Search results “Statistical analysis with missing data”
Multiple Imputation: A Righteous Approach to Handling Missing Data
It will sound like cheating, but it isn't. It's so righteous dude! Multiple imputation (MI) is an effective and responsible way to handle data which is missing at random (MAR). You'll find out what that means too... Please join Elaine Eisenbeisz, Owner and Principal of Omega Statistics, as she presents an overview of MI concepts. (Original Air Date: August, 2014)
Views: 9171 Omega Statistics
How to Use SPSS-Replacing Missing Data Using Multiple Imputation (Regression Method)
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.
R - Data Screening 2 Missing Data
Recorded: Fall 2015 Lecturer: Dr. Erin M. Buchanan This video covers how to check your data for missing data, how much missing data you should consider replacing, what types of data to replace, and how to replace data with the mice package through multiple imputation. Lecture materials and assignment available at statstools.com. http://statstools.com/learn/graduate-statistics/ Used in the following courses: Graduate Statistics
Views: 6437 Statistics of DOOM
Replace Missing Values - Expectation-Maximization - SPSS (part 1)
Learn how to use the expectation-maximization (EM) technique in SPSS to estimate missing values . This is one of the best methods to impute missing values in SPSS.
Views: 148533 how2stats
Little's Missing Completely at Random (MCAR) Test - SPSS
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: 97914 how2stats
R Tutorial 10: How to handle missing values and attributes
In this video, we learn how to handle missing values in R: how to find if there are any missing values and remove them. Also, I show how how to work with attributes that can be attached to any R object. About the series: Difficulty level: Beginner This is a brand new tutorial series to learn R Programming Language for Data science / Statistics. I walk you through a structured approach to learn the language so the concepts falls in place perfectly and you gain a clear understanding. This series is filled with end of the lesson exercises and practice exercises to get you hand-on and have fun learning R. http://rstatistics.net http://r-statistics.co
Views: 25758 LearnR
Making Statistics Accessible: Approaches to Missing Data
Sponsored by the Center for Interdisciplinary Research on AIDS (CIRA) at Yale University's Interdisciplinary Research Methods Core. The presenters are Russell Barbour, Ph.D., CIRA, and Eugenia Buta, Ph.D., CIRA and The Yale Center of Analytical Studies (YCAS).
Views: 4938 YaleUniversity
Replacing missing data: Factual analysis method
Get the Full course here:https://www.udemy.com/r-analytics/?couponCode=YOUTUBESPECIAL Missing data is a common thing in data scientist life. Today we will explore one of the methods for dealing with missing values which will help you for data management and data preparation - Factual Analysis Method. Use this special coupon to get a YouTube-only discount on the full course:https://www.udemy.com/r-analytics/?couponCode=YOUTUBESPECIAL
Views: 2712 SuperDataScience
Statistical Methods for Bias Adjustment, "Analysis of Missing Data" Professor Takahiro Hoshino
Title:Statistical Methods for Bias Adjustment, "Analysis of Missing Data" Professor Takahiro Hoshino, Department of Economics, Keio University My focus research topics are statistical causal inference and its applications. You may not be familiar with the term "causal inference," so let me give you an example. Let's say we want to find out which is the better way to treat a certain illness: medication or surgery. As a result of investigation, of the two groups, one medicated and one having had surgery, is it reasonable to conclude that surgery is the better approach to treatment in cases where it offers a far higher survival rate? If only patients in good overall condition with no complications can undergo surgery, while many patients in poor condition with complications cannot, it may seem that the difference between the survival rates for medication and surgery may be due to the difference in the baseline condition of the patient. If a patient who has undergone surgery could have also been cured by medication, perhaps medication would be a better approach to treatment than placing a heavy burden on the body with surgery. 