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Standard Error of the Estimate used in Regression Analysis (Mean Square Error)
 
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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: 261262 statisticsfun
Panels and Clustering in R
 
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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: 9525 intromediateecon
R12. Robust/White Standard Errors. (Econometrics in R)
 
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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: 32796 intromediateecon
Barplots with SEM or SD error bars using the R software
 
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In this video you will learn how to use the R software to create barplots with error bars (standard error of the mean or standard deviation). Get the data from here: https://drive.google.com/open?id=1yOGik8M9AXDTUgkUOEpKQHJhWhUQCeUJ Get the R script from here: https://drive.google.com/open?id=1uZi_fEo2xrwRwtNnGUITttBwOQarfVKs -~-~~-~~~-~~-~- Please watch: "How to cut out the center of a circle in Inkscape" https://www.youtube.com/watch?v=ERzmSOHWaok -~-~~-~~~-~~-~-
Views: 10795 InSearch
Linear Discriminant Analysis in R
 
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This video tutorial shows you how to use the lad function in R to perform a Linear Discriminant Analysis. It also shows how to do predictive performance and cross validation of the Linear Discriminant Analysis. This is an intermediate video. You should feel comfortable reading data in, subsetting data, regression or anova in R.
Views: 49611 Ed Boone
Error Analysis 1 | Data Quality and Types of Errors
 
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Scientific measurements are characterized by inaccuracy and imprecision due to experimental errors. This video introduces error analysis by showing the normal distribution, defining accuracy and precision, and illustrating how systematic and random errors introduce inaccuracy and imprecision.
Views: 4023 Michael Evans
Regression Analysis (Goodness Fit Tests, R Squared & Standard Error Of Residuals, Etc.)
 
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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: 13563 Allen Mursau
Transforming Data - Data Analysis with R
 
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This video is part of an online course, Data Analysis with R. Check out the course here: https://www.udacity.com/course/ud651. This course was designed as part of a program to help you and others become a Data Analyst. You can check out the full details of the program here: https://www.udacity.com/course/nd002.
Views: 52698 Udacity
R 2.1 - Loading Data and Working With Data Frames
 
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Get a refresher on navigating directories on your computer in R, and learn to load a CSV (comma-separated values) data set in the form of a "data frame" using the read.csv() function, which is a special type of data matrix. This video also introduces factor variables and explores the data in a data frame using the dim(), head(), length(), names(), and subset() functions.
Views: 62029 Google Developers
Spatial Econometrics in R
 
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Spatial Error Models and Spatial Lag Models in R https://sites.google.com/site/econometricsacademy/econometrics-models/spatial-econometrics
Views: 13285 econometricsacademy
Regression 3: Sums of Squares and R-squared
 
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In this video, I give two formulas for r^2, and give one intuitive interpretation of the value of r^2.
Views: 41719 intromediateecon
Linear Regression: SST, SSR, SSE, R-squared and Standard Error with Excel ANOVA
 
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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: 779 FactorPad
R operations   Data Cleaning,Error Correction and Data Transformation on airquality dataset
 
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THIS VIDEO SHOWS R OPERATIONS LIKE DATA CLEANING,ERROR CORECTION AND DATA TRANSFORMATION ON AIR QUALITY DATASET
Views: 8392 yogesh murumkar
FrSky R-XSR short description error analysis FC connection SBUS S.Port signal inversion (English)
 
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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: 14082 N4V1G4T0R
Standard Error of the Mean in R Studio
 
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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: 11011 Nestor Matthews
How to Calculate R Squared Using Regression Analysis
 
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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: 366427 statisticsfun
Split plot analysis, LSD test and plotting bar graphs using R
 
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In this video, you will learn how to carry out analysis for split-plot design with least significant difference test and plotting bar graphs with standard error bars using R studio. package required: agricolae R is a free software and you can download it from the link given below https://www.r-project.org/ Download link for R studio https://www.rstudio.com/products/rstudio/download/ Visit blog for more details: https://agroninfotech.blogspot.com/2018/06/split-plot-analysis-using-r.html Description of video Example data split-plot Console commands for split-plot analysis of variance LSD Mean comparison tests Construction of bar graphs representing standard error Download data file: https://1drv.ms/x/s!As-fQhyw8QbtgQKjAbHNS-T2WnGP Get connnected with us on ____________________________________________________________ G+: https://plus.google.com/u/1/101269397606526645442 Facebook page: https://www.facebook.com/AgronInfoTech/?ref=bookmarks Twitter: https://twitter.com/AgronInfoTech Linked In: https://www.linkedin.com/in/agron-info-tech-7429a6156/ Instagram: https://www.instagram.com/agroninfo/ ___________________________________________________________ If you have any question please comment below. Thanks for watching this video.
Views: 2118 AGRON Info-Tech
Standard error R
 
