Home
Search results “Error analysis in r”
Standard Error of the Estimate used in Regression Analysis (Mean Square Error)
 
03:41
An example of how to calculate the standard error of the estimate (Mean Square Error) used in simple linear regression analysis. This typically taught in statistics. Like us on: http://www.facebook.com/PartyMoreStud... Link to Playlist on Regression Analysis http://www.youtube.com/course?list=EC... Created by David Longstreet, Professor of the Universe, MyBookSucks http://www.linkedin.com/in/davidlongs...
Views: 275482 statisticsfun
Checking Linear Regression Assumptions in R | R Tutorial 5.2 | MarinStatsLectures
 
07:50
Checking Linear Regression Assumptions in R ; Dataset: https://bit.ly/2rOfgEJ; Linear Regression Concept and with R: https://bit.ly/2z8fXg1; More Statistics and R Programming Tutorials: https://goo.gl/4vDQzT; How to test linear regression assumptions in R? In this R tutorial, we will first go over some of the concepts for linear regression like how to add a regression line, how to interpret the regression line (predicted or fitted Y value, the mean of Y given X), how to interpret the residuals or errors (the difference between observed Y value and the predicted or fitted Y value) and the assumptions when fitting a linear regression model. Then we will discuss the regression diagnostic plots in R, the reason for making diagnostic plots, and how to produce these plots in R; You will learn to check the linearity assumption and constant variance (homoscedasticity) for a regression model with residual plots in R and test the assumption of normality in R with QQ (Quantile Quantile) plots. You will also learn to check the constant variance assumption for data with non-constant variance in R, produce and interpret residual plots, QQ plots, and scatterplots for data with non-constant variance, and produce and interpret residual plots, QQ plots, and scatterplots for data with non-linear relationship in R. ►► Download the dataset here: https://statslectures.com/r-scripts-datasets ►► Watch More: ►Linear Regression Concept and Linear Regression with R Series: https://bit.ly/2z8fXg1 ►Simple Linear Regression Concept https://youtu.be/vblX9JVpHE8 ►Nonlinearity in Linear Regression https://youtu.be/tOzwEv0PoZk ► R Squared of Coefficient of Determination https://youtu.be/GI8ohuIGjJA ► Linear Regression in R Complete Series https://bit.ly/1iytAtm ■ Table of Content: 0:00:29 Introducing the data used in this video 0:00:49 How to fit a Linear Regression Model in R? 0:01:03 how to produce the summary of the linear regression model in R? 0:01:15 How to add a regression line to the plot in R? 0:01:24 How to interpret the regression line? 0:01:43 How to interpret the residuals or errors? 0:01:53 where to find the Residual Standard Error (Standard Deviation of Residuals) in R 0:02:14 What are the assumptions when fitting a linear regression model and how to check these assumptions 0:03:01 What are the built-in regression diagnostic plots in R and how to produce them 0:03:24 How to use Residual Plot for testing linear regression assumptions in R 0:03:50 How to use QQ-Plot in R to test linear regression assumptions 0:04:33 How to produce multiple plots on one screen in R 0:05:00 How to check constant variance assumption for data with non-constant variance in R 0:05:12 How to produce and interpret a Scatterplot and regression line for data with non-constant variance 0:05:40 How to produce and interpret the Residual plot for data with non-constant variance in R 0:06:02 How to produce and interpret the QQ plot for data with non-constant variance in R 0:06:12 How to produce and interpret a Scatterplot with regression line for data with non-linear relationship in R 0:06:40 How to produce and interpret the Residual plot for a data with non-linear relationship in R 0:06:52 How to produce and interpret the QQ plot for a data with non-linear relationship in R 0:07:02 what is the reason for making diagnostic plots 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 #RTutorials are created by #marinstatslectures to support a course at The University of British Columbia (#UBC) although we make all videos available to the everyone everywhere for free. Thanks for watching! Have fun and remember that statistics is almost as beautiful as a unicorn!
Transforming Data - Data Analysis with R
 
03:01
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: 58219 Udacity
StatQuest: PCA in R
 
08:57
We've talked about the theory behind PCA in https://youtu.be/FgakZw6K1QQ Now we talk about how to do it in practice using R. If you want to copy and paste the code I use in this video, it's right here: https://statquest.org/2017/11/27/statquest-pca-in-r-clearly-explained/ 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/
Support Vector Machine (SVM) with R - Classification and Prediction Example
 
16:57
Includes an example with, - brief definition of what is svm? - svm classification model - svm classification plot - interpretation - tuning or hyperparameter optimization - best model selection - confusion matrix - misclassification rate Machine Learning videos: https://goo.gl/WHHqWP svm is an important machine learning tool 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: 41581 Bharatendra Rai
Principal Component Analysis in R: Example with Predictive Model & Biplot Interpretation
 
23:44
Provides steps for carrying out principal component analysis in r and use of principal components for developing a predictive model. Link to code file: https://goo.gl/SfdXYz Includes, - Data partitioning - Scatter Plot & Correlations - Principal Component Analysis - Orthogonality of PCs - Bi-Plot interpretation - Prediction with Principal Components - Multinomial Logistic regression with First Two PCs - Confusion Matrix & Misclassification Error - training & testing data - Advantages and disadvantages principal component analysis is an important statistical tool 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: 34053 Bharatendra Rai
Calculating Mean, Standard Deviation, Frequencies in R | R Tutorial 2.8| MarinStatsLectures
 