【True effects cannot be understood by simple comparison】---------------------------------- The same can also be said of verification of the effects of costly TV advertisements (TV Ads). In fact, a comparison of two groups, one which has seen a TV Ad for a game application and one which has not, reveals what first seems to be the opposite effect to the one intended, where the group that has seen the TV Ad used the application for less time and opened the application less times than the group that has not seen the TV Ad. However, the group that has not seen the TV Ad spends more time using smartphones than watching TV, so actually, the result is natural. Really, the proper evaluation index is "how much application usage time would be decreased if the group that saw the TV Ad had not seen it." "Usage time had not seen it" is a missing value, known by the term "potential outcome,". Therefore analysis needs to be performed, factoring in this so-called potential outcome. Looking at almost all problems in society, true effects cannot be obtained by simple comparison in areas such as evaluation of policies in economics, evaluation of marketing measures and the effects of teaching methods. My research on related to the development and application of methodology for the performance of correct policy evaluation and statistical causal effect received the Japan Society for the 13th Promotion of Science Prize and Japan Statistical Society Research Achievement Award. 【The analysis of missing data that handles data that cannot be observed】------------------- Statistical causal inference is one of important fields in missing data analysis that deals with unobservable data that we considered earlier, such as potential "usage time". Recently, decreasing accuracy of government statistics has become problematic and this has led to calls for development of new indices that combine data from government surveys with big data acquired by companies. However, because big data is missing data which is biased in that it contains only "in-house purchasing and behavior logs" of a company's own customers, I am working with the Statistics Bureau of the Ministry of Internal Affairs and Communications on the development of new indices that incorporate big data with bias corrected. No matter how much big data is acquired, because bias that exists in the data may yield incorrect results, The development and application of missing data analysis and statistical data fusion methods are becoming ever-more important in fields such as academic research, government decision-making and corporate marketing practices. http://www001.upp.so-net.ne.jp/bayesian/Eindex.html
Webinar: Approaches to Missing Data--The Good, the Bad, and the Unthinkable
Published on 12/1/2016 Presented on 12/1/2016 Presented by Karen Grace-Martin You’ve probably heard about many different approaches to dealing with missing data, and you’ve probably gotten different opinions about which one you should use. In this webinar, you’ll get an overview of: • the three types of missing data, and how they affect the approach to take • the common approach that is generally worse than any other • the easy, common, seemingly bad approach that often isn’t so bad, and the situations when it doesn’t work • the two approaches that give unbiased results, one that is very easy to implement, but only works in limited situations, and one that is harder to implement well, but works with any statistical analysis.
Views: 165 ASQStatsDivision
Missing value analysis in SPSS - part 1
This video demonstrates missing value analysis in SPSS
Views: 58609 Murtaza Haider
R Statistical: Omitting Rows with "NA" (Missing data)
Sometimes you want to get rid of all observations that have missing data points. Here's how.
Views: 15192 Jared Waxman
Tutorial: Data Cleaning
0:06 – Impossible Values and Response Sets 3:43 – Missing Data 7:45 – Outliers 11:33 – Normality
Views: 17172 Meredith Rocchi
Tipping Point Analysis in Multiple Imputation for Binary Missing Data
Our Senior Statistician Niccoló explores the Tipping Point Analysis in Multiple Imputation for Binary Missing Data in Clinical Trials https://www.quanticate.com/blog/tipping-point-analysis-in-multiple-imputation-for-missing-data
Views: 798 Quanticate
Missing Data Analysis : Multiple Imputation in R
Paper: Advanced Data Analysis Module: Missing Data Analysis : Multiple Imputation in R Content Writer: Souvik Bandyopadhyay
Views: 19477 Vidya-mitra
path analysis with AMOS (structural equation modeling program) when you have complete data
This video provides a general overview of how to utilize AMOS structural equation modeling program to carry out path analysis on a complete dataset (no missing values) The data for this video can be downloaded from here: https://drive.google.com/open?id=1L-94ToRQqaD1oPaxS0mvSbdZHv-GqXBH 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: 61428 Mike Crowson
