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The RevMan Caculator: Calculating missing standard deviations
 
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Entering Data with the RevMan Calculator: Calculating missing standard deviations. Part 3 in a video series produced by Cochrane UK.
Views: 3611 Cochrane UK
Overview of Missing Data Causes _ Treatments in Clinical Trials
 
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Presented by Andrew Grannell Senior Statistician at Statistical Solutions. Here are the links for the reports mentioned in the webinar: 1) "Note for Guidance on Statistical Principles for Clinical Trials" - ICH, 1998 http://www.emea.europa.eu/docs/en_GB/document_library/Scientific_guideline/2009/09/WC500002928.pdf 2) "Guidance on Important Considerations for when Participation of Human subjects in Research is Discontinued" - OHRP, 2008 http://www.primr.org/uploadedFiles/PRIMR_Site_Home/Public_Policy/Recently_Files_Comments/Draft_Guidance_on_Discontinued_Participation.pdf 3) "The Prevention and Treatment of Missing Data in Clinical Trials" - NRC, 2010 http://www.nap.edu/catalog.php?record_id=12955 4) "Guidance for Sponsors, Clinical Investigators and IRB's; Data Retention when Subjects withdraw from FDA-Regulated Clinical Trials - FDA, 2008 http://www.fda.gov/downloads/RegulatoryInformation/Guidances/ucm126489.pdf 5) "Guideline on Missing Data in Confirmatory Clinical Trials" - EMA, 2010 http://www.ema.europa.eu/docs/en_GB/document_library/Scientific_guideline/2010/09/WC500096793.pdf
Views: 673 StatSolSoftware
Expected Value and Variance of Discrete Random Variables
 
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An introduction to the concept of the expected value of a discrete random variable. I also look at the variance of a discrete random variable. The formulas are introduced, explained, and an example is worked through.
Views: 425991 jbstatistics
How to Use SPSS-Replacing Missing Data Using Multiple Imputation (Regression Method)
 
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Technique for replacing missing data using the regression method. Appropriate for data that may be missing randomly or non-randomly. Also appropriate for data that will be used in inferential analysis. Determining randomness of missing data can be confirmed with Little's MCAR Test (http://youtu.be/6ybgVTabJ6s). Resources: FAQ- http://sites.stat.psu.edu/~jls/mifaq.html Schafer, Joseph L. "Multiple imputation: a primer." Statistical methods in medical research 8.1 (1999): 3-15. Sterne, Jonathan AC, et al. "Multiple imputation for missing data in epidemiological and clinical research: potential and pitfalls." BMJ: British Medical Journal 338 (2009). McKnight, Patrick E., Katherine M. McKnight, and Aurelio Jose Figueredo. Missing data: A gentle introduction. Guilford Press, 2007. Haukoos, Jason S., and Craig D. Newgard. "Advanced statistics: missing data in clinical research—part 1: an introduction and conceptual framework." Academic Emergency Medicine 14.7 (2007): 662-668. Newgard, Craig D., and Jason S. Haukoos. "Advanced statistics: missing data in clinical research—part 2: multiple imputation." Academic Emergency Medicine 14.7 (2007): 669-678.
SEM in AMOS when you have incomplete data (new, 2018)
 
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This video provides an overview of SEM (using path analysis) in AMOS when you have missing data. It demonstrates how to estimate the basic model using FIML estimation (enacted by clicking on Estimate Means and Intercepts under Analysis Properties). It also demonstrates the use of the Regression Imputation approach to generate a complete dataset (which will allow you to use other options in AMOS such as Modification indices and Bootstrapping). The video does not cover the theory behind missing data mechanisms or the approaches recommended with particular patterns of missingness. The viewer is encouraged to read more on those topics. A copy of the SPSS data file used in the video can be downloaded here: https://drive.google.com/open?id=18azDsnEoPGBozyF4dMBrw3dh4QKcS4R1 A copy of the .amw file containing the path model generated for the video can be downloaded here: https://drive.google.com/open?id=10ryZkcsNacq_60BWaao7f-ftqkOtqYad A pdf copy of the page referenced in the video on interpreting fit statistics can be obtained here: https://drive.google.com/open?id=14zM-5fZUpN2drO3ZwTfm18ET2ybZBVOF You can also access another video on dealing with missing data by going here: https://youtube.com/watch?v=N-v_PFI98MI&feature=youtu.be For more instructional videos and other materials on various statistics topics, be sure to my webpages at the links below: Introductory statistics: https://sites.google.com/view/statisticsfortherealworldagent/home Multivariate statistics: https://sites.google.com/view/statistics-for-the-real-world/home
Views: 3391 Mike Crowson
Karl's Pearson Correlation in Hindi with solved Example By JOLLY Coaching
 
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Correlation using scattered diagram and KARL PARSON method is explained in this video along with example. This video include the detailed concept of solving any kind of problem related to correlation. Basically correlation refers to a statistical technique which we use to find out the relation exist between two or more variables. I hope this video will help you to solve any kind of problem related to Correlation. Thanks. JOLLY Coaching correlation regression correlation and regression correlation and regression correlation regression methods of correlation techniques of correlation karl's pearson method scattered diagram correlation in hindi correlation hindi correlation in hindi karl's pearson in hindi karl's pearson in hindi scattered diagram in hindi karl's pearson correlation coefficient of correlation how to calculate correlation how to calculate correlation in hindi how to calculate correlation using karl's pearson
Views: 352548 JOLLY Coaching
How To... Perform Simple Linear Regression by Hand
 
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Learn how to make predictions using Simple Linear Regression. To do this you need to use the Linear Regression Function (y = a + bx) where "y" is the dependent variable, "a" is the y intercept, "b" is the slope of the regression line, and "x" is the independent variable. This video also shows you how to determine the slope (b) of the regression line, and the y intercept (a). In order to determine the slope of a line you will need to first determine the Pearson Correlation Coefficient - this is described in a separate video (https://www.youtube.com/watch?v=2SCg8Kuh0tE).
Views: 527264 Eugene O'Loughlin
Missing Data Analysis : Multiple Imputation in R
 
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Paper: Advanced Data Analysis Module: Missing Data Analysis : Multiple Imputation in R Content Writer: Souvik Bandyopadhyay
Views: 23061 Vidya-mitra
Advanced growth modeling, missing data analysis, and survival analysis, Mplus Topic 4, Part 1
 