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This video shows how to use R to calculate the standard error of the estimate in a regression analysis.
Views: 3297 Kathryn Kozak
Simple Linear Regression in R | R Tutorial 5.1| MarinStatsLectures
 
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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!
Introduction to Data Science with R - Data Analysis Part 1
 
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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: 971649 David Langer
R tutorial: Introducing out-of-sample error measures
 
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Learn more about machine learning with R: https://www.datacamp.com/courses/machine-learning-toolbox Hi! I'm Zach Deane Mayer, and I'm one of the co-authors of the caret package. I have a passion for data science, and spend most of my time working on and thinking about problems in machine learning. This course focuses on predictive, rather than explanatory modeling. We want models that do not overfit the training data and generalize well. In other words, our primary concern when modeling is "do the models perform well on new data?" The best way to answer this question is to test the models on new data. This simulates real world experience, in which you fit on one dataset, and then predict on new data, where you do not actually know the outcome. Simulating this experience with a train/test split helps you make an honest assessment of yourself as a modeler. This is one of the key insights of machine learning: error metrics should be computed on new data, because in-sample validation (or predicting on your training data) essentially guarantees overfitting. Out-of-sample validation helps you choose models that will continue to perform well in the future. This is the primary goal of the caret package in general and this course specifically: don’t overfit. Pick models that perform well on new data. Let's walk through a simple example of out-of-sample validation: We start with a linear regression model, fit on the first 20 rows of the mtcars dataset. Next, we make predictions with this model on a NEW dataset: the last 12 observations of the mtcars dataset. The 12 cars in this test set will not be used to determine the coefficents of the linear regression model, and are therefore a good test of how well we can predict on new data. In practice, rather than manually splitting the dataset, we'd actually use the createResamples or createFolds function in caret, but the manual split simplifies this example. Finally, we calculate root-mean-squared-error (or RMSE) on the test set by comparing the predictions from our model to the actual MPG values for the test set. RMSE is a measure of the model's average error. It has the same units as the test set, so this means our model is off by 5 to 6 miles per gallon, on average. Compared to in-sample RMSE from a model fit on the full dataset, our model is signifigantly worse. If we had used in-sample error, we would have fooled ourselves into thinking our model is much better than it actually is in reality. It's hard to make predictions on new data, as this example shows. Out-of-sample error helps account for this fact, so we can focus on models that predict things we don't already know. Let's practice this concept on some example data.
Views: 7442 DataCamp
Spatial Regession in R 1: The Four Simplest Models
 
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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: 10784 BurkeyAcademy
Exploratory Factor Analysis in R
 
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This video tutorial will show you how to conduct an Exploratory factor analysis in R. This is an intermediate level video. You should know how to read data into R, conduct and understand PCA before watching this video.
Views: 42871 Ed Boone
Introduction to Cluster Analysis with R - an Example
 
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Provides illustration of doing cluster analysis with R. R File: https://goo.gl/BTZ9j7 Machine Learning videos: https://goo.gl/WHHqWP Includes, - Illustrates the process using utilities data - data normalization - hierarchical clustering using dendrogram - use of complete and average linkage - calculation of euclidean distance - silhouette plot - scree plot - nonhierarchical k-means clustering Cluster analysis is an important tool related to analyzing big data or working in data science field. Deep Learning: https://goo.gl/5VtSuC Image Analysis & Classification: https://goo.gl/Md3fMi R is a free software environment for statistical computing and graphics, and is widely used by both academia and industry. R software works on both Windows and Mac-OS. It was ranked no. 1 in a KDnuggets poll on top languages for analytics, data mining, and data science. RStudio is a user friendly environment for R that has become popular.
Views: 105056 Bharatendra Rai
Principal Component Analysis in R: Example with Predictive Model & Biplot Interpretation
 
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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: 30883 Bharatendra Rai
Lecture57 (Data2Decision) Robust Regression in R
 