06:50
Calculating Mean, Standard Deviation, Frequencies in R (Descriptive Statistics in R): How to produce numeric summaries for both categorical and numerical variables in R. Here is the Free Practice Dataset (LungCapData): (https://bit.ly/2rOfgEJ); Standard Deviation Explained (https://youtu.be/nlm9gfso4mw) ; For more Statistics and R Programming Tutorials: (https://goo.gl/4vDQzT) ►► Like to support us? You can Donate (https://bit.ly/2CWxnP2), Share our Videos, Leave us a Comment and Give us a Thumbs up! Either way We Thank You! In this R video tutorial, we will learn how to produce numeric summaries for both categorical and numerical variables in R. This tutorial explains: • How to create frequency tables in R • How to create contingency tables in R • How to calculate mean, median, variance, and standard deviation in R • How to calculate Pearson’s correlation in R • How to calculate Spearman’s correlation with R • How to calculate the minimum, maximum and range in R, • How to calculate specific quantiles or percentiles in R • How to calculate the covariance in R Table of Content: 0:00:36 how to access the Help menu in R for any of the commands 0:00:52 how to summarize a categorical variable 0:00:58 how to produce a "frequency table" in R to summarize a categorical variable using "table" command 0:01:10 how to express the "frequency table" in R using proportion 0:01:18 how to ask R for the number of observations using the "length" command 0:01:51 how to produce a "two-way table" or "contingency table" in R to summarize a categorical variable using "table" command 0:02:09 how to calculate the mean and trimmed mean in R to summarize a numeric variable using "mean" command and "trim" argument 0:02:37 how to calculate the "median" in R to summarize a numeric variable using the "median" command 0:02:45 how to calculate the variance in R to summarize a numeric variable using "var" command 0:02:54 how to calculate the "standard deviation" in R to summarize a numeric variable using the "sd" command or "sqrt" command (taking square root of variance) 0:03:23 how to calculate the minimum, maximum and range in R to summarize a numeric variable using "min", "max" and "range" command 0:03:45 how to calculate specific quantiles or percentiles in R using the "quantile" command and "probs" argument 0:04:53 how to calculate "Pearson's correlation" in R to summarize a numerical variable using the "cor" command 0:05:10 how to calculate "Spearman's correlation" in R to summarize a numerical variable using the "cor" command and "method" argument 0:05:22 how to calculate the covariance in R using the "cov" or "var" command 0:05:43 how to summarize all data (both numeric and categorical) in R using the "summary" command These video tutorials are useful for anyone interested in learning data science and statistics with R programming language using RStudio. ► ► Watch More: ► Intro to Statistics Course: https://bit.ly/2SQOxDH ►Data Science with R https://bit.ly/1A1Pixc ►Getting Started with R (Series 1): https://bit.ly/2PkTneg ►Graphs and Descriptive Statistics in R (Series 2): https://bit.ly/2PkTneg ►Probability distributions in R (Series 3): https://bit.ly/2AT3wpI ►Bivariate analysis in R (Series 4): https://bit.ly/2SXvcRi ►Linear Regression in R (Series 5): https://bit.ly/1iytAtm ►ANOVA Concept and with R https://bit.ly/2zBwjgL ►Hypothesis Testing: https://bit.ly/2Ff3J9e ►Linear Regression Concept and with R Lectures https://bit.ly/2z8fXg1 Follow MarinStatsLectures Subscribe: https://goo.gl/4vDQzT website: https://statslectures.com Facebook:https://goo.gl/qYQavS Twitter:https://goo.gl/393AQG Instagram: https://goo.gl/fdPiDn Our Team: Content Creator: Mike Marin (B.Sc., MSc.) Senior Instructor at UBC. Producer and Creative Manager: Ladan Hamadani (B.Sc., BA., MPH) These videos are created by #marinstatslectures to support some courses at The University of British Columbia (UBC) (#IntroductoryStatistics and #RVideoTutorials for Health Science Research), although we make all videos available to the everyone everywhere for free. Thanks for watching! Have fun and remember that statistics is almost as beautiful as a unicorn!
Data Science & Machine Learning - RMSE & R-Squared - DIY- 13 -of-50
 
07:14
Data Science & Machine Learning - RMSE & R-Squared - DIY- 13 -of-50 Do it yourself Tutorial by Bharati DW Consultancy cell: +1-562-646-6746 (Cell & Whatsapp) email: [email protected]n [email protected] website: http://bharaticonsultancy.in/ Google Drive- https://drive.google.com/open?id=0ByQlW_DfZdxHeVBtTXllR0ZNcEU RMSE / RMSD The root-mean-square deviation (RMSD) or root-mean-square error (RMSE) is a very commonly used measure of the differences between predicted values by a model and the actual values seen in the data. The square root of the mean/average of the square of all of the error. It compares the forecasting errors of various models for a target variable. RMSE (angle brace)- sqrt(mean((predicted – actual) ^2)); R-Squared R-squared is a statistical measure of how close the data are to the fitted regression line. It is also known as the coefficient of determination, or the coefficient of multiple determination for multiple predicted values. R-squared is conveniently scaled between 0 and 1, whereas RMSE is not scaled to any particular values. You would want R-Squared closer to 1. Hands On – R Machine Learning Ex-10 Download the data Customer_Age_Income.csv from the google drive link. Implement Simple & Multiple Linear Regression Model for target variable - Spend using predictor variables Age, Income, Job, Auto Loan Indicator, Gender, Marital Status. Note RMSE for each additional variable. Data Science & Machine Learning - Getting Started - DIY- 1 -of-50 Data Science & Machine Learning - R Data Structures - DIY- 2 -of-50 Data Science & Machine Learning - R Data Structures - Factors - DIY- 3 -of-50 Data Science & Machine Learning - R Data Structures - List & Matrices - DIY- 4 -of-50 Data Science & Machine Learning - R Data Structures - Data Frames - DIY- 5 -of-50 Data Science & Machine Learning - Frequently used R commands - DIY- 6 -of-50 Data Science & Machine Learning - Frequently used R commands contd - DIY- 7 -of-50 Data Science & Machine Learning - Installing RStudio- DIY- 8 -of-50 Data Science & Machine Learning - R Data Visualization Basics - DIY- 9 -of-50 Data Science & Machine Learning - Linear Regression Model - DIY- 10(a) -of-50 Data Science & Machine Learning - Linear Regression Model - DIY- 10(b) -of-50 Data Science & Machine Learning - Multiple Linear Regression Model - DIY- 11 -of-50 Data Science & Machine Learning - Evaluate Model Performance - DIY- 12 -of-50 Data Science & Machine Learning - RMSE & R-Squared - DIY- 13 -of-50 machine learning, data science, R programming, Deep Learning, Regression, Neural Network, R Data Structures, Data Frame, RMSE & R-Squared
Views: 7982 BharatiDWConsultancy
Panels and Clustering in R
 
14:03
This video runs through an example script on how to estimate panel data models in R using plm(). By appeal to lm() and lmer(), I show that plm() estimates what we think it should estimate. I also show how to cluster standard errors in R. Here are some resources to which I refer in the video: I posted these scripts on my econometrics website. metrics.tonycookson.com. They are called clusterFunctions.R and exampleofclusteringinR.R, and they are available for download there. Mahmoud Arai's Clustering Functions. http://people.su.se/~ma/clustering.pdf Mitchell Peterson's Test Data (kept referring to him as Thompson in the video, sorry!). http://www.kellogg.northwestern.edu/faculty/petersen/htm/papers/se/test_data.htm And some background reading on this. Peterson 2009 can be found here in earlier WP version. http://ideas.repec.org/p/nbr/nberwo/11280.html Thompson 2011 can be found here. http://schwert.ssb.rochester.edu/f532/JFE11_ST.pdf To address the question about ML/REML. Here is a useful set of slides on how to specify the likelihood. http://www.stat.wisc.edu/~ane/st572/notes/lec21.pdf As I remember it, REML focuses on estimating the error components independently of the systematic part of the regression model. On an intuitive level, this amounts to building in a degrees of freedom correction. There is much more out there on a Google search of "REML vs. ML in random effects"
Views: 9966 intromediateecon
R operations   Data Cleaning,Error Correction and Data Transformation on airquality dataset
 
16:27
THIS VIDEO SHOWS R OPERATIONS LIKE DATA CLEANING,ERROR CORECTION AND DATA TRANSFORMATION ON AIR QUALITY DATASET
Views: 12599 yogesh murumkar
Regression Analysis (Goodness Fit Tests, R Squared & Standard Error Of Residuals, Etc.)
 