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Topic 4: Advanced growth modeling, missing data analysis, and survival analysis. Recorded presentation at Johns Hopkins University, March 23, 2010. Link to handouts associated with this segment: http://www.statmodel.com/download/Topic%204new.pdf NOTE: For more information or to engage in discussion about the topics covered in this video, please visit www.statmodel.com.
Views: 329 Mplus
Finding mean, median, and mode | Descriptive statistics | Probability and Statistics | Khan Academy
 
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Here we give you a set of numbers and then ask you to find the mean, median, and mode. It's your first opportunity to practice with us! Practice this lesson yourself on KhanAcademy.org right now: https://www.khanacademy.org/math/probability/descriptive-statistics/central_tendency/e/mean_median_and_mode?utm_source=YT&utm_medium=Desc&utm_campaign=ProbabilityandStatistics Watch the next lesson: https://www.khanacademy.org/math/probability/descriptive-statistics/central_tendency/v/exploring-mean-and-median-module?utm_source=YT&utm_medium=Desc&utm_campaign=ProbabilityandStatistics Missed the previous lesson? https://www.khanacademy.org/math/probability/descriptive-statistics/central_tendency/v/statistics-intro-mean-median-and-mode?utm_source=YT&utm_medium=Desc&utm_campaign=ProbabilityandStatistics Probability and statistics on Khan Academy: We dare you to go through a day in which you never consider or use probability. Did you check the weather forecast? Busted! Did you decide to go through the drive through lane vs walk in? Busted again! We are constantly creating hypotheses, making predictions, testing, and analyzing. Our lives are full of probabilities! Statistics is related to probability because much of the data we use when determining probable outcomes comes from our understanding of statistics. In these tutorials, we will cover a range of topics, some which include: independent events, dependent probability, combinatorics, hypothesis testing, descriptive statistics, random variables, probability distributions, regression, and inferential statistics. So buckle up and hop on for a wild ride. We bet you're going to be challenged AND love it! About Khan Academy: Khan Academy is a nonprofit with a mission to provide a free, world-class education for anyone, anywhere. We believe learners of all ages should have unlimited access to free educational content they can master at their own pace. We use intelligent software, deep data analytics and intuitive user interfaces to help students and teachers around the world. Our resources cover preschool through early college education, including math, biology, chemistry, physics, economics, finance, history, grammar and more. We offer free personalized SAT test prep in partnership with the test developer, the College Board. Khan Academy has been translated into dozens of languages, and 100 million people use our platform worldwide every year. For more information, visit www.khanacademy.org, join us on Facebook or follow us on Twitter at @khanacademy. And remember, you can learn anything. For free. For everyone. Forever. #YouCanLearnAnything Subscribe to KhanAcademy’s Probability and Statistics channel: https://www.youtube.com/channel/UCRXuOXLW3LcQLWvxbZiIZ0w?sub_confirmation=1 Subscribe to KhanAcademy: https://www.youtube.com/subscription_center?add_user=khanacademy
Views: 2103685 Khan Academy
Missing value analysis in SPSS - part 1
 
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This video demonstrates missing value analysis in SPSS
Views: 60326 Murtaza Haider
Normality test using SPSS: How to check whether data are normally distributed
 
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If data need to be approximately normally distributed, this tutorial shows how to use SPSS to verify this. On a side note: my new project: http://howtowritecitations.com. Statistical analyses often have dependent variables and independent variables and many parametric statistical methods require that the dependent variable is approximately normally distributed for each category of the independent variable. Let us assume that we have a dependent variable, exam scores, and an independent variable, gender. In short, we must investigate the following numerical and visual outputs (and the tutorial shows how to do just that): -The Skewness & kurtosis z-values, which should be somewhere in the span -1.96 to +1.96; -The Shapiro-Wilk p-value, which should be above 0.05; -The Histograms, Normal Q-Q plots and Box plots, which should visually indicate that our data are approximately normally distributed. Remember that your data do not have to be perfectly normally distributed. The main thing is that they are approximately normally distributed, and that you check each category of the independent variable. (In our example, both male and female data.) Step 1. In the menu of SPSS, click on Analyze, select Descriptive Statistics and Explore. Step 2. Set exam scores as the dependent variable, and gender as the independent variable. Step 3. Click on Plots, select "Histogram" (you do not need "Stem-and-leaf") and select "Normality plots with tests" and click on Continue, then OK. Step 4. Start with skewness and kurtosis. The skewness and kurtosis measures should be as close to zero as possible, in SPSS. In reality, however, data are often skewed and kurtotic. A small departure from zero is therefore no problem, as long as the measures are not too large compare to their standard errors. As a consequence, you must divide the measure by its standard error, and you need to do this by hand, using a calculator. This will give you the z-value, which, as I said, should be somewhere within -1.96 to +1.96. Let us start with the males in our example. To calculate the skewness z-value, divide the skewness measure by its standard error. All z-values in the tutorial video are within ±1.96. We can conclude that the exam score data are a little skewed and kurtotic, for both males and females, but they do not differ significantly from normality. Step 5. Check the Shapiro-Wilk test statistic. The null hypothesis for this test of normality is that the data are normally distributed. The null hypothesis is rejected if the p-value is below 0.05. In SPSS output, the p-value is labeled "Sig". In our example, the p-values for males and females are above 0.05, so we keep the null hypothesis. The Shapiro-Wilk test thus indicates that our example data are approximately normally distributed. Step 6. Next, let us look at the graphical figures, for both male and female data. Inspect the histograms visually. They should have the approximate shape of a normal curve. Then, look at the normal Q-Q plot. The dots should be approximately distributed along the line. This indicates that the data are approximately normally distributed. Skip the Detrended Q-Q plots. You do not need them. Finally, look at the box plots. They should be approximately symmetrical. The video contains references to books and articles. About writing out the results: I would put it under the sub-heading "Sample characteristics", and the video contains examples of how I would write. In this tutorial, I show you how to check if a dependent variable is approximately normally distributed for each category of an independent variable. I am assuming that you, eventually, want to use a certain parametric statistical methods to explore and investigate your data. If it turns out that your dependent variable is not approximately normally distributed for each category of the independent variable, it is no problem. In such case, you will have to use non-parametric methods, because they make no assumptions about the distributions. Good luck with your research. Text and video (including audio) © Kent Löfgren, Sweden Here are the references that I discuss in the video (thanks Abdul Syafiq Bahrin for typing them our for me): Cramer, D. (1998). Fundamental statistics for social research. London: Routledge. Cramer, D., & Howitt, D. (2004). The SAGE dictionary of statistics. London: SAGE. Doane, D. P., & Seward, L.E. (2011). Measuring Skewness. Journal of Statistics Education, 19(2), 1-18. Razali, N. M., & Wah, Y. B. (2011). Power comparisons of Shapiro-Wilk, Kolmogorov-Smirnov, Liliefors and Anderson-Darling test. Journal of Statistical Modeling and Analytics, 2(1), 21-33. Shapiro, S. S., & Wilk, M. B. (1965). An Analysis of Variance Test for Normality (Complete Samples). Biometrika, 52(3/4), 591-611.
Views: 459931 Kent Löfgren
Multiple Linear Regression using Excel Data Analysis Toolpak
 
