorical regression model in R.

Views: 2526
Shokoufeh Mirzaei

R-Squared or Coefficient of Determination
Watch the next lesson: https://www.khanacademy.org/math/probability/regression/regression-correlation/v/calculating-r-squared?utm_source=YT&utm_medium=Desc&utm_campaign=ProbabilityandStatistics
Missed the previous lesson?
https://www.khanacademy.org/math/probability/regression/regression-correlation/v/second-regression-example?utm_source=YT&utm_medium=Desc&utm_campaign=ProbabilityandStatistics
Probability and statistics on Khan Academy: We dare you to go through a day in which you never consider or use probability. Did you check the weather forecast? Busted! Did you decide to go through the drive through lane vs walk in? Busted again! We are constantly creating hypotheses, making predictions, testing, and analyzing. Our lives are full of probabilities! Statistics is related to probability because much of the data we use when determining probable outcomes comes from our understanding of statistics. In these tutorials, we will cover a range of topics, some which include: independent events, dependent probability, combinatorics, hypothesis testing, descriptive statistics, random variables, probability distributions, regression, and inferential statistics. So buckle up and hop on for a wild ride. We bet you're going to be challenged AND love it!
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Khan Academy

Checking Linear Regression Assumptions in R ;
Dataset: https://goo.gl/tJj5XG; Linear Regression Concept and with R: https://bit.ly/2z8fXg1;
More Statistics and R Programming Tutorials: https://goo.gl/4vDQzT;
How to test linear regression assumptions in R?
In this R tutorial, we will first go over some of the concepts for linear regression like how to add a regression line, how to interpret the regression line (predicted or fitted Y value, the mean of Y given X), how to interpret the residuals or errors (the difference between observed Y value and the predicted or fitted Y value) and the assumptions when fitting a linear regression model.
Then we will discuss the regression diagnostic plots in R, the reason for making diagnostic plots, and how to produce these plots in R; You will learn to check the linearity assumption and constant variance (homoscedasticity) for a regression model with residual plots in R and test the assumption of normality in R with QQ (Quantile Quantile) plots. You will also learn to check the constant variance assumption for data with non-constant variance in R, produce and interpret residual plots, QQ plots, and scatterplots for data with non-constant variance, and produce and interpret residual plots, QQ plots, and scatterplots for data with non-linear relationship in R.
►► Download the dataset here:
https://statslectures.com/r-stats-datasets
►► Watch More:
►Linear Regression Concept and Linear Regression with R Series: https://bit.ly/2z8fXg1
►Simple Linear Regression Concept https://youtu.be/vblX9JVpHE8
►Nonlinearity in Linear Regression https://youtu.be/tOzwEv0PoZk
► R Squared of Coefficient of Determination https://youtu.be/GI8ohuIGjJA
► Linear Regression in R Complete Series https://bit.ly/1iytAtm
■ Table of Content:
0:00:29 Introducing the data used in this video
0:00:49 How to fit a Linear Regression Model in R?
0:01:03 how to produce the summary of the linear regression model in R?
0:01:15 How to add a regression line to the plot in R?
0:01:24 How to interpret the regression line?
0:01:43 How to interpret the residuals or errors?
0:01:53 where to find the Residual Standard Error (Standard Deviation of Residuals) in R
0:02:14 What are the assumptions when fitting a linear regression model and how to check these assumptions
0:03:01 What are the built-in regression diagnostic plots in R and how to produce them
0:03:24 How to use Residual Plot for testing linear regression assumptions in R
0:03:50 How to use QQ-Plot in R to test linear regression assumptions
0:04:33 How to produce multiple plots on one screen in R
0:05:00 How to check constant variance assumption for data with non-constant variance in R
0:05:12 How to produce and interpret a Scatterplot and regression line for data with non-constant variance
0:05:40 How to produce and interpret the Residual plot for data with non-constant variance in R
0:06:02 How to produce and interpret the QQ plot for data with non-constant variance in R
0:06:12 How to produce and interpret a Scatterplot with regression line for data with non-linear relationship in R
0:06:40 How to produce and interpret the Residual plot for a data with non-linear relationship in R
0:06:52 How to produce and interpret the QQ plot for a data with non-linear relationship in R
0:07:02 what is the reason for making diagnostic plots
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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 #RTutorials are created by #marinstatslectures to support a course at The University of British Columbia (#UBC) 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: 151665
MarinStatsLectures-R Programming & Statistics

This video is an assignment for IN4400 Programming and Data Science for 99% Course, in TU Delft 2016/2017.