23:59
Linear Regression Analysis, Goodness Of Fit Testing (R Squared & Standard Error of Residuals), how well Linear model fits the data, (X-Independent Variable) & (Y-Dependent Variable), calculating the standard error (standard deviation) of the regression residuals, how its done, detailed discussion by Allen Mursau
Views: 14396 Allen Mursau
Lecture57 (Data2Decision) Robust Regression in R
 
10:51
Robust estimation (location and scale) and robust regression in R. Course Website: http://www.lithoguru.com/scientist/statistics/course.html
Views: 5708 Chris Mack
R: Checking the normality (of residuals) assumption
 
02:26
related material at https://sites.google.com/site/buad2053droach/multiple-regression
Views: 8986 DWR447
R tutorial: Cross-validation
 
03:17
Learn more about machine learning with R: https://www.datacamp.com/courses/machine-learning-toolbox In the last video, we manually split our data into a single test set, and evaluated out-of-sample error once. However, this process is a little fragile: the presence or absence of a single outlier can vastly change our out-of-sample RMSE. A better approach than a simple train/test split is using multiple test sets and averaging out-of-sample error, which gives us a more precise estimate of true out-of-sample error. One of the most common approaches for multiple test sets is known as "cross-validation", in which we split our data into ten "folds" or train/test splits. We create these folds in such a way that each point in our dataset occurs in exactly one test set. This gives us 10 test sets, and better yet, means that every single point in our dataset occurs exactly once. In other words, we get a test set that is the same size as our training set, but is composed of out-of-sample predictions! We assign each row to its single test set randomly, to avoid any kind of systemic biases in our data. This is one of the best ways to estimate out-of-sample error for predictive models. One important note: after doing cross-validation, you throw all resampled models away and start over! Cross-validation is only used to estimate the out-of-sample error for your model. Once you know this, you re-fit your model on the full training dataset, so as to fully exploit the information in that dataset. This, by definition, makes cross-validation very expensive: it inherently takes 11 times as long as fitting a single model (10 cross-validation models plus the final model). The train function in caret does a different kind of re-sampling known as bootsrap validation, but is also capable of doing cross-validation, and the two methods in practice yield similar results. Lets fit a cross-validated model to the mtcars dataset. First, we set the random seed, since cross-validation randomly assigns rows to each fold and we want to be able to reproduce our model exactly. The train function has a formula interface, which is identical to the formula interface for the lm function in base R. However, it supports fitting hundreds of different models, which are easily specified with the "method" argument. In this case, we fit a linear regression model, but we could just as easily specify method = 'rf' and fit a random forest model, without changing any of our code. This is the second most useful feature of the caret package, behind cross-validation of models: it provides a common interface to hundreds of different predictive models. The trControl argument controls the parameters caret uses for cross-validation. In this course, we will mostly use 10-fold cross-validation, but this flexible function supports many other cross-validation schemes. Additionally, we provide the verboseIter = TRUE argument, which gives us a progress log as the model is being fit and lets us know if we have time to get coffee while the models run. Let's practice cross-validating some models.
Views: 48347 DataCamp
Introduction to Data Science with R - Data Analysis Part 1
 
01:21:50
Part 1 in a in-depth hands-on tutorial introducing the viewer to Data Science with R programming. The video provides end-to-end data science training, including data exploration, data wrangling, data analysis, data visualization, feature engineering, and machine learning. All source code from videos are available from GitHub. NOTE - The data for the competition has changed since this video series was started. You can find the applicable .CSVs in the GitHub repo. Blog: http://daveondata.com GitHub: https://github.com/EasyD/IntroToDataScience I do Data Science training as a Bootcamp: https://goo.gl/OhIHSc
Views: 1023501 David Langer
ROC Curve & Area Under Curve (AUC) with R - Application Example
 
19:40
Provides easy to apply example obtaining ROC curve and AUC using R. Data: https://goo.gl/VoHhyh Machine Learning videos: https://goo.gl/WHHqWP Includes an example with, - logistic regression model - confusion matrix - misclassification rate - rocr package - accuracy versus cutoff curve - identifying best cutoff values for best accuracy - roc curve - true positive rate (tpr) or sensitivity - false positive rate (fpr) or '1-specificity' - area under curve (auc) roc curve is an important model evaluation tool 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: 46978 Bharatendra Rai
Random Forest in R - Classification and Prediction Example with Definition & Steps
 
30:30
Provides steps for applying random forest to do classification and prediction. R code file: https://goo.gl/AP3LeZ Data: https://goo.gl/C9emgB Machine Learning videos: https://goo.gl/WHHqWP Includes, - random forest model - why and when it is used - benefits & steps - number of trees, ntree - number of variables tried at each step, mtry - data partitioning - prediction and confusion matrix - accuracy and sensitivity - randomForest & caret packages - bootstrap samples and out of bag (oob) error - oob error rate - tune random forest using mtry - no. of nodes for the trees in the forest - variable importance - mean decrease accuracy & gini - variables used - partial dependence plot - extract single tree from the forest - multi-dimensional scaling plot of proximity matrix - detailed example with cardiotocographic or ctg data random forest 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: 66994 Bharatendra Rai
Spatial Regession in R 1: The Four Simplest Models
 
40:37
We run OLS (with spatial diagnostics), SLX, Spatial Error and Spatial Lag Models. We also run the spatial Hausman test. Along the way, we discover a bug in the R SLX code in the spdep package, and get it fixed. Very exciting! Download the file with the data and commands here: http://spatial.burkeyacademy.com/home/files/R%20Spatial%20Regression1.zip Link to the entire Spatial Statistics Playlist: https://www.youtube.com/playlist?list=PLlnEW8MeJ4z6Du_cbY6o08KsU6hNDkt4k See more at http://spatial.burkeyacademy.com My Website: http://www.burkeyacademy.com/ Support me on Patreon! https://www.patreon.com/burkeyacademy Talk to me on my SubReddit: https://www.reddit.com/r/BurkeyAcademy/
Views: 12668 BurkeyAcademy
Linear Regression: SST, SSR, SSE, R-squared and Standard Error with Excel ANOVA
 