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LearnAnalytics demonstrates use of Multiple Linear Regression on Excel 2010. (Data Analysis Toolpak). Data set referenced in video can be downloaded at www.learnanalytics.in/blog/wp-content/uploads/2014/02/car_sales.xlsx
Views: 67722 Learn Analytics
Little's Missing Completely at Random (MCAR) Test - SPSS
 
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Learn how to perform and interpret Little's MCAR test in SPSS. Little's test tests the hypothesis that one's data are missing completely at random, which is an assumption that must be satisfied prior to replacing missing values with various imputation techniques. Missing value analysis
Views: 106683 how2stats
Univariate Analysis and Bivariate Analysis
 
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Subject: Social Work Education Paper:Research Methods and Statistics Module: Univariate Analysis & Bivariate Analysis Content Writer: Dr. Graciella Tavares
Views: 38295 Vidya-mitra
Categorical latent variable modeling with longitudinal data, Mplus Topic 6, Part 1
 
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Topic 6. Categorical latent variable modeling with longitudinal data. Recorded presentation at Freie University, Berlin, July 18, 2009. Link to handouts associated with this segment: http://www.statmodel.com/download/Topic6-v7.pdf NOTE: For more information or to engage in discussion about the topics covered in this video, please visit www.statmodel.com.
Views: 307 Mplus
How To... Perform a One-Way ANOVA Test (By Hand)
 
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Compare the means of three or more samples using a one-way ANOVA (Analysis of Variance) test to calculate the F statistic. This video shows one method for determining F using sums of squares.
Views: 172966 Eugene O'Loughlin
[#1]Assignment Problem|Hungarian Method[Solved Problem using Simple Algorithm] in OR: kauserwise
 
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NOTE: After row and column scanning, If you stuck with more than one zero in the matrix, please do the row scanning and column scanning (REPEATEDLY) as much as possible to cover that zeros with lines, based on algorithm If you still find some zeros without covered by lines, then we need to go for [DIAGONAL selection RULE ]for that I have uploaded a separate video to understand that method easily., please watch this link [ [#2]Assignment Problem||Hungarian Method[DIAGONAL RULE] When we Find More than one Zero ] https://youtu.be/-0DEQmp7B9o Here is the video about assignment problem - Hungarian method on Operations research, In this video we discussed what is assignment problem and how to solve using Hungarian method with step by step procedure of algorithm, hope this will help you to get the subject knowledge at the end. Thanks and All the best. To watch more tutorials pls visit: www.youtube.com/c/kauserwise * Financial Accounts * Corporate accounts * Cost and Management accounts * Operations Research * Statistics ▓▓▓▓░░░░───CONTRIBUTION ───░░░▓▓▓▓ If you like this video and wish to support this kauserwise channel, please contribute via, * Paytm a/c : 6383617203 * Western Union / MoneyGram [ Name: Kauser, Country: India & Email: [email protected] ] [Every contribution is helpful] Thanks & All the Best!!! ───────────────────────────
Views: 1680892 Kauser Wise
RClimTool (Tutorial Video) In English
 
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RClimtool has been designed with the objective to facilitate the performance of statistical analysis, quality control, filling missing data, homogeneity analysis and calculation of indicators for daily weather series of maximum temperature, minimum temperature and precipitation. User manual available here: http://www.aclimatecolombia.org/download/Investigacion%20Uno/RClimTool_UserManual.pdf Join us: https://groups.google.com/forum/?hl=es#!forum/rclimtool
Index Numbers
 
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index number statistics
Views: 394601 Yasser Khan
Statistical Programming DC - Numerical Analysis in R
 
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Abstract:  R has become the go-to environment to support data science and statistical applications across many fields. Researchers write papers in R, engineers develop runtime applications in R, and new statistical methods are first developed in R. Supporting this powerful environment is a complete programming language and a wealth of numerical analysis tools. R has a built-in matrix language that can solve mathematical problems above and beyond statistical regression. This talk will walk through R's tools for matrix manipulation, interpolation, integration, and root finding. Speaker:  James P. Howard, II
Views: 267 Casey Driscoll
Less than more than ogive for cumulative frequency distribution ll CBSE class 10 maths statistics
 
