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R programming for beginners - This video is an introduction to R programming. I have another channel dedicated to R teaching: https://www.youtube.com/c/rprogramming101 In this video I provide a tutorial on some statistical analysis (specifically using the t-test and linear regression). I also demonstrate how to use dplyr and ggplot to do data manipulation and data visualisation. Its R programming for beginners really and is filled with graphics, quantitative analysis and some explanations as to how statistics work. If you’re a statistician, into data science or perhaps someone learning bio-stats and thinking about learning to use R for quantitative analysis, then you’ll find this video useful. Importantly, R is free. If you learn R programming you’ll have it for life. This video was sponsored by the University of Edinburgh. Find out more about their programmes at http://edin.ac/2pTfis2 This channel focusses on global health and public health - so please consider subscribing if you’re someone wanting to make the world a better place – I’d love to you join this community. I have videos on epidemiology, study design, ethics and many more.

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Learn how to perform a Chi Square Test with this easy to follow statistics video. I also provided the links for my other statistics videos as well. Chi Square Test - with contingency table https://www.youtube.com/watch?v=misMgRRV3jQ Hypothesis testing - two tailed test https://www.youtube.com/watch?v=0XXT3bIY_pw Hypothesis testing - one tailed test https://www.youtube.com/watch?v=lNoxKsuJ6Xc Confidence Intervals - with 't' value https://www.youtube.com/watch?v=UmAJJtEo6cQ Practice Quiz - z-test and t-test https://www.youtube.com/watch?v=o_QGaqYAqjo YouTube Channel: http://Youtube.com/MathMeeting Website: http://MathMeeting.com
Views: 349050 Math Meeting

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*** IMPROVED VERSION of this video here: https://youtu.be/tDLcBrLzBos I describe the standard normal distribution and its properties with respect to the percentage of observations within each standard deviation. I also make reference to two key statistical demarcation points (i.e., 1.96 and 2.58) and their relationship to the normal distribution. Finally, I mention two tests that can be used to test normal distributions for statistical significance. normal distribution, normal probability distribution, standard normal distribution, normal distribution curve, bell shaped curve
Views: 1130095 how2stats

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ATEd

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Tutorial introducing the idea of linear regression analysis and the least square method. Typically used in a statistics class. Playlist on Linear Regression http://www.youtube.com/course?list=ECF596A4043DBEAE9C Like us on: http://www.facebook.com/PartyMoreStudyLess Created by David Longstreet, Professor of the Universe, MyBookSucks http://www.linkedin.com/in/davidlongstreet
Views: 792576 statisticsfun

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The kind of graph and analysis we can do with specific data is related to the type of data it is. In this video we explain the different levels of data, with examples. Subtitles in English and Spanish.
Views: 922132 Dr Nic's Maths and Stats

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This video reviews the scales of measurement covered in introductory statistics: nominal, ordinal, interval, and ratio (Part 1 of 2). Scales of Measurement Nominal, Ordinal, Interval, Ratio YouTube Channel: https://www.youtube.com/user/statisticsinstructor Subscribe today! Lifetime access to SPSS videos: http://tinyurl.com/m2532td Video Transcript: In this video we'll take a look at what are known as the scales of measurement. OK first of all measurement can be defined as the process of applying numbers to objects according to a set of rules. So when we measure something we apply numbers or we give numbers to something and this something is just generically an object or objects so we're assigning numbers to some thing or things and when we do that we follow some sort of rules. Now in terms of introductory statistics textbooks there are four scales of measurement nominal, ordinal, interval, and ratio. We'll take a look at each of these in turn and take a look at some examples as well, as the examples really help to differentiate between these four scales. First we'll take a look at nominal. Now in a nominal scale of measurement we assign numbers to objects where the different numbers indicate different objects. The numbers have no real meaning other than differentiating between objects. So as an example a very common variable in statistical analyses is gender where in this example all males get a 1 and all females get a 2. Now the reason why this is nominal is because we could have just as easily assigned females a 1 and males a 2 or we could have assigned females 500 and males 650. It doesn't matter what number we come up with as long as all males get the same number, 1 in this example, and all females get the same number, 2. It doesn't mean that because females have a higher number that they're better than males or males are worse than females or vice versa or anything like that. All it does is it differentiates between our two groups. And that's a classic nominal example. Another one is baseball uniform numbers. Now the number that a player has on their uniform in baseball it provides no insight into the player's position or anything like that it just simply differentiates between players. So if someone has the number 23 on their back and someone has the number 25 it doesn't mean that the person who has 25 is better, has a higher average, hits more home runs, or anything like that it just means they're not the same playeras number 23. So in this example its nominal once again because the number just simply differentiates between objects. Now just as a side note in all sports it's not the same like in football for example different sequences of numbers typically go towards different positions. Like linebackers will have numbers that are different than quarterbacks and so forth but that's not the case in baseball. So in baseball whatever the number is it provides typically no insight into what position he plays. OK next we have ordinal and for ordinal we assign numbers to objects just like nominal but here the numbers also have meaningful order. So for example the place someone finishes in a race first, second, third, and so on. If we know the place that they finished we know how they did relative to others. So for example the first place person did better than second, second did better than third, and so on of course right that's obvious but that number that they're assigned one, two, or three indicates how they finished in a race so it indicates order and same thing with the place finished in an election first, second, third, fourth we know exactly how they did in relation to the others the person who finished in third place did better than someone who finished in fifth let's say if there are that many people, first did better than third and so on. So the number for ordinal once again indicates placement or order so we can rank people with ordinal data. OK next we have interval. In interval numbers have order just like ordinal so you can see here how these scales of measurement build on one another but in addition to ordinal, interval also has equal intervals between adjacent categories and I'll show you what I mean here with an example. So if we take temperature in degrees Fahrenheit the difference between 78 degrees and 79 degrees or that one degree difference is the same as the difference between 45 degrees and 46 degrees. One degree difference once again. So anywhere along that scale up and down the Fahrenheit scale that one degree difference means the same thing all up and down that scale. OK so if we take eight degrees versus nine degrees the difference there is one degree once again. That's a classic interval scale right there with those differences are meaningful and we'll contrast this with ordinal in just a few moments but finally before we do let's take a look at ratio.
Views: 385203 Quantitative Specialists

