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

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

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

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 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.
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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 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!

Views: 227431
MarinStatsLectures- R Programming & Statistics

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

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: 12544
StatQuest with Josh Starmer

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

Paper: Multivariate Analysis
Module name: Introduction toMultivariate Analysis
Content Writer: Souvik Bandyopadhyay

Views: 67072
Vidya-mitra

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

Views: 47322
StatQuest with Josh Starmer

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

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

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
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

Views: 7149
Camo Analytics

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

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

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 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 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

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

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

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

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

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

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
https://sites.google.com/site/econometricsacademy/econometrics-models/principal-component-analysis

Views: 106391
econometricsacademy

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 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

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

Views: 5832
Dragonfly Statistics

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
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

Topics include interpreting sensory data via PCA, rotation of scores, and preference mapping with PCR.

Views: 2865
Camo Analytics

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

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

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/

Views: 1304
Lasse Engbo Christiansen

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

( 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!

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

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

http://quantedu.com/wp-content/uploads/2014/04/Time%20Series/4_2%20Simulate_Multivariate

Views: 10819
Quant Education

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
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statisticsfun

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

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