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Less than more than ogive for cumulative frequency distribution ll CBSE class 10 maths statistics Statistics | Cumulative Frequency Distribution | Less Than Type Ogive | Grade 10 Class 10 Maths Statistics | Cumulative Frequency Distribution | More Than Type Ogive | Grade X Class 10 Maths Ogive or Cumulative Frequency Curve How To Draw An Ogive How to draw Cumulative From graph, find Median from it Oswaal CBSE Sample Question Papers Class 10 Mathematics http://amzn.to/2gxJkSN Oswaal CBSE Sample Question Papers Class 10 Science http://amzn.to/2gSMPQX Oswaal CBSE Sample Question Papers Class 10 English Communicative http://amzn.to/2l02NNn Oswaal CBSE Sample Question Paper for Class 10 English Communicative, Hindi B, Science, Social Science and Maths (Set A 10SP) http://amzn.to/2xSw2b9 Shiv Das CBSE Past Years Board Papers Pack of 4 for Class 10 Maths Science Social Science English Communicative (2018 Board Exam Edition) http://amzn.to/2yv2akC Super 20 Mathematics Sample Papers Class 10th CBSE 2017-18 http://amzn.to/2ytokn9 Super 20 Science Sample Papers Class 10th CBSE 2017-18 http://amzn.to/2x99les Parker Frontier Matte Black CT Fountain Pen http://amzn.to/2f6ZvG0 Parker Ambient Laque Black GT Ball Pen http://amzn.to/2wbPqyW Paperkraft Premium Notebook and Pen Gift Set http://amzn.to/2wboMGu Parker Galaxy Gold Trim Ball Pen http://amzn.to/2y19HUk Lenovo Yoga Tab 3 8 Tablet (8 inch, 16GB, Wi-Fi + 4G LTE + Voice Calling), Slate Black http://amzn.to/2f8KWSu buy Redmi 4 (Gold, 64GB) http://amzn.to/2eAriya buy Cables Kart Omnidirectional 3.5mm Microphone with Stand for Laptop, PC - (Black) http://amzn.to/2wy7ekx buy Blue Microphones Snowball iCE Condenser Microphone (White) http://amzn.to/2wvD1Eu buy Photron Tripod Stedy 450 with 4.5 Feet Pan Head http://amzn.to/2gDHyiF buy Canon EOS 1200D 18MP Digital SLR Camera (Black) http://amzn.to/2eA0gY6 buy Sony Cybershot DSC-WX350/B 18.2MP Digital Camera (Black) http://amzn.to/2gveJRU If you like our videos, subscribe to our channel https://www.youtube.com/channel/UCEVG-1G2sP_CCvRUp3i_fyg Please Like Our Facebook Page. https://www.facebook.com/galaxycoachingclasses/ Or At https://www.facebook.com/galaxymathstricks/ https://www.facebook.com/cbsemaths.8.9.10/ Please Follow Me On Instagram https://www.instagram.com/chetanptl12/ Please Follow me on Twitter. https://twitter.com/chetan21385 Have fun, while you learn. Thanks for watching
Views: 469390 galaxy coaching classes
SPSS Tutorials: Binary Logistic Regression
 
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SPSS Tutorials: Binary Logistic Regression is part of the Departmental of Methodology Software tutorials sponsored by a grant from the LSE Annual Fund. For more information on the Departmental of Methodology visit www.lse.ac.uk/methodologyInstitute or follow us on twitter.com/MethodologyLSE LSE Annual Fund webpage http://www.alumni.lse.ac.uk/olc/pub/LHE/filemanager/annualfund/default.htm
Views: 242425 Methodology LSE
Introductory - Advanced factor analysis and SEM - Mplus Short Courses, Topic 1.
 
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Topic 1. Introductory - advanced factor analysis and structural equation modeling with continuous outcomes. Recorded presentation at Johns Hopkins University, August 20, 2009. Link to handouts associated with this segment: http://www.statmodel.com/download/Topic%201.pdf NOTE: For more information or to engage in discussion about the topics covered in this video, please visit www.statmodel.com.
Views: 3356 Mplus
Gradient Boost Part 1: Regression Main Ideas
 
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Gradient Boost is one of the most popular Machine Learning algorithms in use. And get this, it's not that complicated! This video is the first part in a series that walks through it one step at a time. This video focuses on the main ideas behind using Gradient Boost to predict a continuous value, like someone's weight. We call this, "using Gradient Boost for Regression". In the next video, we'll work through the math to prove that Gradient Boost for Regression really is this simple. In part 3, we'll walk though how Gradient Boost classifies samples into two different categories, and in part 4, we'll go through the math again, this time focusing on classification. This StatQuest assumes that you already understand.... Decision Trees: https://youtu.be/7VeUPuFGJHk AdaBoost: https://youtu.be/LsK-xG1cLYA ...and the tradeoff between Bias and Variance that plagues Machine Learning: https://youtu.be/EuBBz3bI-aA For a complete index of all the StatQuest videos, check out: https://statquest.org/video-index/ This StatQuest is based on the following sources: A 1999 manuscript by Jerome Friedman that introduced Stochastic Gradient Boost: https://statweb.stanford.edu/~jhf/ftp/stobst.pdf The Wikipedia article on Gradient Boosting: https://en.wikipedia.org/wiki/Gradient_boosting The scikit-learn implementation of Gradient Boosting: https://scikit-learn.org/stable/modules/ensemble.html#gradient-boosting If you'd like to support StatQuest, please consider a cool 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/ Lastly, if you want to keep up with me as I research and create new StatQuests, follow me on twitter: https://twitter.com/joshuastarmer
2013POL242HW4, Conservative Vote Intention (POL242Geneva2013)
 