37:31
A tutorial on linear regression for data analysis with Excel ANOVA plus SST, SSR, SSE, R-squared, standard error, correlation, slope and intercept. The 8 most important statistics with Excel functions and the LINEST function with INDEX in a CFA exam prep in Quant 101. For the video transcript and cell formulas see: https://factorpad.com/fin/quant-101/linear-regression.html Zoom straight to the section you are interested in here: 01:44 - Step 1 - Background and Setup 05:34 - Step 2 - Scatter Plot and Data Analysis Tools 10:23 - Step 3 - The 8 Most Important Linear Regression Measures 31:34 - Step 4 - Linear Regression with Excel Functions 35:03 - Step 5 - Final Comments, Homework and Tips Find the outline for the Quant 101 series here: https://factorpad.com/fin/quant-101/quant-portfolio-management.html See what else you can learn at: https://factorpad.com Happy Learning!
Views: 1274 FactorPad
Standard Error of the Mean in R Studio
 
05:00
This video explains steps for generating the stanard error of the mean, by using the following "R" commands: SD, SQRT(), LENGTH(). Created by Nestor Matthews on December 13th, 2016. For best viewing results, in your YouTube screen click on the sprocket icon (lower right of YouTube window) and set the "Quality" option to the highest possible value (typically either 720 or 1080, "HD"). For best viewing results, in your YouTube screen click on the sprocket icon (lower right of YouTube window) and set the "Quality" option to the highest possible value (typically either 720 or 1080, "HD").
Views: 13064 Nestor Matthews
Simple Linear Regression in R | R Tutorial 5.1| MarinStatsLectures
 
05:38
Simple Linear Regression in R: How to Fit a Model; Linear Regression Concept and with R (https://bit.ly/2z8fXg1); Practice Dataset: (https://bit.ly/2rOfgEJ) More Statistics and R Programming Tutorials (https://goo.gl/4vDQzT) ▶︎▶︎Like to support us? You can Donate https://statslectures.com/support-us or Share our Videos with all your friends! How to fit a Linear Regression Model in R, Produce Summaries and ANOVA table for it. ◼︎ What to Expect in this R video Tutorial: ► learn when to use a regression model, and how to use the “lm” function in R to fit a linear regression model for your data ► learn to produce summaries for your regression model using “summary” function in R statistics software; these summaries can include intercept, test statistic, p value, and estimates of the slope for your linear regression model ► become familiar with the Residual Error: a measure of the variation of observations in regression line ► learn to ask R programming software for the attributes of the simple linear regression model using "attributes" function, extract certain attributes from the regression model using the dollar sign ($), add a regression line to a plot in R using "abline" function and change the color or width of the regression line. ► this R tutorial will also show you how to get the simple linear regression model's coefficient using the "coef" function or produce confidence intervals for the regression model using "confint" functions; moreover, you will learn to change the level of confidence using the "level" argument within the "confint" function. ►You will also learn to produce the ANOVA table for the linear regression model using the "anova" function, explore the relationship between ANOVA table and the f-test of the regression summary, and explore the relationship between the residual standard error of the linear regression summary and the square root of the mean squared error or mean squared residual from the ANOVA table. ► ►You can access and download the dataset here: https://statslectures.com/r-scripts-datasets ►► Watch More: ► Intro to Statistics Course: https://bit.ly/2SQOxDH ►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 ◼︎ Table of Content: 0:00:07 When to fit a simple linear regression model? 0:01:11 How to fit a linear regression model in R using the "lm" function 0:01:14 How to access the help menu in R for any function 0:01:36 How to let R know which variable is X and which one is Y when fitting a regression model 0:01:45 How to ask for the summary of the simple linear regression model in R including estimates for intercept, test statistic, p-values and estimates of the slope. 0:02:27 Residual standard error (residual error) in R 0:02:53 How to ask for the attributes of the simple linear regression model in R 0:03:06 How to extract certain attributes from the simple linear regression model in R 0:03:40 How to add a regression line to a plot in R 0:03:52 How to change the color or width of the regression line in R 0:04:07 How to get the simple linear regression model's coefficient in R 0:04:11 How to produce confidence intervals for model's coefficients in R 0:04:21 How to change the level of confidence for model's coefficients in R 0:04:38 How to produce the ANOVA table for the linear regression in R 0:04:47 Explore the relationship between ANOVA table and the f-test of the linear regression summary 0:04:55 Explore the relationship between the residual standard error of the linear regression summary and the square root of the mean squared error or mean squared residual from the ANOVA table 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!
FrSky R-XSR short description error analysis FC connection SBUS S.Port signal inversion (English)
 