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Robust estimation (location and scale) and robust regression in R. Course Website: http://www.lithoguru.com/scientist/statistics/course.html
Views: 5247 Chris Mack
R: Checking the normality (of residuals) assumption
 
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related material at https://sites.google.com/site/buad2053droach/multiple-regression
Views: 7294 DWR447
Calculating Power in R
 
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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: 16923 Ed Boone
R Studio: Importing & Analyzing Data
 
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Tutorial on importing data into R Studio and methods of analyzing data.
Views: 180539 MrClean1796
MSA | Measurement System Analysis | Gage R & R | Total Variation
 
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Measurement System Analysis is the study of part to part variation and measurement variation due to operator and equipment. The components of MSA includes; Resolution or Discrimination, Measurement Error (Equipment and Operator), Bias, Gage R&R, Linearity, Stability, Calibration plot, and Attribute R&R. This video gives you the brief understanding about the MSA and its components. Download course material in PDF http://www.shakehandwithlife.in/categories/bookshelf/msa-complete-training-coursework-ebook Subscribe “Shakehand with Life” YouTube channel to watch upcoming videos:- https://www.youtube.com/shakehandwithlifebahadurgarh Visit “Shakehand with Life” website for learning resource centre:- http://shakehandwithlife.in/ Other Related Videos:- Lean Six Sigma Tools and Techniques https://www.youtube.com/playlist?list=PL2H1lLi34F7hxr51PMJqwuri8ySZ1_o5I Other Popular Videos:- 1. FMEA | How to use FMEA for risk management? https://www.youtube.com/watch?v=BZWuUn93Sq4 2. 7 qc tools | An introduction of all 7 fundamental QC Tools https://www.youtube.com/watch?v=VmMN583M8n0 3. 5S methodology for lean manufacturing https://www.youtube.com/watch?v=dLtMbS3ln5U 4. 7 qc tools in Hindi for easy and effective learning https://www.youtube.com/watch?v=MqnzFtljjyI 5. Calculate Cp and Cpk to measure the process capability for six sigma https://www.youtube.com/watch?v=DmrI4WaLULE 6. What is the ‘Deming’s 14 Points Philosophy’? https://www.youtube.com/watch?v=FFsvseOVwfs Join us on:- Linkedin: https://in.linkedin.com/in/shakehandwithlife Facebook: https://www.facebook.com/shakehandwithlife Google+: https://plus.google.com/u/0/106783930894279779221 SlideShare: http://www.slideshare.net/shakehandwithlife Blog: http://www.blog.shakehandwithlife.in/ WhatsApp Broadcast: +91-9468267324
Views: 37725 Shakehand with Life
Change Reference (Baseline) Category in Regression Model with R | R Tutorial 5.6 |
 
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Change Reference or Baseline Category for a Categorical Variable in Regression Model with R: Using the relevel command in R to change the reference/baseline category for a factor or categorical / qualitative variable in a linear regression model (reparameterize) Practice Dataset (LungCapData) here: https://statslectures.com; More Statistics & R Programming Videos: 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 a linear regression model the intercept or constant term refers to the estimated mean Y-value for the reference or baseline group, and the model coefficients or parameters refer to expected changes in the mean Y-value relative to the reference group. For example, if X is a categorical variable (or a factor) with levels A, B, C, then the intercept will refer to level A, and there will be a coefficient for level B (b_B) which estimates the change in the mean for level B relative to level A, and there will be a coefficient for level C (b_C) which estimates the change in the mean for level C relative to level A. By default, R chooses the category that comes first alphabetically or numerically (alphanumerically) as the reference category. In this video, we show how to use the relevel command (function) to change the reference category in R statistical software for a categorical variable (a factor). In this example, we may wish to make level B (or level C) the reference category. ■Table of Content: 0:00:13 what is the interpretation of the intercept or constant term? 0:00:21 what is the interpretation of the model coefficients or parameters? 0:00:46 using the relevel command to change the reference or baseline category of a categorical variable 0:00:51 how to access the help menu in R 0:01:01 how to fit a linear regression model in R relating one outcome variable to two explanatory variables 0:01:21 how to interpret the fitted regression model output and model coefficients or parameters in R 0:01:26 how to interpret the regression model intercept 0:01:39 how to interpret the model coefficient for a numeric variable in R 0:01:56.5 how to interpret the model coefficient for a categorical variable in R 0:02:25 how to change the reference or baseline group in R 0:02:31 how does R choose the reference or baseline category? 0:02:50 how to use the "relevel" command to change the reference or baseline category in R 0:03:15 fit a model where we have changed the reference category The video provides a tutorial for programming in R Statistical Software for beginners 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!
Checking Linear Regression Assumptions in R | R Tutorial 5.2 | MarinStatsLectures
 