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A description of the concepts behind Analysis of Variance. There is an interactive visualization here: http://demonstrations.wolfram.com/VisualANOVA/ but I have not tried it, and this: http://rpsychologist.com/d3-one-way-anova has another visualization
Views: 557573 J David Eisenberg

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An explanation of how to compute the chi-squared statistic for independent measures of nominal data. For an explanation of significance testing in general, see http://evc-cit.info/psych018/hyptest/index.html There is also a chi-squared calculator at http://evc-cit.info/psych018/chisquared/index.html
Views: 1000616 J David Eisenberg

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Also known as a "Goodness of Fit" test, use this single sample Chi-Square test to determine if there is a significant difference between Observed and Expected values. This video shows a step-by-step method for calculating Chi-square.
Views: 433270 Eugene O'Loughlin

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Views: 26037 Fast learning classes

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This video describes five common methods of sampling in data collection. Each has a helpful diagrammatic representation. You might like to read my blog: https://creativemaths.net/blog/
Views: 787569 Dr Nic's Maths and Stats

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Views: 93907 Cognitive Class

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We review what the main goals of regression models are, see how the linear regression models tie to the concept of linear equations, and learn to interpret the coefficients of a simple linear regression model with an example. TABLE OF CONTENTS: 00:00 Simple Linear Regression 00:17 Objectives of Regressions 02:54 Variable’s Roles 03:30 The Magic: A Linear Equation 04:21 Linear Equation Example 05:24 Changing the Intercept 06:02 Changing the Slope 07:00 But the world is not linear! 07:44 Simple Linear Regression Model 08:25 Linear Regression Example 09:16 Data for Example 09:46 Simple Linear Regression Model 10:17 Regression Result 11:02 Interpreting the Coefficients 12:38 Estimated vs. Actual Values
Views: 381778 dataminingincae

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http://www.ted.com With the drama and urgency of a sportscaster, statistics guru Hans Rosling uses an amazing new presentation tool, Gapminder, to present data that debunks several myths about world development. Rosling is professor of international health at Sweden's Karolinska Institute, and founder of Gapminder, a nonprofit that brings vital global data to life. (Recorded February 2006 in Monterey, CA.) TEDTalks is a daily video podcast of the best talks and performances from the TED Conference, where the world's leading thinkers and doers give the talk of their lives in 18 minutes. TED stands for Technology, Entertainment, Design, and TEDTalks cover these topics as well as science, business, development and the arts. Closed captions and translated subtitles in a variety of languages are now available on TED.com, at http://www.ted.com/translate. Follow us on Twitter http://www.twitter.com/tednews Checkout our Facebook page for TED exclusives https://www.facebook.com/TED
Views: 2912895 TED

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This short video gives an explanation of the concept of confidence intervals, with helpful diagrams and examples. Find out more on Statistics Learning Centre: http://statslc.com or to see more of our videos: https://wp.me/p24HeL-u6
Views: 781607 Dr Nic's Maths and Stats