04:16
Howto connect the R-XSR RX to a flight controller and fix issues. Short description on how to get uninverted SBUS and S.Port signals. Affi Link R-XSR https://goo.gl/7MwSvt Transcript This is the schematic of the R-XSR. Taking a closer look at the connector, starting from the bottom. The pins are: GND, +5V. In the middle is the S.Port pin for telemetry data. 4th pin is SBUS from the R-XSR to the flight controller. Top Pin is for the redundancy function of the R-XSR. The Slave RX SBUS Out is connected to this "SBUS_IN" pin. The signal coming from the R-XSR is an inverted signal. In case we need the uninverted SBUS and S.Port Signal, we cannot use the connector pins. We'll talk about that later what we can do, if the inverted signal does not work for you. After connecting the R-XSR with the FC according to the wiring diagram schematic, we connect the FC to the PC. We start the flight controller configurator software. In this case Betaflight is used. We now open the "Ports" tab. Here we configure the UART for steering of the craft vie the SBUS protocol and the S.Port for telemetry data. I have connected SBUS OUT from the RX to UART1. Therefore I activate the "Serial RX" switch, which is already preselected because that is the default setting. Everything else in this row stays disabled. I have connected the S.Port pin of the R-XSR to UART6. Choose S.Port from the dropdown menu. Do not use FrSky here, that is a different protocol. Klick the "Save and Reboot" button to make the changes permanent. We now switch to the configuration tab to check the RX Settings. In the Receiver Section we make sure, that "Serial Based Receiver" is chosen from the dropdown menu and the SBUS protocol is chosen. We make sure that in the "Other Features" selection the "TELEMETRY" switch is activated. Otherwise the TX will not receive the telemetry data from the FC. Without that switch on, the TX will still be able to receive the basic telemetry data from the R-XSR. Such as RSSI and RX Voltage. Press "Save and Reboot" to make the changes permanent. To verify that everything is working, we change to the "Receiver" tab and move the levers on the remote control. The bars in this tab should move accordingly. Of course, the TX and RX have to be bound and the TX has to be configured for this to work. We can switch the channel map here. We can also check on the TX if the telemetry data is sent. If everything works, we are fine. If nothing happens, it could mean that the remote control is not configured. Option 1 is to check if the remote control is properly configured and bound to the RX. Or the inverted signal does not work for you and we need the uninverted signal. 2nd option would be to try to invert/uninvert the signal via the software. In the CLI type "get inv" to see all options for inversion. We see SBUS inversion and telemetry inverted. We can switch that from the current setting to ON or OFF. As a remark, I have never tried that for myself successfully. It seems to depend on the used FC. However, i show you how this is done. Type "set sbus_inversion OFF" followed by Save. After the reboot, the settings are permanent. We can try that also for telemetry. It's worth a try. If option 1 and 2 did fix the issue, we still have a 3r option: we can use the uninverted signals. Thankfully the R-XSR has dedicated soldering pads for the uninverted SBUS Signal, labeled B, and S.Port signal labeled P. To use these pads to get the uninverted signals the pads are pretinned. The wires are stripped off the silicone isolation just a tiny bit and pretinned as well. With the pretinning, we can just hold the wire to the pad and heat it up to make a good connection, no additional tin is required. To make it even more robust, put some hot glue on the connections and cables and put heat shrink on the R-XSR. That should to the job. I hope that works for you. If you have any questions feel free to leave a comment. Drohnen360: https://goo.gl/m776bm Instagram: https://goo.gl/yaJC57 Twitter: https://goo.gl/xFTsv4 Facebook: https://goo.gl/DXYtx9
Views: 15405 N4V1G4T0R
R Tutorial: Path Analysis and Mediation using Lavaan
 
09:50
All of my videos use "annotations." Make sure that you have annotations turned on or you might miss important information, such as error correction! You can make sure annotations are on by clicking on the gear-shaped symbol near the bottom-right corner of the player. I will never use annotations to for advertising or self-promotion. Link to R Statistical Software homepage: http://www.r-project.org/ Link to RStudio homepage: http://www.rstudio.com/ Link to Lavaan homepage: http://lavaan.ugent.be/ Link to Dr. Yves Rosseel's Lavaan Tutorials: http://lavaan.ugent.be/tutorial/index.html DESCRIPTION This video will walk you through path analysis using the "Lavaan" package in R. I cover the basic steps to estimate model parameters as well as the additional steps needed to estimate indirect effects. NOTE ABOUT SIGNIFICANCE TESTS By default, Lavaan provides significance tests for most effects based on the assumption that the sampling distributions of those effects are normally distributed. There are many cases in which this assumption is not supported (e.g., indirect effects) and you might wish to use an alternative method for significance testing, such as bootstrapped confidence intervals. Lavaan is capable of providing results from these alternative procedures, but a discussion of this topic goes beyond the scope of this video. Although I plan to make separate videos to discuss these methods, I am available to help if you have a specific need. All I ask is that you use the comment section below so that others might benefit from your question. For more info about significance tests for indirect effects in path analysis: Hayes, A. F., & Scharkow, M. (2013). The relative trustworthiness of inferential tests of the indirect effect in statistical mediation analysis does method really matter?. Psychological Science, 0956797613480187. ABOUT MY LAVAAN TUTORIALS These tutorials are based heavily on the work conducted by the developers of Lavaan. The main developer, Yves Rosseel at Ghent University, has put together an excellent set of tutorials (available in PDF format) that go into the topics discussed on this channel in much more detail. I highly recommend this text for educational purposes and as a reference while you conduct your analyses (see link above). -------------------------------------- Let me know how I can improve future videos by leaving a comment!
Views: 25686 Jordan Clark
How to Calculate R Squared Using Regression Analysis
 
07:40
An example on how to calculate R squared typically used in linear regression analysis and least square method. Like us on: http://www.facebook.com/PartyMoreStudyLess Link to Playlist on Linear Regression: http://www.youtube.com/course?list=ECF596A4043DBEAE9C Link to Playlist on SPSS Multiple Linear Regression: http://www.youtube.com/playlist?list=PLWtoq-EhUJe2Z8wz0jnmrbc6S3IwoUPgL Created by David Longstreet, Professor of the Universe, MyBookSucks http://www.linkedin.com/in/davidlongstreet
Views: 387189 statisticsfun
Linear Discriminant Analysis in R | Example with Classification Model & Bi-Plot interpretation
 
20:26
Provides steps for carrying out linear discriminant analysis in r and it's use for developing a classification model. Includes, - Data partitioning - Scatter Plot & Correlations - Linear Discriminant Analysis - Stacked Histograms of Discriminant Function Values - Bi-Plot interpretation - Partition plots - Confusion Matrix & Accuracy - training & testing data - Advantages and disadvantages linear discriminant analysis is an important statistical tool 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: 15616 Bharatendra Rai
Correlations and Covariance in R with Example  | R Tutorial 4.12 | MarinStatsLectures
 