Missing values in R
Views: 205 Jonatan Lindh
R Stats: Data Prep and Imputation of Missing Values
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: 15432 ironfrown
How to Clean SPSS Data
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: 117130 CPG Orlando
Statistics: Finding Missing Data
Using mean to find missing data points
Views: 383 Michelle Kretsch
Handling missing data in MPLUS, video 2 (using FIML estimation)
This is the second video in my series on strategies for dealing with missing data in the context of SEM when using MPLUS. In this video I demonstrate how to invoke Full-information maximum likelihood (FIML) estimation when testing a path analysis model. A copy of the Word document containing the syntax I review in the video can be downloaded here: https://drive.google.com/open?id=1DZuXKViEfHCjBIQ60l1aGZh3-G1lSuQT A copy of the original data file (from video 1) can be downloaded here: https://drive.google.com/open?id=1j93yGxqGO8x9DOYt1z7qIADnSot5dX3H A copy of the .CSV file from the video can be downloaded here: https://drive.google.com/open?id=1vMsGqSqZ0bq7NA9ic6PzaNtOKaDJYasm IMPORTANT: YOU'LL NEED TO CHANGE THE PATH IN THE DATA: FILE IS LINE IN ORDER TO ENSURE MPLUS WILL READ THE .CSV FILE AM PROVIDING YOU. SEE VIDEO 1 IN THIS SERIES (https://youtube.com/watch?v=tDs8_rcJ5Mk&feature=youtu.be) TO OBTAIN MORE DETAILS ON CREATING .CSV FILES AND READING THEM INTO MPLUS) The data in this video is based off the raw data that is publicly available from the American National Election Study 2016: http://www.electionstudies.org/studypages/anes_timeseries_2016/anes_timeseries_2016.htm 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: 509 Mike Crowson
How to Use SPSS: Little's Missing Completely at Random (MCAR) Test
Test to determine if missing data is missing in a random or non-random pattern. Assists in deciding which technique may be most appropriate for replacing missing data.
SPSS: Missing Numerical Data
Instructional video on how missing numeric data is handled in SPSS, statistical analysis and data management software. For more information, visit SSDS at https://ssds.stanford.edu.
R tutorial: Missing data and coarse classification
Learn more about credit risk modeling in R: https://www.datacamp.com/courses/introduction-to-credit-risk-modeling-in-r Now, we have removed the observation containing a bivariate outlier for age and annual income from the data set. What we did not discuss before is that there are missing inputs (or NA's, which stand for not available) for two variables: employment length and interest rate. In this video we will demonstrate some methods for handling missing data on the employment length variable. You'll practice this newly gained knowledge yourself on the variable interest rate. First, you want to know how many inputs are missing, as this will affect what you do with them. A simple way of finding out is with the function summary(). If you do this for employment length, you will see that there are 809 NA's. There are generally three ways to treat missing inputs: delete them, replace them, or keep them. We will illustrate these methods on employment length. When deleting, you can either delete the observations where missing inputs are detected, or delete an entire variable. Typically, you would only want to delete observations if there is just a small number of missing inputs, and would only consider deleting an entire variable when many cases are missing. Using this construction with which() and is.na(), the rows with missing inputs are deleted in the new data set loan_data_no_NA. To delete the entire variable employment length, you simply set the employment length variable in the loan data equal to NULL. Here, we save the result to a copy of the data set called loan_data_delete_employ. Making a copy of your original data before deleting things can be a good way to avoid losing information, but may be costly if working with very large data sets. Second, when replacing a variable, common practice is to replace missing values with the median of the values that are actually observed. This is called median imputation. Last, you can keep the missing values, since in some cases, the fact that a value is missing is important information. Unfortunately, keeping the NAs as such is not always possible, as some methods will automatically delete rows with NAs because they cannot deal with them. So how can we keep NAs? A popular solution is coarse classification. Using this method, you basically put a continuous