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29:51
One of the first steps to data analysis is to perform Exploratory Data Analysis. In this video we go over the basics of multivariate data analysis, or analyzing the relationship between variables Here's the dataset used in this video: https://drive.google.com/open?id=0B67hcgV97X0mbnRYNzhYLU53X2c
Views: 9343 James Dayhuff

37:40
For this seminar, I will take you through a general introduction of multivariate analysis and perform an R demonstration of a simple multivariate analysis: mean comparison.
Views: 4072 RenaissanceWoman

13:35
Introduction to multiple regression in r. The data set is discussed and exploratory data analysis is performed here using correlation matrix and scatterplot matrix.

26:11
We explore some multivariate descriptive tools here. Scatterplot matrix, side-by-side boxplot, two-way crosstab, correlation matrix, and more.

05:19
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!

11:13
See my new blog at http://rollingyours.wordpress.com Get code used in this video from: https://raw.githubusercontent.com/steviep42/youtube/master/YOUTUBE.DIR/BB_phys_stats_ex1.R Best Viewed in Large or Full Screen Mode Part 1 - This video tutorial guides the user through a manual principal components analysis of some simple data. The goal is to acquaint the viewer with the underlying concepts and terminology associated with the PCA process. This will be helpful when the user employs one of the "canned" R procedures to do PCA (e.g. princomp, prcomp), which requires some knowledge of concepts such as loadings and scores.
Views: 141474 Steve Pittard

07:43
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/

16:10
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: 35279 Bharatendra Rai

18:36
Paper: Multivariate Analysis Module name: Introduction toMultivariate Analysis Content Writer: Souvik Bandyopadhyay
Views: 67072 Vidya-mitra

17:15
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

00:16
This video is part of an online course, Data Analysis with R. Check out the course here: https://www.udacity.com/course/ud651. This course was designed as part of a program to help you and others become a Data Analyst. You can check out the full details of the program here: https://www.udacity.com/course/nd002.
Views: 3031 Udacity

27:39
This is the demonstration part related to the Session 5 of the lecture "Applied Multivariate Statistics for Environmental Scientists" that was held at the University Koblenz-Landau, Campus Landau. The demonstration relies on free open source software (R) and can therefore be followed by anyone. The topic of this session is Redundancy Analysis. For more information go to the website: https://www.uni-koblenz-landau.de/en/campus-landau/faculty7/environmental-sciences/landscape-ecology/Teaching/r-statistics
Views: 4130 Ralf Schaefer

59:39
Views: 2716 Ralf Schaefer

18:47
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: 50867 ironfrown

04:14
Views: 3000 Sulthan's Monologue

48:17
Views: 7149 Camo Analytics

25:45
This is the demonstration part related to the Session 6 of the lecture "Applied Multivariate Statistics for Environmental Scientists" that was held at the University Koblenz-Landau, Campus Landau. The demonstration relies on free open source software (R) and can therefore be followed by anyone. The topic of this session is multivariate hypothesis testing. For more information go to the website: https://www.uni-koblenz-landau.de/en/campus-landau/faculty7/environmental-sciences/landscape-ecology/Teaching/r-statistics
Views: 751 Ralf Schaefer

01:09:54
For this video, I will give you the background theory and perform R demonstrations for one-way and factorial Multivariate Analysis of Variance (MANOVA). MANOVA is used for comparing mean vectors containing the means of multiple outcome variables between more group variables with more than 2 categories. It's the multivariate extension of the ANOVA. For more details of the 4 Statistics: https://onlinecourses.science.psu.edu/stat505/node/163 Calculate the F approximation of the 4 statistics: https://support.sas.com/documentation/cdl/en/statug/63033/HTML/default/viewer.htm#statug_introreg_sect012.htm Like Me On FB: https://www.facebook.com/RenaissanceMonaLisa/?pnref=lhc
Views: 2778 RenaissanceWoman

10:00
Multivariate time series models are different from that of Univariate Time Series models in a way that it also takes structural forms that is it includes lags of different time series variable apart from the lags of it's own. For Study Packs Visit : http://analyticuniversity.com/
Views: 29189 Analytics University