06:36
Correlations and Covariance in R with Example: Learn how to calculate Pearson's correlation, Spearman's rank correlation, Kendall's rank correlation, and covariance in R (with example). 📝 Find R practice dataset (LungCapData) here: (https://statslectures.com/r-scripts-datasets ) 👍🏼Best Statistics & R Programming Language Tutorials: ( https://goo.gl/4vDQzT ) ►► Want to support us? You can Donate (https://bit.ly/2CWxnP2), Share Our Videos, Leave Comments or Give us a Like! ! In this R video we will learn how to calculate Pearson's correlation, Spearman's rank correlation, and Kendall's rank correlation, create confidence intervals and hypothesis tests and calculate Covariance in R. Here we will be using "cor", "cor.test","cov" ,"pairs", method”, functions and arguments in R and more. Pearson’s correlation is a parametric measure of the linear association between two numeric variables, Spearman’s rank correlation is a non-parametric measure of the monotonic association between two numeric variables, Kendall’s rank correlation is another non-parametric measure of the association based on concordance or discordance of x-y pairs. Covariance is a measure of the joint variability of two random variables. Table of Content: 0:00:08 When should we use Pearson's correlation in statistics and in research? 0:00:16 When should we use Spearman's rank correlation in statistics and in research? 0:00:24 When should we use kendall's rank correlation in statistics and in research? 0:00:54 how to access the help menu in R statistical software for correlation functions 0:01:05 how to produce a scatterplot in the R programming language to explore the relationship between variables using "plot" function 0:01:39 how to calculate the correlation between variables using the "cor" function in R 0:01:46 how to calculate Pearson's correlation using "method" argument in R 0:02:17 how to calculate Spearman's rank correlation using "method" argument in R 0:02:24 how to calculate kendall's rank correlation in R using "method" argument 0:02:34 how to produce a confidence interval and test the hypothesis for the correlation in R using the "cor.test" function 0:03:21 how to calculate the p-value when there are exact values in dataset using "exact" argument in R 0:03:42 how to change the alternative hypothesis using the "alt" argument in R 0:04:03 how to change the confidence level using the "conf.level" function in R 0:04:13 how to calculate the covariance in R using the "cov" function 0:04:27 how to produce all possible pair-wise plots using the "pairs" function in R 0:04:50 how to produce a "pairs" plot only for some of the variables in the dataset by sub-setting data using square brackets in R 0:05:26 how to produce a correlation matrix in R using the "cor" function and "method" argument 0:05:37 how to deal with categorical variables in the dataset when creating a correlation matrix by subsetting data using square brackets in R 0:06:18 how to produce the covariance matrix using the "cov" function in R These video tutorials are useful for anyone interested in learning data science and statistics with R programming language using RStudio. ► ► Watch More: ► Intro to Statistics Course: https://bit.ly/2SQOxDH ►Data Science with R Course https://bit.ly/1A1Pixc ►Getting Started with R using RStudio (Series 1): https://bit.ly/2PkTneg ►Graphs and Descriptive Statistics in R using RStudio (Series 2): https://bit.ly/2PkTneg ►Probability distributions in R using RStudio (Series 3): https://bit.ly/2AT3wpI ►Bivariate analysis in R using RStudio (Series 4): https://bit.ly/2SXvcRi ►Linear Regression in R using RStudio (Series 5): https://bit.ly/1iytAtm ►ANOVA Concept and with R using RStudio https://bit.ly/2zBwjgL ►Hypothesis Testing: https://bit.ly/2Ff3J9e ►Linear Regression Concept and with R Lectures https://bit.ly/2z8fXg1 Follow MarinStatsLectures Subscribe: https://goo.gl/4vDQzT website: https://statslectures.com Facebook: https://goo.gl/qYQavS Twitter: https://goo.gl/393AQG Instagram: https://goo.gl/fdPiDn Our Team: Content Creator: Mike Marin (B.Sc., MSc.) Senior Instructor at UBC. Producer and Creative Manager: Ladan Hamadani (B.Sc., BA., MPH) These videos are created by #marinstatslectures to support some Statistics and R Programming 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! #rprogramming #statistics #Rstats
ANOVA, ANOVA Multiple Comparisons & Kruskal Wallis in R | R Tutorial 4.9 | MarinStatsLectures|
 
04:37
Analysis of Variance (ANOVA), Multiple Comparisons & Kruskal Wallis in R with Examples: Learn how to Conduct ANOVA in R, ANOVA Pairwise Comparisons in R, and Kruskal Wallis One-Way ANOVA in R with Examples! 👉🏼Related: ANOVA in Statistics & ANOVA in R Lecture Series: https://bit.ly/2Jb3uPr 📝 Find R practice dataset here: (https://statslectures.com/r-scripts-datasets ) 👍🏼Best Statistics & R Programming Language Tutorials: ( https://goo.gl/4vDQzT ) ►► Want to support us? You can Donate (https://bit.ly/2CWxnP2), Share Our Videos, Leave Comments or Give us a Like! In this Tutorial, you will learn to use various functions in R to: Conduct one-way analysis of variance (ANOVA) test in R, View ANOVA table in R, produce a visual display for the pair-wise comparisons of the analysis of variance in R, conduct multiple comparisons/ANOVA pair-wise comparisons in R, produce Kruskal-Wallis one-way analysis of variance using ranks with R Statistical Software. ■Table of Content 0:00:12 when should we use one-way analysis of variance (ANOVA) in statistics and in research 0:00:37 how to conduct ANOVA in R software using the "aov" command/function 0:00:42 how to access the help menu in R for ANOVA commands 0:00:52 how to create a boxplot in R statistical software 0:01:42 how to view ANOVA table in R using "summary" function 0:02:07 how to ask R for what is stored in an object using the "attributes" function. 0:02:23 how to extract certain attributes from an object in R using the dollar sign ($) 0:02:48 how to conduct multiple comparisons/pair-wise comparisons for the analysis of variance in R using the "TukeyHSD" command 0:03:17 how to produce a visual display for the pair-wise comparisons of the analysis of variance in R programming language using "plot" function 0:03:50 how to produce Kruskal-Wallis one-way analysis of variance using ranks in R using the "kruskal.test" function 0:03:56 when is it appropriate to use Kruskal-Wallis one-way analysis of variance for data in statistics and in research This video is a tutorial for programming in R Statistical Software for beginners, using RStudio. ▶︎▶︎ Watch More ▶︎ANOVA Use and Assumptions https://youtu.be/_VFLX7xJuqk ▶︎ Understanding the Sum of Squares in ANOVA, the concept of analysis of variance, and ANOVA hypothesis testing https://youtu.be/-AeU4y2vkIs ▶︎ ANOVA F Statistic and P-Value: https://youtu.be/k-xZzEYL8oc ▶︎ ANOVA & Bonferroni Multiple Comparisons Correction https://youtu.be/pscJPuCwUG0 ▶︎ Two Sample t-test in Statistics https://youtu.be/mBiVCrW2vSU ▶︎ Paired t-test in Statistics https://youtu.be/Q0V7WpzICI8 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 Statistics and R Programming 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! #rprogramming #statistics #Rstats
Calculating Power in R
 
05:48
This video tutorial shows you how to calculate the power of a one-sample and two-sample tests on means. The code will soon be on my blog page. Here is the link to the page with the syntax. http://threestandarddeviationsaway.blogspot.com/p/calculating-power-in-r.html
Views: 17884 Ed Boone
Bootstrap Sample Technique in R software
 
08:40
Bootstrap is very simple technique used for small samples. Major takeaways from video are: What?: Resampling with replacement from sample data. Why?: To find std errors without invoking CLT. How?: K times repetitive sampling of size n (observation). When to use?: Most suitable for small sample sizes. Major Advantage: No need to invoke CLT or normality assumption
Views: 19160 Sarveshwar Inani
DSO 530: Logistic Regression in R
 
