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R: Exploratory Data Analysis (EDA), Multivariate Analysis
 
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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
Multivariate Statistical Analysis Part I: Introduction and Mean Comparison (with R demonstration)
 
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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
R - Multiple Regression (part 1)
 
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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.
Views: 45049 Jalayer Academy
R - Exploring Data (part 5) - Multivariate Summaries
 
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We explore some multivariate descriptive tools here. Scatterplot matrix, side-by-side boxplot, two-way crosstab, correlation matrix, and more.
Views: 11366 Jalayer Academy
Multiple Linear Regression in R | R Tutorial 5.3 | MarinStatsLectures
 
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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!
Principal Components Analysis Using R - P1
 
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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
StatQuest: Multiple Regression in R
 
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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/
Multiple Linear Regression in R
 
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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: 35279 Bharatendra Rai
Introduction to Multivariate Analysis
 
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Paper: Multivariate Analysis Module name: Introduction toMultivariate Analysis Content Writer: Souvik Bandyopadhyay
Views: 67072 Vidya-mitra
Logistic Regression in R, Clearly Explained!!!!
 
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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
Multivariate Data - Data Analysis with R
 
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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
Session 5 Applied Multivariate statistics RDA - Demonstration in R
 
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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
R Stats: Multiple Regression - Variable Selection
 
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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: 50867 ironfrown
Multivariate Test of normality - Mardia, Henze - Zirkler, Royston test using R / R studio
 
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Multivariate Test of normality - Mardia, Henze - Zirkler, Royston test using R / R studio Website: http://www.iamsulthan.in Facebook page: https://www.facebook.com/www.iamsulthan.in Twitter:https://twitter.com/sulthankhan LinkedIn: https://in.linkedin.com/in/sulthan-a-6502a446 Google plus: https://plus.google.com/u/0/114871954491126528103 YouTube: https://www.youtube.com/channel/UCvSmgyIUCxxgoHKlwutH7kQ Fb profile page:https://www.facebook.com/iamsulthan.in Instagram: https://www.instagram.com/iamsulthan/ Research Gate: https://www.researchgate.net/profile/sulthan_a2 Google scholar: https://scholar.google.co.in/citations?hl=en&user=nwM3-60AAAAJ Academia: https://jmc.academia.edu/IamSulthan Scopus: https://www.scopus.com/authid/detail.uri?authorId=56127320100 Orcid: http://orcid.org/0000-0003-4379-9046
Views: 3000 Sulthan's Monologue
Session 6 Applied Multivariate statistics - Multivariate hypothesis testing - Demonstration in R
 
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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
Multivariate Statistical Analysis Part 2:  MANOVA (with R Demonstration)
 
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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
What are Multivariate Time Series Models
 
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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
boral: R package for multivariate data analysis in Ecology
 
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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
Session 5 Applied Multivariate statistics - RDA, similarity measures and NMDS
 
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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
Correlation in R
 
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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
Statistics with R (2) - Multiple regression with an interaction term
 
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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: 99524 Christoph Scherber
Testing Multivariate Normality using R [Bengali]
 
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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
Session 2 Applied Multivariate Statistics Multiple Regression R demo
 
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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
Introduction to Cluster Analysis with R - an Example
 
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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
Session 5 Applied Multivariate statistics Similarity measures and NMDS - Demonstration in R
 
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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
Principal Component Analysis and Factor Analysis in R
 
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Principal Component Analysis and Factor Analysis in R https://sites.google.com/site/econometricsacademy/econometrics-models/principal-component-analysis
Views: 106391 econometricsacademy
Factoshiny: interactive graphs in exploratory multivariate data analysis
 
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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
Session 7 Applied Multivariate statistics - Cluster analysis - Demonstration in R
 
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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
Introduction to MANOVA, MANOVA vs ANOVA n MANOVA using R
 
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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
Mahalanobis Distance - Outlier Detection for Multivariate Statistics in R
 