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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!
Linear Regression Using R
 
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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: 19956 statisticsfun
R Spatial Data 1: Read in SHP File
 
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Here we use R and RStudio to read in a spatial data file (as a SHP file), read in a contiguity (GAL) file created in GeoDa, create the same queen contiguity matrix in R and check that the two are the same, and compute a Moran's I. Link to Data File: https://sites.google.com/a/burkeyacademy.com/spatial/home/files/R%20Spatial%201%20Data.zip Link to Playlist of all Spatial Videos: https://www.youtube.com/playlist?list=PLlnEW8MeJ4z6Du_cbY6o08KsU6hNDkt4k 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/ Link to download R: https://cloud.r-project.org/ Link to Download RStudio: https://www.rstudio.com/products/rstudio/download/ Here are the commands we used: library(spdep) library(rgdal) NCVACO = readOGR(dsn = ".", layer = "NCVACO") queen.nb=read.gal("queen.gal", region.id=NCVACO$FIPS) summary(queen.nb) queen.R.nb=poly2nb(NCVACO, row.names=NCVACO$FIPS) #Rook would be rook.R.nb=poly2nb(NCVACO,queen=FALSE) summary(queen.R.nb) isTRUE(all.equal(queen.nb,queen.R.nb,check.attributes=FALSE)) #moran(variable, listw, no. regions, sum of weights) moran(NCVACO$SALESPC,nb2listw(queen.nb), length(NCVACO$SALESPC), Szero(nb2listw(queen.nb))) moran.test(NCVACO$SALESPC,nb2listw(queen.nb))
Views: 7239 BurkeyAcademy
Regression I: What is regression? | SSE, SSR, SST | R-squared | Errors (ε vs. e)
 
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All videos here: http://www.zstatistics.com/ The first video in a series of 5 explaining the fundamentals of regression. Please note that in my videos I use the abbreviations: SSR = Sum of Squares due to the Regression SSE = Sum of Squares due to Error. Intro: 0:00 Y-hat line: 2:26 Sample error term, e: 3:47 SSR, SSE, SST: 8:40 R-squared intro: 9:43 Population error term, ε: 12:11 Second video here: http://www.youtube.com/watch?v=4otEcA3gjLk Ever wondered WHY you have to SQUARE the error terms?? Here we deal with the very basics: what is regression? How do we establish a relationship between two variables? Why must we SQUARE the error terms? What exactly is SSE, SSR and SST? What is the difference between a POPULATION regression function and a SAMPLE regression line? Why are there so many different types of error terms?? Enjoy.
Views: 626817 zedstatistics
Regression Analysis ( Model Testing For Muticollinearity, Correlation Matrix, R Square, Etc.)
 
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Multiple Regression Analysis, Multi-collinearity Model Testing, When Two or More Independent Variables Measure Same Thing, (Standard Errors are Large), Is Linear Regression Model Better When X's (Independent Variables) Are Combined Versus Used Separately ??, (1) Calculate Correlation Matrix (Multicollinearity), Need High Degree Correlation Between Y-Dependent & X's Independent Variables & (2) Need Low Degree Correlation Between X's Independent Variables, then Each X's Contribute To Regression Model, 𝐑^𝟐 always increases when Variables are added, but How does it affect 𝐑_𝐀𝐃𝐉 ??, Check If R Increases Or Decreases Upon Adding a Variable, detailed example by Allen Mursau
Views: 31899 Allen Mursau
Correlation and Covariance in R (R Tutorial 4.9)
 