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2013POL242HW4 *Reliability Analysis using the 2011 CES Data*. *Add weight* WEIGHT by WGTSAMP *Recode indicators of DV (vote intention for Conservative Party of Canada)* missing values CPS11_18 CPS11_23 (996 thru 999) missing values MBS11_D1 (-99) compute RECPS_18 = CPS11_18 / 10 compute RECPS_23 = CPS11_23 / 10 fre var=RECPS_18/. fre var=RECPS_23/. fre var=MBS11_D1/. *Checking that DV indicators go together* reliability /variables=CPS11_18 CPS11_23 MBS11_D1 /scale(all)=all /summary=All. reliability /variables=RECPS_18 RECPS_23 MBS11_D1 /scale(all)=all /summary=All. *Combining DV indicators into an index*. compute VoteIntC=RECPS_18+RECPS_23+MBS11_D1 recode VoteIntC (0 thru 10=1) (10.40 thru 20=2) (20.40 thru 30=3) into CVoteInt value labels CVoteInt 1'low' 2'med' 3'hi' *Checking the distribution of DV index*. fre var=VoteIntC /statistics mean median mode stddev variance skew kurtosis. fre var=CVoteInt /statistics mean median mode stddev variance skew kurtosis. *Correlation Analysis using the 2011 CES Data*. *Add weight* WEIGHT by WGTSAMP *Recode indicators of DV (vote intention for Conservative Party of Canada)* missing values CPS11_18 CPS11_23 (996 thru 999) missing values MBS11_D1 (-99) compute RECPS_18 = CPS11_18 / 10 compute RECPS_23 = CPS11_23 / 10 fre var=RECPS_18/. fre var=RECPS_23/. fre var=MBS11_D1/. *Combining DV indicators into an index*. compute VoteIntC=RECPS_18+RECPS_23+MBS11_D1 recode VoteIntC (0 thru 10=1) (10.40 thru 20=2) (20.40 thru 30=3) into CVoteInt value labels CVoteInt 1'low' 2'med' 3'hi' *Recode Attitudes towards the Gun Registry* missing values PES11_27 (8, 9) recode PES11_27 (7=1) (5=2) (3=3) (1=4) into AtSGuReg value labels AtSGuReg 1'strongly disagree' 2'somewhat disagree' 3'somewhat agree' 4'strongly agree' fre var=PES11_27 AtSGuReg/. *Recode Perceptions about the Performance of the Economy* missing values CPS11_40 (8, 9) recode CPS11_40 (3=1) (5=2) (1=3) into EconPerc value labels EconPerc 1'worse' 2'not made much difference' 3'better' fre var= CPS11_40 EconPerc/. *Recode Support for same-sex marriage* missing values PES11_29 (9) recode PES11_29 (1=3) (5=1) (8=2) into SSamSexM value labels SSamSexM 1'oppose' 2'no opinion' 3'favour' fre var=PES11_29 SSamSexM/. *Running Correlations between the DV and the 3 IVs*. Correlations VoteIntC AtSGuReg EconPerc SSamSexM *Running Correlations among DV and IVS. Correlation VoteIntC AtSGuReg EconPerc SSamSexM *Multivariate Regression Syntax using the 2011 CES Data.* *Add weight* WEIGHT by WGTSAMP *Recode indicators of DV (vote intention for Conservative Party of Canada)* missing values CPS11_18 CPS11_23 (996 thru 999) missing values MBS11_D1 (-99) compute RECPS_18 = CPS11_18 / 10 compute RECPS_23 = CPS11_23 / 10 fre var=RECPS_18/. fre var=RECPS_23/. fre var=MBS11_D1/. *Combining DV indicators into an index*. compute VoteIntC=RECPS_18+RECPS_23+MBS11_D1 recode VoteIntC (0 thru 10=1) (10.40 thru 20=2) (20.40 thru 30=3) into CVoteInt value labels CVoteInt 1'low' 2'med' 3'hi' *Recode Attitudes towards the Gun Registry* missing values PES11_27 (8, 9) recode PES11_27 (7=1) (5=2) (3=3) (1=4) into AtSGuReg value labels AtSGuReg 1'strongly disagree' 2'somewhat disagree' 3'somewhat agree' 4'strongly agree' fre var=PES11_27 AtSGuReg/. *Recode Perceptions about the Performance of the Economy* missing values CPS11_40 (8, 9) recode CPS11_40 (3=1) (5=2) (1=3) into EconPerc value labels RECPS_40 1'worse' 2'not made much difference' 3'better' fre var= CPS11_40 EconPerc/. *Recode Support for same-sex marriage* missing values PES11_29 (9) recode PES11_29 (1=3) (5=1) (8=2) into SSamSexM value labels REPES_29 1'oppose' 2'no opinion' 3'favour' fre var=PES11_29 SSamSexM/. *Running Multivariate Regression with the DV predicted by 3 Ivs*. Regression variables VoteIntC AtSGuReg EconPerc SSamSexM /statistics coeff r tol /descriptive=n /dependent=VoteIntC /method=enter. *Running the same Multivariate Regression in Stages* Regression variables VoteIntC AtSGuReg EconPerc SSamSexM /statistics coeff r tol /descriptives=n /dependent=VoteIntC /method=enter AtSGuReg /method=enter EconPerc /method=enter SSamSexM a. As a learning experience: 10 b. As a means of assessing student performance: 9 c. As preparation for future study and work life: 8.5 REFERENCE: 1. Anderson, Cameron D. 2008. "Economic Voting, Multilevel Governance and Information in Canada." Canadian Journal of Political Science Vol. 41, No. 2: 329-354. 2. Belanger, Eric and Bonnie M. Meguid. 2008. "Issus Salience, Issue Ownership, and Issue-based Vote Choice." Electoral Studies Vol. 27: 477-491. 3. Conservative Party of Canada. 2011. "Here for Canada." Last modified April 18. http://www.conservative.ca/media/2012/06/ConservativePlatform2011_ENs.pdf. 4. LeDuc, Lawrence. 2013. "The federal election in Canada, May 2011." Electoral Studies Vol. 31: 222-242. doi: 10.1016/j.electstud.2011.12.002
Views: 48 Pol242Geneva2013
How to Clean SPSS Data
 
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This video will teach you valuable skills to prepare your data for analysis in SPSS by describing the process of running frequencies, replacing missing data, and recoding items for reverse coding.
Views: 128410 CPG Orlando
Getting started in scikit-learn with the famous iris dataset
 
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Now that we've set up Python for machine learning, let's get started by loading an example dataset into scikit-learn! We'll explore the famous "iris" dataset, learn some important machine learning terminology, and discuss the four key requirements for working with data in scikit-learn. Download the notebook: https://github.com/justmarkham/scikit-learn-videos Iris dataset: http://archive.ics.uci.edu/ml/datasets/Iris scikit-learn dataset loading utilities: http://scikit-learn.org/stable/datasets/ Fast Numerical Computing with NumPy (slides): https://speakerdeck.com/jakevdp/losing-your-loops-fast-numerical-computing-with-numpy-pycon-2015 Fast Numerical Computing with NumPy (video): https://www.youtube.com/watch?v=EEUXKG97YRw Introduction to NumPy (PDF): http://www.engr.ucsb.edu/~shell/che210d/numpy.pdf WANT TO GET BETTER AT MACHINE LEARNING? HERE ARE YOUR NEXT STEPS: 1) WATCH my scikit-learn video series: https://www.youtube.com/playlist?list=PL5-da3qGB5ICeMbQuqbbCOQWcS6OYBr5A 2) SUBSCRIBE for more videos: https://www.youtube.com/dataschool?sub_confirmation=1 3) JOIN "Data School Insiders" to access bonus content: https://www.patreon.com/dataschool 4) ENROLL in my Machine Learning course: https://www.dataschool.io/learn/ 5) 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: 162031 Data School
Outputting Stata Summary and Regression Tables for Excel, Word, or LaTeX
 