11:09
boral is an R package designed for Bayesian analysis of multivariate data (e.g., community composition data) in ecology. UPDATE June 2016: - There is a software article now on boral, available on the Methods in Ecology and Evolution website: http://onlinelibrary.wiley.com/doi/10.1111/2041-210X.12514/full - A second video discussing some of the updates to boral that have been made since 2014 can be found here: https://www.youtube.com/watch?v=XmrVVMG1HXI Check it out: http://cran.r-project.org/web/package... Disclaimer: I am in no way affiliated with the construction company in Australia also known as BORAL, although my voice is probably as dry as their cement =P
Views: 5797 Francis Hui

01:15:24
Session 5 of the lecture "Applied Multivariate Statistics for Environmental Scientists". The lecture relies on free open source software (R) and can therefore be followed by anyone. The topic of this session is Redundancy Analysis, similarity measures and non-metric multidimensional scaling (NMDS). For more information go to the website: https://www.uni-koblenz-landau.de/en/campus-landau/faculty7/environmental-sciences/landscape-ecology/Teaching/r-statistics
Views: 6721 Ralf Schaefer

05:07
This video will show you how to make scatterplots, matrix plots and calculate Pearson's, Spearman's and Kendall's correlation coefficients.
Views: 162439 Ed Boone

25:09
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: 99524 Christoph Scherber

10:15
This video explains how to test multivariate normality assumption of data-set/ a group of variables using R software. Two formal tests along with Q-Q plot are also demonstrated. #Research #Academia #Multivariate #Univariate #Normality #Test #Normal #Distribution #Bengali #Bangladesh #West-Bengal #Education #Statistics #Econometric #Economics #Mardia #Henze-Zirkler #Q-Q #plot #Standard #R #Codes install.packages("MVN") library(MVN) describe(dataset) hist(dataset) #multivariate normality test #mardia test result=mardiaTest(dataset, qqplot=TRUE) result #Henze-Zirkler test result=hzTest(dataset, qqplot=TRUE) result #bonus #univariate plots uniPlot(dataset, type="qqplot") #creates univariate Q-Q plots uniPlot(dataset, type="histogram") #creates univariate histograms
Views: 3124 Research HUB

01:21:06
R demo related to the session 2 of the lecture "Applied Multivariate Statistics for Environmental Scientists". University Koblenz-Landau, Campus Landau in Germany, winter semester 2016/17. The topic of this session is Multiple Linear Regression and includes methods and strategies, including modern approaches such as the LASSO. For more information go to the website: https://www.uni-koblenz-landau.de/en/campus-landau/faculty7/environmental-sciences/landscape-ecology/Teaching/r-statistics
Views: 1604 Ralf Schaefer

18:11
Provides illustration of doing cluster analysis with R. R File: https://goo.gl/BTZ9j7 Machine Learning videos: https://goo.gl/WHHqWP Includes, - Illustrates the process using utilities data - data normalization - hierarchical clustering using dendrogram - use of complete and average linkage - calculation of euclidean distance - silhouette plot - scree plot - nonhierarchical k-means clustering Cluster analysis is an important tool related to analyzing big data or working in data science field. Deep Learning: https://goo.gl/5VtSuC Image Analysis & Classification: https://goo.gl/Md3fMi 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: 111894 Bharatendra Rai

20:16
This is the demonstration part related to the Session 5 of the lecture "Applied Multivariate Statistics for Environmental Scientists" that was held at the University Koblenz-Landau, Campus Landau. The demonstration relies on free open source software (R) and can therefore be followed by anyone. The topic of this session is similarity measures and non-metric multidimensional scaling (NMDS). For more information go to the website: https://www.uni-koblenz-landau.de/en/campus-landau/faculty7/environmental-sciences/landscape-ecology/Teaching/r-statistics
Views: 3866 Ralf Schaefer

15:45

11:10
How to use Factoshiny, the library that allows us to use FactoMineR with a graphical user interface and that allow us to make interactive graphs.
Views: 5976 François Husson

47:20
Session 7 of the lecture "Applied Multivariate Statistics for Environmental Scientists". The demonstration relies on free open source software (R) and can therefore be followed by anyone. The topic of this session is Cluster analysis (Hierarchical agglomerative and kmeans). For more information go to the website: https://www.uni-koblenz-landau.de/en/campus-landau/faculty7/environmental-sciences/landscape-ecology/Teaching/r-statistics
Views: 568 Ralf Schaefer

