Home
Search results “Data analysis and statistical methods”

17:59

01:02:58
Copyright Broad Institute, 2013. All rights reserved. The presentation above was filmed during the 2012 Proteomics Workshop, part of the BroadE Workshop series. The Proteomics Workshop provides a working knowledge of what proteomics is and how it can accelerate biologists' and clinicians' research. The focus of the workshop is on the most important technologies and experimental approaches used in modern mass spectrometry (MS)-based proteomics.

09:33
Seven different statistical tests and a process by which you can decide which to use. The tests are: Test for a mean, test for a proportion, difference of proportions, difference of two means - independent samples, difference of two means - paired, chi-squared test for independence and regression. This video draws together videos about Helen, her brother, Luke and the choconutties. There is a sequel to give more practice choosing and illustrations of the different types of test with hypotheses.
Views: 715025 Dr Nic's Maths and Stats

39:54
This tutorial provides an overview of statistical analyses in the social sciences. It distinguishes between descriptive and inferential statistics, discusses factors for choosing an analysis procedure, and identifies the difference between parametric and nonparametric procedures.
Views: 222932 The Doctoral Journey

01:05:31
Ethan Meyers, Hampshire College - MIT BMM Summer Course 2018

24:49
Views: 19316 S Manikandan

09:44
In this lecture, I show which types of statistical models should be used when; the most important decision concerns the explanatory variables: When these are continuous, the analysis type will be regression; however, when these are factors, then we will conduct an analysis of variance. Overall, I show that both analyses are special examples of what is called a Linear Statistical Model. I briefly introduce linear statistical models. Later lectures will cover this in greater detail.
Views: 37959 Christoph Scherber

45:32
Views: 845 Ekeeda

02:22:43
Views: 411436 ExcelIsFun

05:39
The process of doing statistical analysis follows a clearly defined sequence of steps whether the analysis is being done in a formal setting like a medical lab or informally like you would find in a corporate environment. This lecture gives a brief overview of the process.
Views: 47486 White Crane Education

20:17
Views: 29950 Simplilearn

10:05
Views: 7236 morgankenneth12

18:32
Views: 71667 David Russell

12:27
In this lecture, I provide a very basic introduction to quantitative data analysis and statistics. We begin by defining what "data" is, what a dataset looks like, and software tools for analyzing data.
Views: 3764 David Russell

53:55
Data download: http://www.windengineering.byg.dtu.dk/download The video introduces basic methods in statistics and three Matlab scripts that can be used to analyse measured data for example from wind tunnel testing. The scripts allow basic signal processing (detrending and digital filtering), assessment of probability and spectral densities (Matlab signal processing toolbox required!), the collection of maximum and minimum extremes from sub-series for extreme value analysis, correlation between two time series and the calculation of the joint probability density function. The video is used for education at the Technical University of Denmark (DTU) in course 11374 "Seismic and Wind Engineering" and for preparation of wind tunnel testing in civil engineering. For further information see www.windengineering.byg.dtu.dk or contact the author under [email protected]
Views: 12374 Holger Koss

02:03
“Fundamentals of Engineering Statistical Analysis” is a free online course on Janux that is open to anyone. Learn more at http://janux.ou.edu. Created by the University of Oklahoma, Janux is an interactive learning community that gives learners direct connections to courses, education resources, faculty, and each other. Janux courses are freely available or may be taken for college credit by enrolled OU students. Dr. Kash Barker is an Assistant Professor in the School of Industrial Engineering. Video produced by NextThought (http://nextthought.com). Copyright © 2000-2014 The Board of Regents of the University of Oklahoma, All Rights Reserved.
Views: 1975 Janux

01:39:12
Views: 2260 Florence HEP

06:02
www.ozanozcan.us
Views: 260397 ozanteaching

17:12
Qualitative research is a strategy for systematic collection, organization, and interpretation of phenomena that are difficult to measure quantitatively. Dr. Leslie Curry leads us through six modules covering essential topics in qualitative research, including what it is qualitative research and how to use the most common methods, in-depth interviews and focus groups. These videos are intended to enhance participants' capacity to conceptualize, design, and conduct qualitative research in the health sciences. Welcome to Module 5. Bradley EH, Curry LA, Devers K. Qualitative data analysis for health services research: Developing taxonomy, themes, and theory. Health Services Research, 2007; 42(4):1758-1772. Learn more about Dr. Leslie Curry http://publichealth.yale.edu/people/leslie_curry.profile Learn more about the Yale Global Health Leadership Institute http://ghli.yale.edu
Views: 153962 YaleUniversity