14:50
In this video: (1) Slicing our data into testing and training data sets, (2) fit logistic regression model using the training data set, (3) predict a categorical vairable from the fitted model using an "unseen" testing data, and (4) create the confusion matrix to compute the miss-classification error rate
Views: 50611 Abbass Al Sharif
Multiple Linear Regression in R | R Tutorial 5.3 | MarinStatsLectures
 
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!
Linear Regression Using R
 
05:26
How to calculate Linear Regression using R. http://www.MyBookSucks.Com/R/Linear_Regression.R http://www.MyBookSucks.Com/R Playlist http://www.youtube.com/playlist?list=PLF596A4043DBEAE9C
Views: 22799 statisticsfun
How to Perform K-Means Clustering in R Statistical Computing
 
10:03
In this video I go over how to perform k-means clustering using r statistical computing. Clustering analysis is performed and the results are interpreted. http://www.influxity.com
Views: 206745 Influxity
R12. Robust/White Standard Errors. (Econometrics in R)
 
06:07
In this video, I demonstrate how to get R to produce robust standard errors without having to create the robust variance-covariance matrix yourself every time you do it (using either hccm() in car or vcovHC in sandwich()). The key is to use a command that extends summary.lm(), which I have renamed summaryR(). I also demonstrate how to conveniently use the robust variance-covariance matrix when conducting a linear hypothesis test, merely by using the white.adjust option to linearHypothesis. These two commands are quite useful if you want to use robust standard errors. Some information on this video (including code that will allow you to install the summaryR() command) is available at my econometrics blog: http://novicemetrics.blogspot.com/2011/04/video-tutorial-on-robust-standard.html
Views: 34316 intromediateecon
Fundamentals of Quantitative Modeling - R squared and Root Mean Squared Error RMSE
 
12:59
Fundamentals of Quantitative Modeling - Module 4: Regression Models To get certificate subscribe at: https://www.coursera.org/learn/wharton-quantitative-modeling/home/welcome ============================ Fundamentals of Quantitative Modeling: https://www.youtube.com/playlist?list=PL2jykFOD1AWYksOj1iJ3o69RJLXTREtwl ============================ Youtube channel: https://www.youtube.com/user/intrigano ============================ https://scsa.ge/en/online-courses/ https://www.facebook.com/cyberassociation/ Fundamentals of Quantitative Modeling About this course: How can you put data to work for you? Specifically, how can numbers in a spreadsheet tell us about present and past business activities, and how can we use them to forecast the future? The answer is in building quantitative models, and this course is designed to help you understand the fundamentals of this critical, foundational, business skill. Through a series of short lectures and demonstrations, you’ll learn the key ideas and process of quantitative modeling so that you can begin to create your own models for your own business or enterprise. By the end of this course, you will have seen a variety of practical commonly used quantitative models as well as the building blocks that will allow you to start structuring your own models. Module 4: Regression Models This module explores regression models, which allow you to start with data and discover an underlying process. Regression models are the key tools in predictive analytics, and are also used when you have to incorporate uncertainty explicitly in the underlying data. You’ll learn more about what regression models are, what they can and cannot do, and the questions regression models can answer. You’ll examine correlation and linear association, methodology to fit the best line to the data, interpretation of regression coefficients, multiple regression, and logistic regression. You’ll also see how logistic regression will allow you to estimate probabilities of success. By the end of this module, you’ll be able to identify regression models and their key components, understand when they are used, and be able to interpret them.
Views: 213 intrigano
How to calculate a regression equation, R Square, Using Excel Statistics
 
06:52
Tutorial shows how to calculate a linear regression line using excel. Like MyBooKSucks on: http://www.facebook.com/PartyMoreStudyLess Playlist on Regression http://www.youtube.com/playlist?list=PLF596A4043DBEAE9C Created by David Longstreet, Professor of the Universe, MyBookSucks http://www.linkedin.com/in/davidlongstreet
Views: 149241 statisticsfun
Social Media Analytics - Twitter Analysis in R (Example @realDonaldTrump)
 
16:06
Case Study: Donald Trump Twitter (@realDonaldTrump) Analysis Click here to see how to link to Twitter database: https://www.youtube.com/watch?v=ebutXE4MJ3Y (UPDATED) Twitter Analytics in R codes Powerpoint can be downloaded at https://drive.google.com/open?id=0Bz9Gf6y-6XtTNDE5a2V0dXBjWVU How to process tweets with emojis in R? What if there is a gsub utf-8 invalid error? (Example Solution) 1. Use gsub to replace the emojis (utf-8 coding) codes. 2. See slide 7 in the Powerpoint file above.
Views: 6968 The Data Science Show
R - Graphs - Bar Charts with Error Bars in Ggplot2
 
27:28
Recorded: Fall 2015 Lecturer: Dr. Erin M. Buchanan This video covers the basic ideas of functions using R - topics include: - ggplot2 - bar graphs with one independent variable - bar graphs with two independent variables - error bars - stat summary - changing the legends, axes labels, and group labels Lecture materials and assignment available at statstools.com http://statstools.com/learn/graduate-statistics/ Used in the following courses: Graduate Statistics
Views: 15734 Statistics of DOOM
R Tutorial: Confirmatory Factor Analysis using Lavaan
 
07:32
All of my videos use "annotations." Make sure that you have annotations turned on or you might miss important information, such as error correction! You can make sure annotations are on by clicking on the gear-shaped symbol near the bottom-right corner of the player. I will never use annotations to for advertising or self-promotion. Link to R Statistical Software homepage: http://www.r-project.org/ Link to RStudio homepage: http://www.rstudio.com/ Link to Lavaan homepage: http://lavaan.ugent.be/ Link to Dr. Yves Rosseel's Lavaan Tutorials: http://lavaan.ugent.be/tutorial/index.html DESCRIPTION This video will walk you through confirmatory factor analysis using the "Lavaan" package in R. I cover the basic steps to estimate model parameters for latent variables as well as some changes that can be made to the Lavaan defaults. ABOUT MY LAVAAN TUTORIALS These tutorials are based heavily on the work conducted by the developers of Lavaan. The main developer, Yves Rosseel at Ghent University, has put together an excellent set of tutorials (available in PDF format) that go into the topics discussed on this channel in much more detail. I highly recommend this text for educational purposes and as a reference while you conduct your analyses (see link above). -------------------------------------- Let me know how I can improve future videos by leaving a comment!
Views: 17853 Jordan Clark
Goalkeeper Training including Error Analysis [TRAILER]
 