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Don't you dare spend hours copying over every cell of your table by hand! There are many easier ways to get your results out of Stata. Goes over outreg2, mkcorr, and copying tables. For some reason the sound didn't kick in at the start and I had to cut out the part where I explain that you might need to download outreg2 using ssc install outreg2.
Views: 54988 Nick Huntington-Klein
Logistic Regression Using Excel
 
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Predict who survives the Titanic disaster using Excel. Logistic regression allows us to predict a categorical outcome using categorical and numeric data. For example, we might want to decide which college alumni will agree to make a donation based on their age, gender, graduation date, and prior history of donating. Or we might want to predict whether or not a loan will default based on credit score, purpose of the loan, geographic location, marital status, and income. Logistic regression will allow us to use the information we have to predict the likelihood of the event we're interested in. Linear Regression helps us answer the question, "What value should we expect?" while logistic regression tells us "How likely is it?" Given a set of inputs, a logistic regression equation will return a value between 0 and 1, representing the probability that the event will occur. Based on that probability, we might then choose to either take or not take a particular action. For example, we might decide that if the likelihood that an alumni will donate is below 5%, then we're not going to ask them for a donation. Or if the probability of default on a loan is above 20%, then we might refuse to issue a loan or offer it at a higher interest rate. How we choose the cutoff depends on a cost-benefit analysis. For example, even if there is only a 10% chance of an alumni donating, but the call only takes two minutes and the average donation is 100 dollars, it is probably worthwhile to call.
Views: 195794 Data Analysis Videos
Compare Two Excel Lists to Spot the Differences
 
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Over time you will collect many lists of Excel data. It can be a challenge to compare the contents of one list with the contents in another list. For example, to find out which customers do not exist in another list. In this lesson I demonstrate three techniques that you can use to compare Customer lists: 1) The =MATCH() Function 2) The VLookup() Function 3) Pivot Tables I invite you to visit my website - www.thecompanyrocks.com - to view all of my video lessons.
Views: 672062 Danny Rocks
R Stats: Data Prep and Imputation of Missing Values
 
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This video demonstrates how to prepare data for use with the Naive Bayes classifier and its cross-validation. It focuses primarily on the selection of suitable variables from a large data set and imputation of missing values. The video also explains the use of Spearman rank correlation for ordinal variables, where the traditional Pearson correlation is not applicable. The lesson is quite informal and avoids more complex statistical concepts. The data for this lesson can be obtained from the UCI Machine Learning Repository: * https://archive.ics.uci.edu/ml/datasets/wiki4he The R source code for this video can be found (some small discrepancies are possible): * http://visanalytics.org/youtube-rsrc/r-stats/Demo-B3-Imputing-Missing-Values.r Videos in data analytics and data visualization by Jacob Cybulski, visanalytics.org.
Views: 19005 ironfrown
Probability - Tree Diagrams 1
 
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How to use a tree diagram to calculate combined probabilities of two independent events
Views: 724185 Ron Barrow
Statistical Rethinking - Lecture 20
 
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Lecture 20 - Measurement error, missing data imputation, false-positive science - Statistical Rethinking: A Bayesian Course with R Examples
Views: 2305 Richard McElreath
Machine Learning Bangla Course: Data Processing: Missing Data through Imputing
 
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Download dataset from this link: https://drive.google.com/open?id=1yRTuRPLNpLQRI1zEcq9Gx3N6WTcBCqMP What is Machine Learning? Machine learning is a field of computer science that uses statistical techniques to give computer systems the ability to "learn" (e.g., progressively improve performance on a specific task) with data, without being explicitly programmed. Machine learning is closely related to (and often overlaps with) computational statistics, which also focuses on prediction-making through the use of computers. It has strong ties to mathematical optimization, which delivers methods, theory and application domains to the field. Machine learning is sometimes conflated with data mining, where the latter subfield focuses more on exploratory data analysis and is known as unsupervised learning. You should check this video tutorial to easily download Anaconda Navigator for Python Distribution. https://youtu.be/4v7Uke37QGs First of all, you have to download Anaconda Navigator Distribution for Python. For this go to this link and download for your computer depending on your operating system, Windows, Linux or Mac. https://www.anaconda.com/download/ We have used Python 3.6 Version for our course. So you should download that to cope up with us. The next video: https://www.youtube.com/watch?v=ohampM4H6fY&index=4&list=PLA-CsqNypl-SqtkfwXAK7trT_M2g5yAGe Data Proessing Complete Playlist: https://www.youtube.com/playlist?list=PLA-CsqNypl-SqtkfwXAK7trT_M2g5yAGe The previous video:https://www.youtube.com/watch?v=RaC85Y2kS5Q&list=PLA-CsqNypl-SqtkfwXAK7trT_M2g5yAGe&index=2 1/How can we Master Machine Learning on Python? 2/How can we Have a great intuition of many Machine Learning models? 3/How can we Make accurate predictions? 4/How can we Make powerful analysis? 5/How can we Make robust Machine Learning models? 6/How can we Create strong added value to your business? 7/How do we Use Machine Learning for personal purpose? 8/How can we Handle specific topics like Reinforcement Learning, NLP and Deep Learning? 9/How can we Handle advanced techniques like Dimensionality Reduction? 10/How do we Know which Machine Learning model to choose for each type of problem? 11/How can we Build an army of powerful Machine Learning models and know how to combine them to solve any problem? Subscribe to our channel to get video updates. সাবস্ক্রাইব করুন আমাদের চ্যানেলেঃ https://www.youtube.com/channel/UC50C-xy9PPctJezJcGO8q2g Follow us on Facebook: https://www.facebook.com/Planeter.Bangladesh/ Follow us on Instagram: https://www.instagram.com/planeter.bangladesh Follow us on Twitter: https://www.twitter.com/planeterbd Our Website: https://www.planeterbd.com For More Queries: [email protected] Phone Number: +8801727659044, +8801728697998 #machinelearning #bigdata #ML #DataScience #DataSet #XY #DeepLearning #robotics #রবোটিক্স #প্ল্যনেটার #Planeter #ieeeprotocols #DataProcessing #MissingData #SimpleLinearRegression #MultiplelinearRegression #PolynomialRegression #SupportVectorRegression(SVR) #DecisionTreeRegression #RandomForestRegression #EvaluationRegressionModelsPerformance #MachineLearningClassificatioModels #LogisticRegression #machinelearnigcourse #machinelearningcoursebangla #machinelearningforbeginners #banglamachinelearning #artificialintelligence #machinelearningtutorials #machinelearningcrashcourse #imageprocessing #SpyderIDE #BestBanglaMachineLearningTutorialSeries #ML #MachineLearning
Views: 749 Planeter
SPSS Tutorial.1
 