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Mahalanobis Distance - Outlier Detection for Multivariate Statistics in R
Views: 5832 Dragonfly Statistics
Multivariate Statistical Analysis Part 3: MANCOVA (with R Demonstration)
 
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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
Time Series Analysis (Georgia Tech) - 3.1.3 - Multivariate Time Series - Data Examples
 
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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
Multivariate Data Analysis of Sensory data 28 Oct 2015
 
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Topics include interpreting sensory data via PCA, rotation of scores, and preference mapping with PCR.
Views: 2865 Camo Analytics
Session 1 Applied Multivariate Statistics - Part 1: Lecture
 
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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
Session 4 Applied Multivariate statistics Principal component analysis demonstration in R
 
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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
02417 Lecture 10 part A: Marima package in R for multivariate ARMA models
 
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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/
Session 7 Applied Multivariate statistics Clustering demonstration R
 
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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
Linear Regression Algorithm | Linear Regression in R | Data Science Training | Edureka
 
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( 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 Subscribe to our channel to get video updates. Hit the subscribe button above. Check our complete Data Science playlist here: https://goo.gl/60NJJS #LinearRegression #Datasciencetutorial #Datasciencecourse #datascience How it Works? 1. There will be 30 hours of instructor-led interactive online classes, 40 hours of assignments and 20 hours of project 2. We have a 24x7 One-on-One LIVE Technical Support to help you with any problems you might face or any clarifications you may require during the course. 3. You will get Lifetime Access to the recordings in the LMS. 4. At the end of the training you will have to complete the project based on which we will provide you a Verifiable Certificate! - - - - - - - - - - - - - - About the Course Edureka's Data Science course will cover the whole data life cycle ranging from Data Acquisition and Data Storage using R-Hadoop concepts, Applying modelling through R programming using Machine learning algorithms and illustrate impeccable Data Visualization by leveraging on 'R' capabilities. - - - - - - - - - - - - - - Why Learn Data Science? Data Science training certifies you with ‘in demand’ Big Data Technologies to help you grab the top paying Data Science job title with Big Data skills and expertise in R programming, Machine Learning and Hadoop framework. After the completion of the Data Science course, you should be able to: 1. Gain insight into the 'Roles' played by a Data Scientist 2. Analyse Big Data using R, Hadoop and Machine Learning 3. Understand the Data Analysis Life Cycle 4. Work with different data formats like XML, CSV and SAS, SPSS, etc. 5. Learn tools and techniques for data transformation 6. Understand Data Mining techniques and their implementation 7. Analyse data using machine learning algorithms in R 8. Work with Hadoop Mappers and Reducers to analyze data 9. Implement various Machine Learning Algorithms in Apache Mahout 10. Gain insight into data visualization and optimization techniques 11. Explore the parallel processing feature in R - - - - - - - - - - - - - - Who should go for this course? The course is designed for all those who want to learn machine learning techniques with implementation in R language, and wish to apply these techniques on Big Data. The following professionals can go for this course: 1. Developers aspiring to be a 'Data Scientist' 2. Analytics Managers who are leading a team of analysts 3. SAS/SPSS Professionals looking to gain understanding in Big Data Analytics 4. Business Analysts who want to understand Machine Learning (ML) Techniques 5. Information Architects who want to gain expertise in Predictive Analytics 6. 'R' professionals who want to captivate and analyze Big Data 7. Hadoop Professionals who want to learn R and ML techniques 8. Analysts wanting to understand Data Science methodologies For more information, Please write back to us at [email protected] or call us at IND: 9606058406 / US: 18338555775 (toll free). Instagram: https://www.instagram.com/edureka_learning/ Facebook: https://www.facebook.com/edurekaIN/ Twitter: https://twitter.com/edurekain LinkedIn: https://www.linkedin.com/company/edureka Customer Reviews: Gnana Sekhar Vangara, Technology Lead at WellsFargo.com, says, "Edureka Data science course provided me a very good mixture of theoretical and practical training. The training course helped me in all areas that I was previously unclear about, especially concepts like Machine learning and Mahout. The training was very informative and practical. LMS pre recorded sessions and assignmemts were very good as there is a lot of information in them that will help me in my job. The trainer was able to explain difficult to understand subjects in simple terms. Edureka is my teaching GURU now...Thanks EDUREKA and all the best. "
Views: 71456 edureka!
Multiple Regression Interpretation in Excel
 