01:18

08:58
1. What is MANOVA 2. Difference between MANOVA and ANOVA 3. NULL Hypothesis of MANOVA 4. Execution steps of MANOVA 5. MANOVA using R
Views: 9180 Gopal Malakar

08:38
Mahalanobis Distance - Outlier Detection for Multivariate Statistics in R
Views: 5832 Dragonfly Statistics

02:47
Views: 618 Katie Ann Jager

46:14
This is the 3rd part of my Multivariate Statistical Analysis video series on Multivariate Analysis of Covariance (MANCOVA). MANCOVA is a method for looking at if the means of the multiple outcome variables between each group (specified by the group variable) are statistically different while controlling for a covariate which can influence the outcome but is not of study interest. Like Me On FB: https://www.facebook.com/RenaissanceMonaLisa/
Views: 1261 RenaissanceWoman

00:32
Views: 16 Ashley Brown

09:27
Time Series Analysis PLAYLIST: https://tinyurl.com/TimeSeriesAnalysis-GeorgiaTech Unit 3: Multivariate Time Series Modelling Part 1: Multivariate Time Series Lesson: 3 - Multivariate Time Series - Data Examples Notes, Code, Data: https://tinyurl.com/Time-Series-Analysis-NotesData
Views: 810 Bob Trenwith

40:35
Topics include interpreting sensory data via PCA, rotation of scores, and preference mapping with PCR.
Views: 2865 Camo Analytics

01:05:51
This is the session 1 of the lecture "Applied Multivariate Statistics for Environmental Scientists" that was held at the University Koblenz-Landau, Campus Landau in Germany in the winter semester 2015/2016. The lecture relies on free open source software (R) and can therefore be followed by anyone. The topics of this session are Introduction and revisiting univariate statistics. For more information look at the course website: https://www.uni-koblenz-landau.de/en/campus-landau/faculty7/environmental-sciences/landscape-ecology/Teaching/r-statistics
Views: 7896 Ralf Schaefer

55:17
This is the demonstration part related to the Session 4 of the lecture "Applied Multivariate Statistics for Environmental Scientists" that was held at the University Koblenz-Landau, Campus Landau. The demonstration relies on free open source software (R) and can therefore be followed by anyone. The topic of this session is Introduction to multivariate analysis and Principal Component Analysis (PCA). For more information go to the website: https://www.uni-koblenz-landau.de/en/campus-landau/faculty7/environmental-sciences/landscape-ecology/Teaching/r-statistics
Views: 759 Ralf Schaefer

45:52
This is part of the course 02417 Time Series Analysis as it was given in the fall of 2017 and spring 2018. The full playlist is here: https://www.youtube.com/playlist?list=PLtiTxpFJ4k6TZ0g496fVcQpt_-XJRNkbi You can download the slides here: https://drive.google.com/drive/folders/1OYamq8_PONteNHEdgkEG-jLvraeaGOp6?usp=sharing The course is based on the book: Time Series Analysis by Henrik Madsen: http://henrikmadsen.org/books/time-series-analysis/

48:27
Session 7 of the lecture "Applied Multivariate Statistics for Environmental Scientists" that was held at the University Koblenz-Landau, Campus Landau in Germany in the winter semester 2015/2016. The lecture relies on free open source software (R) and can therefore be followed by anyone. The topic of this session is Cluster analysis. For more information go to the website: https://www.uni-koblenz-landau.de/en/campus-landau/faculty7/environmental-sciences/landscape-ecology/Teaching/r-statistics
Views: 675 Ralf Schaefer

57:06
Views: 71456 edureka!

06:33
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: 334680 TheWoundedDoctor

00:27
Views: 64 Robert Ho

16:07
Provides steps for handling missing data using mice (multivariate imputation by chained equations) package. R code: https://goo.gl/kUzRin Data file: https://goo.gl/aksd3E More ML videos: https://goo.gl/WHHqWP To install mice, use following codes: install.packages("devtools") devtools::install_github(repo = "stefvanbuuren/mice") handling missing values is an important step related to analyzing big data or working in data science field. 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: 4364 Bharatendra Rai

10:35