variable into so-called bins. Let's start off making a new variable emp_cat, which will be the variable replacing emp_length. The employment length in our data set ranges from 0 to 62 years. We will put employment length into bins of roughly 15 years, with groups 0 to 15, 15 to 30, 30 to 45, 45 plus, and a "missing” group, representing the NAs. Let's see how this changes our data. Let's look at the plot of this new factor variable. It appears that the bin '0-15' contains a very high proportion of the cases, so it might seem more reasonable to look at bins of different ranges but with similar frequencies, as shown here. You can get these results by trial and error for different bin ranges, or by using quantile functions to know exactly where the breaks should be to get more balanced bins. Before trying all of this in R yourself, let me finish the video with a couple of remarks. First, all the methods for missing data handling can also be applied to outliers. If you think an outlier is wrong, you can treat it as NA and use any of the methods we have discussed in this chapter. Second, you may have noticed I only talked about missingness for continuous variables in this chapter. What about factor variables? Here's the basic approach. For categorical variables, deletion works in the exact same way as for continuous variables, deleting either observations or entire variables. When we wish to replace a missing factor variable, this is done by assigning it to the modal class, which is the class with the highest frequency. Keeping NAs for a categorical variable is done by including a missing category. Now, let's try some of these methods yourself!
Views: 4431 DataCamp
Statistical Methods for Missing Data Final With objectives
Paper: Advanced Data Analytic Techniques Module: Statistical Methods for Missing Data Content Writer: Prof Kalyan Das
Views: 160 Vidya-mitra
Frequency analysis of Rainfall/Flood data | Hydrology | CE
Real Life Application Frequency analysis plays an important role in hydraulic engineering applications such as those concerned with floods, for example in construction of Dams it is necessary to find out the probability of occurring an extreme flood. Explanation To understand the concept with clarity it’s important to understand two things:- 1. Probability 2. Return Period With the basic concept of probability we know that probability of any event is given as Favorable cases by total number of cases. Return Period also called as Recurrence Interval or Frequency (T) is the time period on an average after which peak flood discharge is likely to be equaled or exceeded. In order to find the Return Period Plotting Position method is used, in this method the given data is arranged in decreasing order of magnitude and accordingly rank(m) is assigned to each value. The return period for any value is then calculated by following three methods:- California Formula Weibull’s Formula Hazens Formula Then the probability that a particular value is equal or exceeded is then given by- Probability=1/T, where T is the Return Period. We make use of Binomial Event for calculating probability, Binomial even is an event which has only two possible outcomes and is suited for this analysis also as either flood can occur or flood cannot occur. We calculate the probability that a particular event (having probability p) happens exactly r times out of n trials. The probability of Reliability and Risk is important for us considering the design of hydraulic structures. Reliability: This is the probability that a particular flood magnitude is never equaled or exceeded in the design life of structure. Risk: This is the probability that a particular flood magnitude is equaled or exceeded at least once in the design life of structure. THE GATE ACADEMY- Blogs https://goo.gl/nE8qwu https://goo.gl/Ktn8XS THE GATE ACADEMY provide comprehensive and rigorous coaching for the GATE exams. Our student-centred guidance focuses on the strengths and weaknesses of each student. This has enabled us to achieve a proven track record of GATE toppers from our institute. THE GATE ACADEMY appreciates diversity in requirements and hence have tailor-made digital & distance learning courses for addressing these different needs. For more information, please write back to us at [email protected] Call us at: 080- 61766222
Learn JMP 11 (Part 4) - Handling Messy and Missing Data
See robust fitting methods that reduce the influence of outliers in most platforms; imputation for handling missing data in JMP Pro PLS platform; informative missing option in Fit Model and Partition.