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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
Handling Missing Values using R
 
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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
4 2 Simulating Multivariate Time Series in R
 
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http://quantedu.com/wp-content/uploads/2014/04/Time%20Series/4_2%20Simulate_Multivariate
Views: 10819 Quant Education
How to Calculate Multiple Linear Regression with SPSS
 
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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: 209445 statisticsfun
Metabolomic Data Analysis Using MetaboAnalyst
 
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This is the fifth module in the 2016 Informatics and Statistics for Metabolomics workshop hosted by the Canadian Bioinformatics Workshops. This lecture is by David Wishart from the University of Alberta. How it Begins by Kevin MacLeod is licensed under a Creative Commons Attribution license (https://creativecommons.org/licenses/by/4.0/) Source: http://incompetech.com/music/royalty-free/index.html?isrc=USUAN1100200 Artist: http://incompetech.com/ Table of Contents: 00:10 - 00:56 - Learning Objectives 01:15 - A Typical Metabolomics Experiment 03:16 - 2 Routes to Metabolomics 03:31 - Metabolomics Data Workflow 05:11 - Data Integrity/Quality 06:46 - Data/Spectral Alignment 07:28 - Binning (3000 pts to 14 bins) 07:53 - Data Normalization/Scaling 09:40 - Data Normalization/Scaling 10:20 - Data QC, Outlier Removal & Data Reduction 12:08 - MetaboAnalyst 13:25 - MetaboAnalyst History 14:37 - MetaboAnalyst Overview 16:00 - MetaboAnalyst Modules 17:00 - MetaboAnalyst Modules 18:03 - Example Datasets 19:09 - Example Datasets 19:34 - Metabolomic Data Processing 19:44 - Example Datasets 19:45 - Example Datasets 19:45 - MetaboAnalyst Modules 19:48 - Example Datasets 19:48 - Example Datasets 19:49 - Metabolomic Data Processing 19:51 - Common Tasks 20:38 - Select a Module (Statistical Analysis) 20:48 - Common Tasks 20:49 - Metabolomic Data Processing 20:49 - Example Datasets 20:49 - Example Datasets 20:55 - Example Datasets 20:55 - Metabolomic Data Processing 20:56 - Common Tasks 20:56 - Select a Module (Statistical Analysis) 21:07 - Data Upload 21:37 - Alternatively … 22:18 - Data Set Selected 23:24 - Data Integrity Check 24:11 - Data Normalization 25:51 - Data Normalization 26:28 - Data Normalization 27:40 - Normalization Result 29:49 - Data Normalization 29:59 - Normalization Result 30:03 - Data Normalization 30:14 - Normalization Result 30:15 - Data Normalization 30:15 - Data Normalization 30:16 - Data Normalization 30:21 - Data Normalization 30:21 - Data Normalization 30:36 - Normalization Result 31:24 - Data Normalization 31:26 - Next Steps 31:37 - Quality Control 32:41 - Visual Inspection 33:36 - Outlier Removal (Data Editor) 34:01 - Noise Reduction (Data Filtering) 35:09 - Noise Reduction (cont.) 35:36 - Data Reduction and Statistical Analysis 36:07 - Common Tasks 36:30 - 37:00 - ANOVA 37:31 - ANOVA 38:58 - View Individual Compounds 39:46 - What’s Next? 40:06 - Overall Correlation Pattern 41:20 - High Resolution Image 41:53 - What’s Next? 42:40 - Pattern Matching 43:29 - Pattern Matching (cont.) 44:57 - 45:16 - Pattern Matching (cont.) 45:17 - Pattern Matching 45:32 - Pattern Matching (cont.) 45:34 - 45:55 - Multivariate Analysis 46:58 - PCA Scores Plot 47:40 - PCA Loading Plot 47:58 - PCA Scores Plot 48:15 - PCA Loading Plot 49:14 - 3D Score Plot 50:28 - 51:06 - 3D Score Plot 51:07 - PCA Loading Plot 51:08 - PCA Scores Plot 51:46 - PCA Loading Plot 51:47 - 3D Score Plot 51:49 - 52:30 - Multivariate Analysis 52:54 - PLS-DA Score Plot 53:23 - Evaluation of PLS-DA Model 55:19 - Important Compounds 57:07 - Model Validation 58:07 - 58:16 - Hierarchical Clustering (Heat Maps) 58:34 - Heatmap Visualization 58:48 - Heatmap Visualization (cont.) 59:16 - What’s Next? 59:28 - Heatmap Visualization (cont.) 59:29 - Heatmap Visualization 59:32 - Heatmap Visualization (cont.) 01:00:18 - What’s Next? 01:02:45 - Download Results 01:02:56 - Analysis Report 01:03:21 - Select a Module (Enrichment Analysis) 01:03:29 - Metabolite Set Enrichment Analysis (MSEA) 01:04:18 - Enrichment Analysis 01:04:56 - MSEA 01:05:09 - The MSEA Approach 01:05:18 - Data Set Selected 01:06:02 - Start with a Compound List for ORA 01:06:10 - Upload Compound List 01:06:38 - Perform Compound Name Standardization 01:06:55 - Name Standardization (cont.) 01:07:11 - Select a Metabolite Set Library 01:07:46 - Result 01:08:39 - Result (cont.) 01:09:04 - The Matched Metabolite Set 01:09:18 - Phenylalanine and Tyrosine Metabolism in SMPDB 01:09:42 - Single Sample Profiling (SSP) (Basically used by a physician to analyze a patient) 01:10:14 - Concentration Comparison 01:10:34 - Concentration Comparison (cont.) 01:11:13 - Quantitative Enrichment Analysis (QEA) 01:11:30 - Result 01:11:47 - The Matched Metabolite Set 01:12:02 - Select a Module (Pathway Analysis) 01:12:09 - Pathway Analysis 01:12:57 - Data Set Selected 01:13:03 - Pathway Analysis Module 01:13:09 - Data Upload 01:13:22 - Perform Data Normalization 01:13:38 - Select Pathway Libraries 01:13:54 - Perform Network Topology Analysis 01:14:04 - Pathway Position Matters 01:14:44 - Which Node is More Important? 01:14:54 - Pathway Position Matters 01:14:56 - Which Node is More Important? 01:15:01 - Pathway Visualization 01:17:15 - Pathway Visualization (cont.) 01:17:28 - Pathway Impact 01:17:43 - Result 01:17:54 - Select a Module (Biomarker Analysis) 01:18:58 - Biomarker Analysis 01:19:37 - Select Test Data Set 1 01:19:42 - Data Set Selected 01:20:34 - Perform Data Integrity Check
Views: 10342 Bioinformatics DotCa
Decision Tree with R | Complete Example
 
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Also called Classification and Regression Trees (CART) or just trees. R file: https://goo.gl/Kx4EsU Data file: https://goo.gl/gAQTx4 Includes, - Illustrates the process using cardiotocographic data - Decision tree and interpretation with party package - Decision tree and interpretation with rpart package - Plot with rpart.plot - Prediction for validation dataset based on model build using training dataset - Calculation of misclassification error Decision trees are an important tool for developing classification or predictive analytics models related to analyzing big data or data science. 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: 59193 Bharatendra Rai