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SPSS EPIDEMIOLOGY PREVENTIVE MEDICINE MEDICINE BIO-STATISTICS STATISTICS DATA EPIDEMIOLOGICAL STUDIES
Interpolation 01- Newton forward difference formula In Hindi
 
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This video lecture " Interpolation 01- Newton forward difference formula in hindi" will help Engineering and Basic Science students to understand following topic of Engineering-Mathematics: 1. Concept of interpolation and extrapolation 2. Formation of forward difference table 3. statement of Newton forward difference interpolation formula 4. two solved problem soon we will upload next video. For any query and feedback, please write us at: [email protected] OR call us at: +919301197409(Hike number) For latest updates subscribe our channel " Bhagwan Singh Vishwakarma" or join us on Facebook "Maths Bhopal"...
MSPTDA 12: Using Locale in Power Query Power BI: Import & Append Text Files from Different Countries
 
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Download Excel START File: https://people.highline.edu/mgirvin/AllClasses/348/MSPTDA/Content/PowerQuery/012-MSPTDA-LocaleStart.xlsx Download Zipped Folder with Text Files: https://people.highline.edu/mgirvin/AllClasses/348/MSPTDA/Content/PowerQuery/TextFiles012.zip Download Excel FINISHED File: https://people.highline.edu/mgirvin/AllClasses/348/MSPTDA/Content/PowerQuery/012-MSPTDA-LocaleFinished.xlsx Download Power BI Desktop FINISHED File: https://people.highline.edu/mgirvin/AllClasses/348/MSPTDA/Content/PowerQuery/012-MSPTDA-LocaleFinished.pbix Download pdf Notes about Power Query: https://people.highline.edu/mgirvin/AllClasses/348/MSPTDA/Content/PowerQuery/012-MSPTDA-Locale.pdf Comprehensive video about using Locale Settings so that Power Query interprets Dates and Numbers from different parts of the world correctly. In this Video learn about how to use the “Using Locale…” Feature and Regional Settings to import Text Files from Different Countries so that Dates and Numbers in Different Formats can be interpreted correct, and the multiple Text Files and be Appended into a single table. Also see how to change the Locale settings on individual columns. Comprehensive Microsoft Power Tools for Data Analysis Class, BI 348, taught by Mike Girvin, Excel MVP and Highline College Professor. Topics: 1. (00:15) Introduction 2. (00:25) Text Files from Different Countries have Different Date and Number Formats 3. (02:40) Change Regional Settings in Power Query and Power BI Desktop 4. (04:28) Using Locale… Feature on Single Columns to interpret Dates and Numbers Correctly 5. (06:50) Convert ISO Dates to Proper Dates in Power Query 6. (08:04) Power BI Desktop: Import Multiple Text Files with Different Date and Number Formats From Folder and Append. See 1) Create Table in Power BI Desktop, 2) Build Custom Function 3) Import Text Files From Folder and Append 7. (20:30) Summary Assigned Homework: Download pdf file with homework description: https://people.highline.edu/mgirvin/AllClasses/348/MSPTDA/Content/PowerQuery/012-MSPTDA-Homework-Start.docx Zipped Text Files: https://people.highline.edu/mgirvin/AllClasses/348/MSPTDA/Content/PowerQuery/012-TextFilesForHomework.zip Example of Finished Homework in Excel: https://people.highline.edu/mgirvin/AllClasses/348/MSPTDA/Content/PowerQuery/012-HomeworkFinishedExample.xlsx
Views: 7460 ExcelIsFun
SmartPLS New Project, Load and Troubleshoot Data
 
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A demo for how to start a new SmartPLS project, load data, and troubleshoot it. I now have an article published that cites this video. Paul Benjamin Lowry and James Gaskin (2014). "Partial least squares (PLS)structural equation modeling (SEM) for building and testing behavioral causal theory: When to choose it and how to use it," IEEE Transactions on Professional Communication (57:2), pp. 123-146. http://www.kolobkreations.com/PLSIEEETPC2014.pdf
Views: 27810 James Gaskin
How to make pdf of multiple graphs in R studio using for loop
 
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Hello researchers, This video is very useful when you want to make multiple graphs on a single PDF. It looks awesome friends.
Views: 4309 Sarveshwar Inani
Tutorial: Data Analytics with R: Data Preparation
 
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This video shows you some basic steps of preparing your data for data analysis. Specifically, it reviews the following topics: Data Collection: Gathering data; Merging data from multiple sources Data Layout: Laying out data into rows and columns Variables: Understanding different types of variables Nominal Data: Using dummy variables for nominal data Reading Data: Reading data into R Checking Data: Assessing potential data problems Missing Data: Options for dealing with missing data Sampling: Gathering samples from data Data Partitions: Partitioning data into Training and Validation datasets for Supervised Learning
Views: 105 Stephan Sorger
03 SPSS for Beginners - Descriptive Statistics
 
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How to Use SPSS In this third video about SPSS for Beginners, Dr. Daniel shows you three ways to approach descriptive statistics in SPSS. If you want quick and basic descriptives, use the Descriptives command to get the most commonly used statistics. The Frequencies command gives you a wide range of possibilities with the most flexibility to choose exactly what output that you want. When you want maximum output with lots of graphs – or if you want to split the descriptive statistics by a categorical variable (like gender), then use the Explore command. Link to a Google Drive folder with all of the files that I use in the videos including the Bear Handout and StatsClass.sav. As I add new files, they will appear here, as well. https://drive.google.com/drive/folders/1n9aCsq5j4dQ6m_sv62ohDI69aol3rW6Q?usp=sharing
Views: 150863 Research By Design
SPSS: Learning About Your Data
 
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Instructional video how to learn about your data using descriptive statistics in SPSS, statistical analysis and data management software. For more information, visit SSDS at https://ssds.stanford.edu.
JASP/Excel - One-Way Between Subjects ANOVA Example
 