Views: 5546 JMPSoftwareFromSAS
How to Clean Up Raw Data in Excel
Al Chen (https://twitter.com/bigal123) is an Excel aficionado. Watch as he shows you how to clean up raw data for processing in Excel. This is also a great resource for data visualization projects. Subscribe to Skillshare’s Youtube Channel: http://skl.sh/yt-subscribe Check out all of Skillshare’s classes: http://skl.sh/youtube Like Skillshare on Facebook: https://www.facebook.com/skillshare Follow Skillshare on Twitter: https://twitter.com/skillshare Follow Skillshare on Instagram: http://instagram.com/Skillshare
Views: 72166 Skillshare
Missing Data Analysis, Mplus Short Course Topic 11, Part 9
Mplus Short Course Topic 11: Regression and Mediation Analysis Part 9 - Missing Data Analysis Link to handouts associated with this segment (slides 151-170): http://www.statmodel.com/download/Aug16_JH_Slides.zip NOTE: For more information or to engage in discussion about the topics covered in this video, please visit www.statmodel.com.
Views: 275 Mplus
Replacing Missing Values in SPSS with the Series Mean
This video demonstrates how to replace missing values with the series mean in SPSS. Recoding missing values using the “Recode into Same Variables” function is reviewed.
Views: 50751 Dr. Todd Grande
SEM Boot Camp 2018: Basic Statistics
This is a video stream of the morning session where I cover some basics, such as normality, outliers, missing data, regression, anova, t-test, correlation
Views: 1880 James Gaskin
Missing Value - kNN imputation in R
This video discusses about how to do kNN imputation in R for both numerical and categorical variables.
Views: 19795 Gourab Nath
Introduction to Missing Data Handling with Mplus
On March 1, 2017, Dr. James Peugh from Cincinnati Children’s Hospital Medical Center presented this 90-minute talk at the University of Kentucky on how to handle missing data in Mplus. This was the first presentation in the Spring 2017 Applied Quantitative and Psychometric Series (AQPS). This presentation focused on how to handle missing data for Four SEM-Based Analyses (Categorical CFA with Covariate (MIMIC Model), Moderated Mediation (MacArthur Method), SEM with a Latent Variable Interaction Term, and Multilevel ANCOVA SEM. Visit http://education.uky.edu/edp/apslab/events/#MissingData to download the PowerPoint Handout and Mplus Data Files for this talk.
Views: 864 Michael Toland
Statistical Rethinking - Lecture 20
Lecture 20 - Measurement error, missing data imputation, false-positive science - Statistical Rethinking: A Bayesian Course with R Examples
Views: 2236 Richard McElreath
SAS Enterprise Miner Tip: Imputing Missing Values
https://support.sas.com/edu/schedules.html?id=857&ctry=US Jeff Thompson, a statistical training specialist with SAS Education, provides an overview of the predictive modeling portion of the SAS training course "Applied Analytics Using SAS Enterprise Miner." Thompson also provides a tip on the imputation of missing values. To learn more about the SAS training course "Applied Analytics Using SAS Enterprise Miner," visit https://support.sas.com/edu/schedules.html?id=857&ctry=US
Views: 32953 SAS Software
Statistical Data Analysis in Python, SciPy2013 Tutorial, Part 1 of 4