Views: 1921
Septian Gilang Permana Putra

Learn how to include a categorical variable (a factor or qualitative variable) in a regression model in R. You will also learn how to interpret the model coefficients. The video provides a tutorial for programming in R Statistical Software for beginners.
You can access and download the "LungCapData" dataset here:
Excel format: https://bit.ly/LungCapDataxls
Tab Delimited Text File: https://bit.ly/LungCapData
Here is a brief overview of the topics addressed in this video:

Views: 66010
MarinStatsLectures-R Programming & Statistics

The theory of fitting polynomial regression models in R.

Views: 11722
Tom Sherratt

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 prepare variables while employing a stepwise regression with backward elimination of variables. The lesson explains how to transform highly skewed variables (using Log10 transform) and later report their characteristics, how to check variable normality and their multiple collinearity (using Variance Inflation Factors) and their extreme values (using Cook's distance). The process will be guided by the measures of model quality, such as R-Squared and Adjusted R-Squared statistics, and variables' p-values, which represent the level of coefficient confidence. As always, 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, with correction for the previously transformed variables. The explanation will be quite informal and will avoid the more complex statistical concepts. Note that visual presentation and interpretation of multiple regression results 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-D2-Multiple-Reg-Var-Prep-No-Interact.r
Videos in data analytics and data visualization by Jacob Cybulski, visanalytics.org.

Views: 11142
ironfrown

This video describes three approaches to data visualization for multidimensional data, which is typical for data exploration in multiple regression modelling using R. Examples used throughout the lesson utilize a three dimensional regression model. The first method introduced in the video shows how to fix one of the variables at multiple predetermined levels to investigate its impact on the relationship between the remaining variables. The second approach relies on the use of R coplots, which allow one (or two) of the variables to be used as conditioning the plot representing the relationship between the remaining two variables. The overlapping ranges of the conditioning variable's values can be used to produce multiple charts, each plotted in a separate panel to provide a glimpse into the entire three dimensional variable dependency. The third and last technique involves the use of 3D plots with regression hyper-planes. Other aspects of data visualization include plotting with transformed and raw units, use of Loess 2D and 3D fit to determine linearity, impact of data cleansing on the visual data representation and observation of data clusters to establish regularity of data distribution. The explanation will be quite informal and will avoid the more complex statistical concepts.
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-D3-Multiple-Reg-Vis.r
Videos in data analytics and data visualization by Jacob Cybulski, visanalytics.org.

Views: 12680
ironfrown

How should you interpret R squared? what does it really tell us?
this video should help

Views: 181501
MrNystrom

Learn how to include interaction or effect modification in a regression model in R. You will also learn how to interpret the model coefficients. The video provides a tutorial for programming in R Statistical Software for beginners.
You can access and download the "LungCapData" dataset here:
Excel format: https://bit.ly/LungCapDataxls
Tab Delimited Text File: https://bit.ly/LungCapData
You can access and download the R code here:
Click here to open with R : http://bit.ly/1H4RLWr
Click here for the text file: http://bit.ly/1JK7QXF

Views: 77318
MarinStatsLectures-R Programming & Statistics

Learn about regression and r-squared
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Views: 7531
Simple Learning Pro

Talking through 3 model selection procedures: forward, backward, stepwise.

Views: 95614
Phil Chan

Lecturer: Dr. Erin M. Buchanan
Missouri State University
Spring 2016
This video covers how to run and interpret hierarchical multiple linear regression with continuous variables. We walk through data screening, outliers, assumptions, and running the linear model.
Note: This video was recorded live during class - it will have pauses, changes in voice loudness as I wander around the room, and ridiculous jokes. If anything is unclear, please leave a comment, and I will clarify.
Lecture materials and assignment available at statstools.com.
http://statstools.com/learn/advanced-statistics/

Views: 4081
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