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Learn how to calculate "Pearson's", "Spearman's rank" and "Kendall's rank" correlation and create "confidence intervals" and "hypothesis tests" using "cor" and "cor.test" command. Also learn how to calculate covariance using "cov" command, produce pairwise plots using "pairs" command and a correlation or covariance matrix using the "cor" and "cov" commands. This video provides a beginner introduction to programming in R Statistical Software. You can access and download the "LungCapData" dataset here: Excel format: https://bit.ly/LungCapDataxls Tab Delimited Text File: https://bit.ly/LungCapData Here is a quick overview of the topics addressed in this video: 0:00:08 what is "Pearson's correlation" 0:00:16 what is "Spearman's rank correlation" 0:00:24 what is "kendall's rank correlation" 0:00:54 how to access the help menu in R for correlation commands 0:01:05 how to produce a scatterplot in R to explore the relationship between variables using "plot" command 0:01:39 how to calculate the correlation between variables using the "cor" command 0:01:46 how to calculate "pearson's correlation" in R using "method" command 0:02:17 how to calculate "Spearman's rank correlation" in R using "method" argument 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 using the "cor.test" command 0:03:21 how to calculate the "p value" when there are exact values in dataset using "exact" argument 0:03:42 how to change the alternative hypothesis using the "alt" argument 0:04:03 how to change confidence level using the "conf.level" command 0:04:13 how to calculate the covariance in R using the "cov" command 0:04:27 how to produce all possible pair-wise plots using the "pairs" command 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 0:05:26 how to produce a correlation matrix using the "cor" command and "method" argument 0:05:37 how to deal with categorical variables in the dataset when creating correlation matrix by subsetting data using square brackets 0:06:18 how to produce the covariance matrix using the "cov" command
R Tutorial: Path Analysis and Mediation using Lavaan
 
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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: 24141 Jordan Clark
Statistics with R: Convert data type FACTOR to NUMERIC and similar operations
 
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Tutorial guide to R for beginners Visit me at: http://www.statisticsmentor.com
Views: 65324 Phil Chan
Social Media Analytics - Twitter Analysis in R (Example @realDonaldTrump)
 
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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: 6265 The Data Science Show
Robust or Clustered Errors and Post-Regression Statistics - R for Economists Moderate 2
 
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This series of videos will serve as an introduction to the R statistics language, targeted at economists. In this video we cover what to do once you've already run your regression! We pull out the predicted values and residuals with predict() and residuals(), we use Breusch-Pagan (bptest()) to check for heteroskedasticity, we calculate heteroskedasticity- or cluster-robust standard errors with coeftest() in the sandwich package, and we perform F-tests of regression coefficients with linearHypothesis() in the AER package. These commands and tests work with all kinds of regression commands, not just OLS (lm()). The code and script for this video can be found at https://www.dropbox.com/s/py6v5rnshyxsbfc/Moderate%202%20Robust%20Errors%20and%20Post%20Regression%20Tests.R?dl=1 Download the code from all my R videos at once at http://nickchk.com/R%20for%20Economists%20Code.zip You can find links to every video in the series here: http://nickchk.com/videos.html#rstats There are videos on: [BASIC] Getting Started, Getting Help, Objectives and Variables, Vectors and Matrices, Data Frames, Packages, Summary Statistics (of One and Two Variables), Plots and Graphs, and Linear Regression (OLS), [MODERATE] Regression Formulas, Robust or Clustered Standard Errors and Post-Regression Stats, Regression Plots, Instrumental Variables (IV Regression), Time Series, ARIMA and ARMA, Probit and Logit, Tobit and Heckman, Panel Data, and Missing Data, and [ADVANCED] Simulations, The Tidyverse, Reshape and Join/Merge, dplyr (Introduction, Piping, and Grouping), ggplot (Introduction, Geometries, Overlaid and Grouped Plots, and Titles and Labels), and vtable
Basic Excel Business Analytics #47: SST = SSR + SSE & R Squared & Standard Error of Estimate
 