Presenter: Christopher Fonnesbeck Description This tutorial will introduce the use of Python for statistical data analysis, using data stored as Pandas DataFrame objects. Much of the work involved in analyzing data resides in importing, cleaning and transforming data in preparation for analysis. Therefore, the first half of the course is comprised of a 2-part overview of basic and intermediate Pandas usage that will show how to effectively manipulate datasets in memory. This includes tasks like indexing, alignment, join/merge methods, date/time types, and handling of missing data. Next, we will cover plotting and visualization using Pandas and Matplotlib, focusing on creating effective visual representations of your data, while avoiding common pitfalls. Finally, participants will be introduced to methods for statistical data modeling using some of the advanced functions in Numpy, Scipy and Pandas. This will include fitting your data to probability distributions, estimating relationships among variables using linear and non-linear models, and a brief introduction to Bayesian methods. Each section of the tutorial will involve hands-on manipulation and analysis of sample datasets, to be provided to attendees in advance. The target audience for the tutorial includes all new Python users, though we recommend that users also attend the NumPy and IPython session in the introductory track. Tutorial GitHub repo: https://github.com/fonnesbeck/statistical-analysis-python-tutorial Outline Introduction to Pandas (45 min) Importing data Series and DataFrame objects Indexing, data selection and subsetting Hierarchical indexing Reading and writing files Date/time types String conversion Missing data Data summarization Data Wrangling with Pandas (45 min) Indexing, selection and subsetting Reshaping DataFrame objects Pivoting Alignment Data aggregation and GroupBy operations Merging and joining DataFrame objects Plotting and Visualization (45 min) Time series plots Grouped plots Scatterplots Histograms Visualization pro tips Statistical Data Modeling (45 min) Fitting data to probability distributions Linear models Spline models Time series analysis Bayesian models Required Packages Python 2.7 or higher (including Python 3) pandas 0.11.1 or higher, and its dependencies NumPy 1.6.1 or higher matplotlib 1.0.0 or higher pytz IPython 0.12 or higher pyzmq tornado
Views: 71524 Enthought
Excel - Time Series Forecasting - Part 1 of 3
Part 2: http://www.youtube.com/watch?v=5C012eMSeIU&feature=youtu.be Part 3: http://www.youtube.com/watch?v=kcfiu-f88JQ&feature=youtu.be This is Part 1 of a 3 part "Time Series Forecasting in Excel" video lecture. Be sure to watch Parts 2 and 3 upon completing Part 1. The links for 2 and 3 are in the video as well as above.
Views: 774122 Jalayer Academy
How to Calculate missing values via interpolation
In this session I show you how you calculate a missing value for an indicator. Sometimes you don't have a number in between a time series. For instance you have a number for 2010 and 2012 but you don't have a number for the year 2011. You do this with interpolation. This session will teach you how to interpolate. You can use the data in a graph, in a policy research note etc. once you have interpolated it.
Views: 106935 Policy in Paradise TV
Individual participant data meta-analysis, a look into statistical methods
VIDEO COMPLETO SU http://didattica.dctv.unipd.it/biostats.php Seminario del 21 marzo 2018 dal titolo: "Individual participant data meta-analysis, a look into statistical me thods with focus to the management of systematically missing data". Saluti introduttivi Prof. Dario Gregori DCTV RELATORE DOTT. MATTEO ROTA Dipartimento di Scienze Cliniche e di Comunità, Università degli studi di Milano Individual participant data meta-analyses (IPD-MAs) are considered to be the gold standard approach in evidence synthesis. IPD-MAs allow access to raw data from each participant, data checking, verification and centralized recoding of data according to common definitions. Two competing statistical approaches - one-stage and two-stage randomeffects models - have shown to produce similar results. But how can we deal with the presence of systematically missing variables?