06:27
Views: 13018 Great Learning

30:52
Subject:Geography Paper: Quantitative techniques in geography
Views: 2077 Vidya-mitra

24:05
Views: 7454 Simplilearn

14:06
Use simple data analysis techniques in SPSS to analyze survey questions.
Views: 812787 Claus Ebster

13:14
Part 1
Views: 172712 Teresa Johnson

01:11:27
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: 72052 Enthought

08:18

01:23:02
Webinar 8: Methods of data analysis: Advanced and emerging methods of statistical analysis Tues 20th September 2016 Short talks within the webinar include: ---------------------------------------------------------------------------------- “Using more and more variables in statistical analysis” by Paul Lambert (est 20 mins) "Data Analysis Skills" by Alasdair Rutherford “The idea of multilevel modelling” by Paul Lambert (est. 10 mins) “Estimating and communicating uncertainty” by Alasdair Rutherford (est 20 mins) . The webinar includes a mix of presentation sessions and opportunities for online discussions, questions, clarifications, and information provision. ---------------------------------------------------------------------------------- Find details of our other webinars at http://thinkdata.org.uk/events/CSDPWebinars/ More information on the research, capacity building, and collaboration activities of the Think Data network can be found at http://www.thinkdata.org.uk The Scottish Civil Society Data Partnership project is run by the Universities of Stirling and St Andrews and the Scottish Council for Voluntary Organisations (SCVO). http://www.stir.ac.uk http://www.st-andrews.ac.uk http://www.scvo.org.uk Funded by the Economic and Social Research Council (ESRC) http://www.esrc.ac.uk/ ---------------------------------------------------------------------------------- Music: http://www.bensound.com/royalty-free-music
Views: 51 Think Data

18:36
Paper: Multivariate Analysis Module name: Introduction toMultivariate Analysis Content Writer: Souvik Bandyopadhyay
Views: 55139 Vidya-mitra

00:16
Views: 14 Stănescu

05:18
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: 687563 statisticsfun

04:16
Views: 13254 The Audiopedia

24:57
Includes application examples, scales of measurement (nominal, ordinal, interval & ratio), qualitative versus quantitative data, cross-sectional versus time-series data, experimental versus observational data, and descriptive statistics versus statistical inference.
Views: 28977 Bharatendra Rai

01:00:37
Through real-world examples, webinar participants learn strategies for choosing appropriate outcome measures, methods for analysis and randomization, and sample sizes as well as tips for collecting the right data to answer your scientific questions.
Views: 8774 RhoInc1984

55:06
This session will provide information regarding descriptive statistics that are often used when reviewing assessment data. We will cover the statistics available in the Baseline reporting site and we will use example situations to identify which statistics should be used to answer the questions being asked. We will also provide an overview regarding levels of measurement that can help determine what types of statistics you are able to run on your data. - See more at: http://www2.campuslabs.com/support/training/basic-statistics-quantitative-analysis-i-5/#sthash.FDO5HA6i.dpuf
Views: 34682 Campus Labs

15:22
This video is the first in a series of six which cover best practice for analyzing spectra with multivariate data analysis. In this edition we introduce multivariate data analysis, or chemometrics, and why these powerful tools are especially useful for analyzing spectral data. The video gives examples of typical applications, discusses the benefits of Multivariate analysis over Univariate analysis, and gives an explanation of some important multivariate methods. The video concludes with a demonstration of spectral data being analyzed.
Views: 28407 Camo Analytics

00:27
Views: 17 Marie Dunning

01:00
Views: 26 Louise Whitmire

47:10
A step-by-step approach for choosing an appropriate statistcal test for data analysis.

15:49
R programming for beginners - This video is an introduction to R programming in which 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.