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Dr. Manishika Jain in this lecture explains the meaning of Sampling & Types of Sampling Research Methodology Population & Sample Systematic Sampling Cluster Sampling Non Probability Sampling Convenience Sampling Purposeful Sampling Extreme, Typical, Critical, or Deviant Case: Rare Intensity: Depicts interest strongly Maximum Variation: range of nationality, profession Homogeneous: similar sampling groups Stratified Purposeful: Across subcategories Mixed: Multistage which combines different sampling Sampling Politically Important Cases Purposeful Sampling Purposeful Random: If sample is larger than what can be handled & help to reduce sample size Opportunistic Sampling: Take advantage of new opportunity Confirming (support) and Disconfirming (against) Cases Theory Based or Operational Construct: interaction b/w human & environment Criterion: All above 6 feet tall Purposive: subset of large population – high level business Snowball Sample (Chain-Referral): picks sample analogous to accumulating snow Advantages of Sampling Increases validity of research Ability to generalize results to larger population Cuts the cost of data collection Allows speedy work with less effort Better organization Greater brevity Allows comprehensive and accurate data collection Reduces non sampling error. Sampling error is however added. Population & Sample @2:25 Sampling @6:30 Systematic Sampling @9:25 Cluster Sampling @ 11:22 Non Probability Sampling @13:10 Convenience Sampling @15:02 Purposeful Sampling @16:16 Advantages of Sampling @22:34 #Politically #Purposeful #Methodology #Systematic #Convenience #Probability #Cluster #Population #Research #Manishika #Examrace For IAS Psychology postal Course refer - http://www.examrace.com/IAS/IAS-FlexiPrep-Program/Postal-Courses/Examrace-IAS-Psychology-Series.htm For NET Paper 1 postal course visit - https://www.examrace.com/CBSE-UGC-NET/CBSE-UGC-NET-FlexiPrep-Program/Postal-Courses/Examrace-CBSE-UGC-NET-Paper-I-Series.htm types of sampling types of sampling pdf probability sampling types of sampling in hindi random sampling cluster sampling non probability sampling systematic sampling
Views: 383889 Examrace

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Views: 515765 edureka!

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Principal Component Analysis, is one of the most useful data analysis and machine learning methods out there. It can be used to identify patterns in highly complex datasets and it can tell you what variables in your data are the most important. Lastly, it can tell you how accurate your new understanding of the data actually is. In this video, I go one step at a time through PCA, and the method used to solve it, Singular Value Decomposition. I take it nice and slowly so that the simplicity of the method is revealed and clearly explained. There is a minor error at 1:47: Points 5 and 6 are not in the right location If you are interested in doing PCA in R see: https://youtu.be/0Jp4gsfOLMs For a complete index of all the StatQuest videos, check out: https://statquest.org/video-index/ If you'd like to support StatQuest, please consider a StatQuest t-shirt or sweatshirt... https://teespring.com/stores/statquest ...or buying one or two of my songs (or go large and get a whole album!) https://joshuastarmer.bandcamp.com/ ...or just donating to StatQuest! https://www.paypal.me/statquest

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Description

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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: 1017238 David Langer

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NOTE: On April 2, 2018 I updated this video with a new video that goes, step-by-step, through PCA and how it is performed. Check it out! https://youtu.be/FgakZw6K1QQ RNA-seq results often contain a PCA or MDS plot. This StatQuest explains how these graphs are generated, how to interpret them, and how to determine if the plot is informative or not. I've got example code (in R) for how to do PCA and extract the most important information from it on the StatQuest website: https://statquest.org/2015/08/13/pca-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/ ...or just donating to StatQuest! https://www.paypal.me/statquest

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Multivariate distance with the Mahalanobis distance. Using eigenvectors and eigenvalues of a matrix to rescale variables.
Views: 59800 Matthew E. Clapham

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Updated video 2018: SPSS for Beginners - Introduction https://youtu.be/_zFBUfZEBWQ This video provides an introduction to SPSS/PASW. It shows how to navigate between Data View and Variable View, and shows how to modify properties of variables.
Views: 1566565 Research By Design

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Short tutorial on exporting SPSS to Microsoft Word to accompany my book 'Discovering Statistics Using SPSS'.
Views: 211130 Andy Field

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Views: 630213 Quantitative Specialists

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This video will tell you difference between parametric and non-parametric methods in machine learning.
Views: 11842 HowTo

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Use simple data analysis techniques in SPSS to analyze survey questions.
Views: 856610 Claus Ebster