01:38
GERMAN SOCCER TRAINING For more videos, tips and background info go to http://www.1x1sport.com/goalkeeper-training-including-error-analysis/ „Goalkeeper Training including Error Analysis“ is the perfect guide for soccer coaches and players alike, because the effective drills featured in the video will help you to train and optimize the all-important fundamentals of goalkeeping. This video can also serve as an inspirational tool to create professional training programs that will help you to make your goalies better – regardless whether they’re a young and inexperienced amateur or a seasoned pro. With this video you’ll learn how to spot technical errors and other potential problems. When you know what your players are doing wrong you can make conclusive suggestions for improvements and this way you’ll be able to raise the level of your goalkeepers’ abilities tremendously. 1x1SPORT The European Sports & Training Academy presents Goalkeeper Training including Error Analysis | Featuring fundamental drills to help you become a better goalie - Learn how to detect, prevent, and correct errors - Effective drills for coaches and goalkeepers - Suitable for all ages and skill sets - Modern training methods for modern goalkeepers - Developed by experts and pros - Optimize all aspects of goalkeeping
Boxplots and Grouped Boxplots in R | R Tutorial 2.2 | MarinStatsLectures
 
04:15
Boxplots and Grouped Boxplots in R: How to Create and Modify Boxplots and Group Boxplots (Side By Side Boxplots) with R; Practice with Free Dataset: (https://bit.ly/2rOfgEJ) Need More Statistics and R Programming Tutorials? (https://goo.gl/4vDQzT) ►► Like to support us? You can Donate (https://bit.ly/2CWxnP2), Share our Videos, Leave us a Comment and Give us a Thumbs up! Either way We Thank You! In this R video tutorial, we will learn how to produce box plots (a.k.a. box and whisker diagram) in R, as well as "side by side boxplots" for multiple groups (i.e. boxplots with groups) with examples. In this R tutorial we will also learn how to add titles, change axes labels, and many other modifications to the plot by using "boxplot", "~", "ylim", and "quantile" functions and "xlab", "ylab", "ylim", "las" arguments. ► ►You can access and download the "LungCapData” dataset here: https://statslectures.com/r-scripts-datasets ◼︎ Table of Content: ► 0:00:05 what is a box plot and when should we use it ► 0:00:25 how to produce a "box plot" in R? By using "box.plot" function ► 0:00:30 how to access the help menu in R ► 0:00:56 how to report the "minimum", "first quartile", "median", "third quartile", and "maximum" in a "box plot" in R? By using the "quantile" function ► ► Modifying Box Plots in R ► 0:01:31 how to add a title to a boxplot in R using the "main" argument ► 0:01:39 how to label the x-axis or the y-axis of a "box plot" using "xlab" or "ylab" arguments ► 0:01:52 how to change the limits for the y-axis of a "box plot" in R using the "ylim" argument ► 0:02:04 how to rotate the values on the y-axis of a "box plot" using "las" argument ► 0:02:13 how to produce side-by-side box plots in R using "boxplot" and "~" (separate) functions (for example: when comparing the distribution of a numeric variable for different groups that are formed by a categorical variable) ► 0:02:53 how to add a title to side-by-side boxplots in R using the "main" argument ► 0:03:04 how to produce side-by-side box plots in R using the "square brackets" to subset data ► 0:03:35 double equal sign (==) , what does this mean? These video tutorials are useful for anyone interested in learning data science and statistics with R programming language using RStudio. ►► Watch More: ► Intro to Statistics Course: https://bit.ly/2SQOxDH ►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 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!
Statistics with R (3) - Generalized, linear, and generalized least squares models (LM, GLM, GLS)
 
28:01
In this video, I show how how to implement linear models, generalized linear models and generalized least squares models in R. Using the "airquality" dataset, I show how to fit and interpret the models. The core of the session is the interpretation of partial slope coefficients in Poisson generalized linear models. Finally, I give an outlook on generalized additive models which will be covered in one of the next sessions. The R code used in this video is: airquality plot(Ozone~Wind,airquality) model1=lm(Ozone~Wind,airquality) plot(model1) coef(model1) (Intercept) Wind 96.872895 -5.550923 #predictions for Wind speeds of 19 and 20 mph: Ozone1=coef(model1)[1]+coef(model1)[2]*19 Ozone2=coef(model1)[1]+coef(model1)[2]*20 Ozone1 Ozone2 ## model2=glm(Ozone~Wind,airquality,family=poisson) coef(model2) (Intercept) Wind 5.0795877 -0.1488753 Ozone1.glm=exp(coef(model2)[1]+coef(model2)[2]*19) Ozone2.glm=exp(coef(model2)[1]+coef(model2)[2]*20) Ozone2.glm/Ozone1.glm 0.8616765 exp(coef(model2)[2]) #exp(-0.1488753 ) #0.8616765 ## library(nlme) model3=gls(Ozone~Wind,airquality) summary(airquality$Ozone) model3=gls(Ozone~Wind,airquality,na.action=na.exclude) head(airquality) airquality$Date= as.Date(paste(1973,airquality$Month,airquality$Day,sep="-")) library(lattice) xyplot(Ozone~Date,airquality) model4=gls(Ozone~Wind*Date,airquality,na.action=na.exclude) air2=subset(airquality,complete.cases(Ozone)) model5=gls(Ozone~Wind*Date,air2) plot(ACF(model5,form=~Date),alpha=0.05) model6=update(model5,correlation=corAR1()) library(MuMIn) AICc(model5,model6) df AICc model5 5 1099.40 model6 6 1095.21 summary(model6)
Views: 103675 Christoph Scherber
Standard error R
 
03:10
This video shows how to use R to calculate the standard error of the estimate in a regression analysis.
Views: 3571 Kathryn Kozak
R Tutorial 10: How to handle missing values and attributes
 
10:00
In this video, we learn how to handle missing values in R: how to find if there are any missing values and remove them. Also, I show how how to work with attributes that can be attached to any R object. About the series: Difficulty level: Beginner This is a brand new tutorial series to learn R Programming Language for Data science / Statistics. I walk you through a structured approach to learn the language so the concepts falls in place perfectly and you gain a clear understanding. This series is filled with end of the lesson exercises and practice exercises to get you hand-on and have fun learning R. http://rstatistics.net http://r-statistics.co
Views: 31419 LearnR
Analysis of Variance (ANOVA) in R
 
07:58
This is a quick tutorial on how to perform ANOVA in R. I misstated at the end the hypothesis we are testing the means, not variances of the variables. So for this example we reject the null and say there is not enough evidence to suggest different means between groups. The median line in the boxplot should be close to the mean (if your data is normal which is an assumption in ANOVA) ---------- Data Set ------------ http://college.cengage.com/mathematics/brase/understandable_statistics/7e/students/datasets/owan/frames/frame.html Thank you very much for your time! #R #ANOVA
Views: 22918 thatRnerd