Import Data and Analyze with MATLAB
Data are frequently available in text file format. This tutorial reviews how to import data, create trends and custom calculations, and then export the data in text file format from MATLAB. Source code is available from http://apmonitor.com/che263/uploads/Main/matlab_data_analysis.zip
Views: 354066 APMonitor.com
SPSS: How To Enter, Code, And Analyze Multiple Choice Data
0:08 Multiple choice item vs. Likert scale item 1:33 Multiple choice questions with one correct answer 3:27 Multiple choice questions with multiple correct answers 6:03 "Multiple response set" in SPSS 7:52 How to pronounce "Likert"? This video discusses how to best enter and code multiple choice type data in SPSS as well as how to analyze such data using descriptive stats and multiple response sets. Please LIKE this video if you enjoyed it. Otherwise, there is a thumb-down button, too... :P ▶ Please SUBSCRIBE to see new videos (almost) every week! ◀ ▼MY OTHER CHANNEL (MUSIC AND PIANO TUTORIALS)▼ https://www.youtube.com/ranywayz ▼MY SOCIAL MEDIA PAGES▼ https://www.facebook.com/ranywayz https://nl.linkedin.com/in/ranywayz https://www.twitter.com/ranywayz Animations are made with Sparkol. Music files retrieved from YouTube Audio Library. All images used in this video are free stock images or are available in the public domain. The views expressed in this video are my own and do not necessarily reflect the organizations with which I am affiliated. #SPSS #Statistics #DataEntry
Views: 44462 Ranywayz Random
Data Screening in SPSS- Part 1: Explore
A quick tutorial on Explore in SPSS: screening for missing data, normality, and minumum and maximum values
Views: 38273 Siobhan O'Toole
Filling missing data using remote sensing Altimetry: Better than Multiple Imputation :)
Filling missing data using remote sensing Altimetry: Better than Multiple Imputation :) Multiple Imputation: https://www.youtube.com/watch?v=87qtBnZ788c Altimeter data: http://www.legos.obs-mip.fr/soa/hydrologie/hydroweb/Page_2.html Date conversion: http://sopac.ucsd.edu/convertDate.shtml?indate=2003-02-19 Datum conversion: http://geographiclib.sourceforge.net/cgi-bin/GeoidEval?input=4.7652+6.0956&option=Reset
Importing , Checking and Working with Data in R | R Tutorial 1.7 | MarinStatsLectures
Importing Data, Checking the Imported Data and Working With Data in R; Dataset: https://goo.gl/tJj5XG More Statistics and R Programming Tutorials: https://goo.gl/4vDQzT How to import a datasets into R , How to make sure data was imported correctly into R and How to begin to work with the imported data in R. ▶︎We will learn to use read.table function (which reads a file in table format and creates a data frame from it, with cases corresponding to lines and variables to fields in the file), and some of the arguments such as header argument and sep argument. ▶︎We will learn to use file.choose function to choose a file interactively ▶︎We will discuss how to use Menu options in RStudio to import data into R ▶︎and how to check the imported data to make sure it was imported correctly into R using the dim function to retrieve dimension of an object and let you know the number of rows and columns of the imported data, the head function in R (head() function), which returns the first or last parts of a vector, matrix, table, data frame and will let you see the first several rows of the data, the tail function in R (tail() function) to see the last several rows of the data in R, the double square brackets in R to subset data (brackets lets you select or subset data from a vector, matrix, array, list or data frame) , and the names function in R to get the names of an object in R. ▶︎▶︎ Download the dataset here: https://statslectures.com/r-stats-datasets ▶︎▶︎Watch More ▶︎Export Data from R (CSV , TXT and other formats): https://bit.ly/2PWS84w ▶︎Graphs and Descriptive Statistics in R: https://bit.ly/2PkTneg ▶︎Probability Distributions in R: https://bit.ly/2AT3wpI ▶︎Bivariate Analysis in R: https://bit.ly/2SXvcRi ▶︎Linear Regression in R: https://bit.ly/1iytAtm ▶︎Intro to Statistics Course: https://bit.ly/2SQOxDH ◼︎ Topics in the video: 0:00:07 How to read a dataset into R using read.table function and save it as an object 0:00:27 How to access the help menu in R 0:01:02 How to let R know that the first row of our data is headers by using header argument 0:01:14 How to let R know how the observations are separated by using sep argument 0:02:03 How to specify the path to the file using file.choose function 0:03:15 How to use Menu options in R Studio to import data into R 0:05:23 How to prepare the Excel data for importing into R 0:06:15 How to know the dimensions (the number of rows and columns) of the data in R using the dim function 0:06:35 How to see the first several rows of the data using the head command in R 0:06:45 How to see the last several rows of the data in R using the tail function 0:07:18 How to check if the data was read correctly into R using square brackets and subsetting data 0:08:21 How to check the variable names in R using the names function 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) The #RTutorial is created by #marinstatslectures to support the statistics course (SPPH400 #IntroductoryStatistics) 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!
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