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Views: 339631 NurseKillam

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Lecturer: Dr. Erin M. Buchanan Missouri State University Spring 2017 This video covers the differences between and how to calculate the following nominal data tests: proportion test (independent), goodness of fit chi-square, independence chi-square, McNemar's, and Fisher's Exact test. Lecture materials and assignment available at statstools.com. http://statstools.com/learn/advanced-statistics/
Views: 508 Statistics of DOOM

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This video covers how to find the weighted mean for a set of data. Remember that each data point is multiplied by a given weight, and then divided by the total weight. for more videos visit http://mysecretmathtutor.com
Views: 255506 MySecretMathTutor

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Views: 85717 study with chanchal

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The main types of probability sampling methods are simple random sampling, stratified sampling, cluster sampling, multistage sampling, and systematic random sampling. The key benefit of probability sampling methods is that they guarantee that the sample chosen is representative of the population
Views: 149831 Manager Sahab

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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: 6877 The Data Science Show

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Views: 510828 edureka!

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Learn Advanced Data Analytics www.DataStrategyWithJonathan.com Martin Chan http://linkedin.com/in/martin-chan-tc 3:24 Tools for Survey Analytics 5:23 The Importance of Reproducibility 8:50 RQDA 11:28 16k R Packages 14:53 Opensource 20:25 The value of Community 26:05 Transitioning from Excel to R Programming 30:55 Learning to Code 37:18 Scalability 44:00 Statistics 49:00 When to use Excel vs R Programming 52:49 RMarkdown 1:02:00 Different Philosophies 1:06:00 Don't show code to stakeholders 1:08:00 Power Point is like time down the drain 1:12:40 Functions and Gists 1:15:00 Make your own package 1:24:00 No Code tools to help you get started with coding Learn how non coders can learn to code using tools like Excel VBA and R Tidyverse. Build flexible reports with R Shiny Flexdashboard R Packages mentioned in this video - Data Analysis: tidyverse (dplyr, tidyr, ggplot2) (06:25) - Qualitative Analysis: RQDA (09:02) Learn more about RQDQ at Martins Blog https://martinctc.github.io/blog/a-short-r-package-review-rqda/ - Combining R and Python in RStudio: reticulate (22:04) - Outputs: rmarkdown (52:55):, flexdashboard (55:44) - Automating PowerPoint: mschart, officer (59:23) - Point-and-click "coding": ggplotAssist, esquisse (1:27:17) Hilary Parker's blog on writing R package from scratch - on cats! (1:15:25) https://hilaryparker.com/2014/04/29/writing-an-r-package-from-scratch/ Hadley Wickham's talk on "You can't do data science in a GUI" (1:28:05) https://speakerdeck.com/hadley/you-cant-do-data-science-in-a-gui
Views: 682 Jonathan Ng

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An ROC curve is the most commonly used way to visualize the performance of a binary classifier, and AUC is (arguably) the best way to summarize its performance in a single number. As such, gaining a deep understanding of ROC curves and AUC is beneficial for data scientists, machine learning practitioners, and medical researchers (among others). SUBSCRIBE to learn data science with Python: https://www.youtube.com/dataschool?sub_confirmation=1 JOIN the "Data School Insiders" community and receive exclusive rewards: https://www.patreon.com/dataschool RESOURCES: - Transcript and screenshots: https://www.dataschool.io/roc-curves-and-auc-explained/ - Visualization: http://www.navan.name/roc/ - Research paper: http://people.inf.elte.hu/kiss/13dwhdm/roc.pdf LET'S CONNECT! - Newsletter: https://www.dataschool.io/subscribe/ - Twitter: https://twitter.com/justmarkham - Facebook: https://www.facebook.com/DataScienceSchool/ - LinkedIn: https://www.linkedin.com/in/justmarkham/
Views: 315276 Data School

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Define the research questions Define the target population, target variable and target parameter Define the quality measure Specify the sampling frame Specify the sampling design Determine the sample size Determine the sampling plan Carry out the sampling in the field

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Check out http://www.engineer4free.com for more free engineering tutorials and math lessons! Project Management Tutorial: Use forward and backward pass to determine project duration and critical path. Please support my work: PATREON | https://www.patreon.com/Engineer4Free Every dollar is seriously appreciated and enables me to continue making more tutorials
Views: 872394 Engineer4Free

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VCE Further Maths Tutorials. Core (Data Analysis) Tutorial: Smoothing Time Series Data. This tute runs through mean and median smoothing, from a table and straight onto a graph, using 3 and 5 mean & median smoothing and 4 point smoothing with centring. For more tutorials, visit www.vcefurthermaths.com
Views: 58614 vcefurthermaths

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Views: 436 Clay Reisler

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This clip show the calculation of each of these values for a small data set.
Views: 535251 John Quinn