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Download file from “Highline BI 348 Class” section: https://people.highline.edu/mgirvin/excelisfun.htm Learn: 1) (00:14) What we will do in this video: SST, SSR, SSE, R^2 and Standard Error 2) (00:44) What we did last video 3) (01:11) How do we think about “How good our Estimated Regression Line is”? 4) (01:26) What a Perfect Line would look like 5) (01:48) What is a Residual? 6) (04:35) Compare Ybar (Y Mean) Line to Estimated Regression Line 7) (06:18) Visual Proof of Calculations for SST, SSR, SSE and R^2. Visual proof that any Y value and Predicted Value has two parts: Explained and Explained, or, Error and Regression, or Residual and Regression. 8) (08:18) Benefit of using Regression Line over Y Bar Line. 9) (09:00) What we must Square the Errors / Residuals. 10) (11:02) Formulas for SST, SSR and SSE. 11) (12:12) Coefficient of Determination (goodness of Fit of Regression Equation to Sample Raw X Y Data Points), R Squared Formula. 12) (13:32) Use Excel FORECAST function to calculate Predicted Values. 13) (15:08) Using Excel to calculate Residuals 14) (15:45) SST = Sum of Squares Total, calculate in Excel 15) (16:29) SSR = Sum of Squares Regression, calculate in Excel 16) (17:06) SSE = Sum of Squares Error, calculate in Excel 17) (17:31) Alternative methods for calculating SST, SSR and SSE, including using the Excel function SUMSQ and residuals to calculate SSE. 18) (18:50) Use Excel to calculate Coefficient of Determination, R Squared (three methods: SSR/SST, 1-SSE/SST and RSQ Function) 19) (20:58) Relationship between Coefficient of Determination (R Squared) and Coefficient of Correlation 20) (24:42) Estimate of Variation (Mean Square Error = MSE) and Standard Deviation (Standard Error of the Estimate = s) for the Regression Line / Equation. 21) (27:20) Calculate Standard Error of the Estimate using STEXY function in Excel. 19) (28:14) Second Example on BMX Racing Bike and Price data set. 22) (30:02) Summary and Conclusion Download Excel File Not: After clicking on link, Use Ctrl + F (Find) and search for “Highline BI 348 Class” or for the file name as seen at the beginning of the video.
Views: 10276 ExcelIsFun
Multiple Linear Regression in R | R Tutorial 5.3 | MarinStatsLectures
 
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Multiple Linear Regression Model in R; Fitting the model and interpreting the outcomes! Practice Dataset: (https://bit.ly/2rOfgEJ); Linear Regression Concept and with R (https://bit.ly/2z8fXg1) More Statistics and R Programming Tutorial (https://goo.gl/4vDQzT) Learn how to fit and interpret output from a multiple linear regression model in R and produce summaries. ▶︎ You will learn to use "lm", "summary", "cor", "confint" functions. ▶︎ You will also learn to use "plot" function for producing residual and QQ plots in R. ▶︎ We recommend that you first watch our video on simple linear regression concept (https://youtu.be/vblX9JVpHE8) and in R (https://youtu.be/66z_MRwtFJM) ▶︎▶︎Download the dataset here: https://statslectures.com/r-scripts-datasets ▶︎▶︎Like to support us? You can Donate https://statslectures.com/support-us or Share our Videos and help us reach more people! ◼︎ Table of Content: 0:00:07 Multiple Linear Regression Model 0:00:32 How to fit a linear model in R? using the "lm" function 0:00:36 How to access the help menu in R for multiple linear regression 0:01:06 How to fit a linear regression model in R with two explanatory or X variables 0:01:19 How to produce and interpret the summary of linear regression model fit in R 0:03:16 How to calculate Pearson's correlation between the two variables in R 0:03:26 How to interpret the collinearity between two variables in R 0:03:49 How to create a confidence interval for the model coefficients in R? using the "confint" function 0:03:57 How to interpret the confidence interval for our model's coefficients in R 0:04:13 How to fit a linear model using all of the X variables in R 0:04:27 how to check the linear regression model assumptions in R? by examining plots of the residuals or errors using the "plot(model)" function ►► Watch More: ►Linear Regression Concept and with R https://bit.ly/2z8fXg1 ►R Tutorials for Data Science https://bit.ly/1A1Pixc ►Getting Started with R (Series 1): https://bit.ly/2PkTneg ►Graphs and Descriptive Statistics in R (Series 2): https://bit.ly/2PkTneg ►Probability distributions in R (Series 3): https://bit.ly/2AT3wpI ►Bivariate analysis in R (Series 4): https://bit.ly/2SXvcRi ►Linear Regression in R (Series 5): https://bit.ly/1iytAtm ►ANOVA Concept and with R https://bit.ly/2zBwjgL ►Linear Regression Concept and with R https://bit.ly/2z8fXg1 ► Intro to Statistics Course: https://bit.ly/2SQOxDH ►Statistics & R Tutorials: Step by Step https://bit.ly/2Qt075y This video is a tutorial for programming in R Statistical Software for beginners, using RStudio. Follow MarinStatsLectures Subscribe: https://goo.gl/4vDQzT website: https://statslectures.com Facebook:https://goo.gl/qYQavS Twitter:https://goo.gl/393AQG Instagram: https://goo.gl/fdPiDn Our Team: Content Creator: Mike Marin (B.Sc., MSc.) Senior Instructor at UBC. Producer and Creative Manager: Ladan Hamadani (B.Sc., BA., MPH) These videos are created by #marinstatslectures to support some courses at The University of British Columbia (UBC) (#IntroductoryStatistics and #RVideoTutorials for Health Science Research), although we make all videos available to the everyone everywhere for free. Thanks for watching! Have fun and remember that statistics is almost as beautiful as a unicorn!
Summary of Linear Regression Model in R
 