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Lecturer: Dr. Erin M. Buchanan Missouri State University Spring 2017 This video covers one-way between subjects ANOVA. Excel: we cover data screening: accuracy, missing, outliers, normality, linearity, homogeneity, homoscedasticity JASP: Levene's test, how to run the ANOVA, how to analyze post hocs, effect size, write ups Excel: Graphs with error bars GPower: sample size estimation for future studies Lecture materials and assignment available at statstools.com. http://statstools.com/learn/advanced-statistics/
Views: 1005 Statistics of DOOM
01 SPSS for Beginners - How to Use SPSS Introduction
 
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How to Use SPSS This is the first in a series of eight videos that will introduce you to using SPSS for introductory statistics. This series is designed for people with little or no experience with SPSS. You will learn about the SPSS work space, how to navigate between Data View and Variable View, how to create variables, and how to modify properties of variables. Link to a Google Drive folder with all of the files that I use in the videos including the Bear Handout and StatsClass.sav. As I add new files, they will appear here, as well. https://drive.google.com/drive/folders/1n9aCsq5j4dQ6m_sv62ohDI69aol3rW6Q?usp=sharing
Views: 252268 Research By Design
The Geometric Mean
 
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Thanks to all of you who support me on Patreon. You da real mvps! $1 per month helps!! :) https://www.patreon.com/patrickjmt !! The Geometric Mean. In this video, I show how to find the geometric mean of two numbers and show what that number means in relation to the numbers that you started with.
Views: 332193 patrickJMT
Categorical latent variable modeling with longitudinal data, Mplus Topic 6, Part 3
 
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Topic 6. Categorical latent variable modeling with longitudinal data. Recorded presentation at Freie University, Berlin, July 18, 2009. Link to handouts associated with this segment: http://www.statmodel.com/download/Topic6-v7.pdf NOTE: For more information or to engage in discussion about the topics covered in this video, please visit www.statmodel.com.
Views: 120 Mplus
The submission process from the publisher's point of view
 
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Jonathan Patience, Senior Editor at Taylor & Francis, outlines the pathway to publication; encompassing the initial decision, peer review, revision and acceptance. Recorded 13 June 2018 at a MedComms Networking event in Oxford. Produced by NetworkPharma.tv = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = ABSTRACT: Taylor & Francis Group, an Informa business, publishes more than 2,500 journals and over 5,000 new books each year, offering a range of commercial services, including accelerated publishing, supplements, sponsored focus and reprints. The company recently acquired Dove Medical Press, which specialises in the publication of open access journals across the broad spectrum of science, technology and especially medicine. This talk aims to de-mystify the process of manuscript submission to medical journals. From a publisher’s perspective, it covers considerations prior to submission, pre-submission inquiries, the stages your manuscript undergoes after submission, and how to improve your chances of manuscript acceptance. Prior to submission, it is important to check the journal’s author guidelines and aims and scope. The manuscript should be on scope for the journal, have a clear objective explaining what it adds to the literature, and include all requested sections and funding information. At peer review, the most common criticisms we receive for industry-funded papers are that they have a marketing tone, so it is important to be careful of the language used, letting data speak for itself. Other common criticisms include missing references, flawed statistical analysis, lack of transparency or results being over or under-emphasised, and missing p-values in support of claims of significance. To speed up the decision and avoid rejection post-peer review, make sure to provide clear, point-by-point responses to all reviewers’ comments, or rationale where you feel no change is needed. Adding new data, long delays during revision, unclear responses and unaddressed comments can all delay the editorial decision. Written by Jonathan Patience, Senior Editor at Taylor & Francis = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = We are building a library of free webcasts, like this one, for the global MedComms Community and others at http://www.networkpharma.tv and we’d welcome your suggestions for new topics and speakers. Full details of this MedComms Networking event are at http://medcommsnetworking.com/event_130618.html Jonathan’s presentation (PDF format) is at http://medcommsnetworking.com/presentations/patience_130618.pdf Jonathan’s Linkedin page is at https://www.linkedin.com/in/jonathan-patience-679b1224/ More about Taylor & Francis can be found at http://taylorandfrancis.com/ Filming and technical direction by Mario Crispino, Freelance Cameraman & Editor Editorial support by Penny Gray, Freelance Medical Writer [For the avoidance of doubt: this video is intended to be freely accessible to all. Please feel free to share and use however you like. Cheers, Peter Llewellyn, Director NetworkPharma Ltd and Founder of the MedComms Networking Community activity at http://www.medcommsnetworking.com]
Views: 315 MedComms
NIPS 2013 Tutorial - Causes and Counterfactuals: Concepts, Principles and Tools
 
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Judea Pearl and Elias Bareinboim Slides: http://www.cs.ucla.edu/~eb/nips-dec2013-pearl-bareinboim-tutorial-full.pdf The traditional aim of machine learning methods is to infer meaningful features of an underlying probability distribution from samples drawn of that distribution. With the help of such features, one can infer associations of interest and predict or classify yet unobserved samples. Causal analysis goes one step further; it aims at inferring features of the data-generating process, that is, of the invariant strategy by which Nature assigns values to the variables in the distribution. Process features enable us to predict, not merely relationships governed by the underlying distribution, but also how that distribution would CHANGE when conditions are altered, say, by deliberate interventions or by spontaneous transformations. We will review concepts, principles, and mathematical tools that were found useful in reasoning about causal and counterfactual relations, and will demonstrate their applications in several data-intensive sciences. These include questions of confounding control, policy analysis, misspecification tests, mediation, heterogeneity, selection bias, missing data, and the integration of findings from diverse studies. The following topics will be emphasized: 1. The 3-layer causal hierarchy: association, intervention and counterfactuals. http://ftp.cs.ucla.edu/pub/stat_ser/r350.pdf 2. What mathematics can tell us about "transfer learning" or "generalizing across domains" http://ftp.cs.ucla.edu/pub/stat_ser/r372.pdf http://ftp.cs.ucla.edu/pub/stat_ser/r387.pdf 3. What causal analysis tells us about recovery from selection bias and missing data. http://ftp.cs.ucla.edu/pub/stat_ser/r381.pdf http://ftp.cs.ucla.edu/pub/stat_ser/r410.pdf 4. The Mediation Formula, and what it tells us about "How nature works" http://ftp.cs.ucla.edu/pub/stat_ser/r379.pdf
Views: 2919 NIPS