01:10

06:51
Let's go on a journey through univariate analysis and learn about descriptive statistics in research!
Views: 46193 ChrisFlipp

01:04:01
What can text analysis tell us about society? Corpora of news, books, and social media encode human beliefs and culture. But it is impossible for a researcher to read all of today's rapidly growing text archives. My research develops statistical text analysis methods that measure social phenomena from textual content, especially in news and social media data. For example: How do changes to public opinion appear in microblogs? What topics get censored in the Chinese Internet? What character archetypes recur in movie plots? How do geography and ethnicity affect the diffusion of new language? In order to answer these questions effectively, we must apply and develop scientific methods in statistics, computation, and linguistics. In this talk I will illustrate these methods in a project that analyzes events in international politics. Political scientists are interested in studying international relations through *event data*: time series records of who did what to whom, as described in news articles. To address this event extraction problem, we develop an unsupervised Bayesian model of semantic event classes, which learns the verbs and textual descriptions that correspond to types of diplomatic and military interactions between countries. The model uses dynamic logistic normal priors to drive the learning of semantic classes; but unlike a topic model, it leverages deeper linguistic analysis of syntactic argument structure. Using a corpus of several million news articles over 15 years, we quantitatively evaluate how well its event types match ones defined by experts in previous work, and how well its inferences about countries correspond to real-world conflict. The method also supports exploratory analysis; for example, of the recent history of Israeli-Palestinian relations.
Views: 1143 Microsoft Research

08:32
Views: 4255 Ranywayz Random

00:12

00:15
Views: 5 Michael 2

47:12
Multivariate statistical techniques are the application of statistics to simultaneous observations and can include the analysis of more than one outcome (dependent) variable. Good multivariate analysis starts with exploratory and graphical analyses to reveal potential relations in the data and to highlight potential outliers. First, this presentation will discuss how to extend univariate and bivariate methods for graphical analysis to multivariate data, as well as methods unique to multivariate data. Second, multivariate outlier detection will be presented. Third, there will be a brief discussion of multivariate statistical analysis methods, such as multiple regression, principal component analysis, and cluster analysis, including examples and suggestions as to when one might want to use these techniques.

09:52
This video is part of the University of Southampton, Southampton Education School, Digital Media Resources http://www.southampton.ac.uk/education http://www.southampton.ac.uk/~sesvideo/

54:53
Free MATLAB Trial: https://goo.gl/yXuXnS Request a Quote: https://goo.gl/wNKDSg Contact Us: https://goo.gl/RjJAkE Learn more about MATLAB: https://goo.gl/8QV7ZZ Learn more about Simulink: https://goo.gl/nqnbLe ------------------------------------------------------------------------- Researchers and scientists have to commonly process, visualize and analyze large amounts of data to extract patterns, identify trends and relationships between variables, prove hypothesis, etc. A variety of statistical techniques are used in this data mining and analysis process. Using a realistic data from a clinical study, we will provide an overview of the statistical analysis and visualization capabilities in the MATLAB product family. Highlights include: • Data management and organization • Data filtering and visualization • Descriptive statistics • Hypothesis testing and ANOVA • Regression analysis
Views: 14807 MATLAB

03:36
This statistical analysis overview explains descriptive and inferential statistics. Watch more at http://www.lynda.com/Excel-2007-tutorials/business-statistics/71213-2.html?utm_medium=viral&utm_source=youtube&utm_campaign=videoupload-71213-0101 This specific tutorial is just a single movie from chapter one of the Excel 2007: Business Statistics course presented by lynda.com author Curt Frye. The complete Excel 2007: Business Statistics course has a total duration of 4 hours and 19 minutes and covers formulas and functions for calculating averages and standard deviations, charts and graphs for summarizing data, and the Analysis ToolPak add-in for even greater insights into data Excel 2007: Business Statistics table of contents: Introduction 1. Introducing Statistics 2. Learning Useful Excel Techniques 3. Summarizing Data Using Tables and Graphics 4. Describing Data Using Numerical Methods 5. Using Probability Distributions 6. Sampling Values from a Population 7. Testing Hypotheses 8. Using Linear and Multiple Regression Conclusion