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In this video you will understand about the summary of linear regression model fitted by the function lm() in R. You will get to know about what is residuals,errors,Root mean squared error or RMSE,p-value,standard error and t- statistic and F statistics This channel includes machine learning algorithms and implementation of machine learning algorithms in R like random forest algorithm in R,neural networks algorithms in R,decision tree in R and so on.Please do subscribe and like this channel for more videos on advances topics like deeplearning,graph theory,etc.
Easy Econometric Series R-square  & standard error in URDU/HINDI
 
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Coefficient of determination & standard error of parameters estimates HERE ARE THE LINKS FOR MY ALL VIDEOS YOU MAY LIKE  Links for; MOTIVATIONAL videos (40 + videos) https://www.youtube.com/watch?v=WM3Upd0MeDg&index=19&list=PLU-cxjF-s0HmnD_TYqmkb6VBL6AusSB_Y Basics of ECONOMICS; Micro & Macro (25 + videos) https://www.youtube.com/playlist?list=PLU-cxjF-s0HngkQ9xdQ5waYtpEO2MkP-D ECONOMETRICS; An introduction (20 + videos) https://www.youtube.com/watch?v=WNf1DLTRlTo&index=7&list=PLU-cxjF-s0Hk0DOXPqauiHBfRgaFpqYxK&t=0s RESEARCH & THESIS Writing (6 videos) https://www.youtube.com/watch?v=ib7yYggOgpA&t=308s&list=PLU-cxjF-s0HlVJnf2dev1KtoAD3ZljWVg&index=3 OLIGOPOLY & GAME THEORY ( 6 videos) https://www.youtube.com/playlist?list=PLU-cxjF-s0HnlCialpcyfz0O8xminDXja Principles of MARKETING ( 10+ videos) https://www.youtube.com/watch?v=mCAogfEz6bs&t=0s&list=PLU-cxjF-s0HlgTwSl9Xk3bx5z8q7kOD6F&index=12 Management; HRM & STRATEGIC MANAGEMENT ( 6 videos) https://www.youtube.com/watch?v=uO3azOb2icE&t=314s&list=PLU-cxjF-s0Hml7o706eKofZ7D4bXIcJwH&index=6 ========= About KOKAB MANZOOR ========= Kokab Manzoor is Certified Trainer, Speaker & Career Counsellor. He has trained thousands of students & Professionals about Leadership & Management skills, Motivation, Personality Grooming, Career selection and about variety of other life skills. Has a sound understanding of needed traits for workplace success and a strong ability to train employees in improving those characteristics. Follow me www.Youtube.com/kokabmanzoor www.Facebook.com/kokabmanzoor19 [email protected] http://kokabmanzoor1.blogspot.com/
Views: 1316 Kokab Manzoor
Statistics with R (1) - Linear regression
 
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In this video, I show how to use R to fit a linear regression model using the lm() command. I also introduce how to plot the regression line and the overall arithmetic mean of the response variable, and I briefly explain the use of diagnostic plots to inspect the residuals. Basic features of the R interface (script window, console window) are introduced. The R code used in this video is: data(airquality) names(airquality) #[1] "Ozone" "Solar.R" "Wind" "Temp" "Month" "Day" plot(Ozone~Solar.R,data=airquality) #calculate mean ozone concentration (na´s removed) mean.Ozone=mean(airquality$Ozone,na.rm=T) abline(h=mean.Ozone) #use lm to fit a regression line through these data: model1=lm(Ozone~Solar.R,data=airquality) model1 abline(model1,col="red") plot(model1) termplot(model1) summary(model1)
Views: 333420 Christoph Scherber