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Learn Basic statistics for Business Analytics
 
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Business Analytics and Data Science are almost same concept. For both we need to learn Statistics. In this video I tried to create value on most used statistical methods for Data Science or Business Analytics for Statistical model Building. Statistics is the study of the collection, analysis, interpretation, presentation, and organization of data. In applying statistics any can handle a scientific, industrial, or societal problem. I value your time and effort that is why I have capture almost 20 statically concept in this video. Learn Basic statistics for Business Analytics Here I have capture how to learn Mean, how to learn Mode, How to learn median, Concept of Sleekness, Concept of Kurtosis, learn Variables, concept of Standard deviation, Concept of Covariance, Concept of correlation, Concept of regression, How to read regression formula, how to read regression graph, Concept of Intercept, Concept of slope coefficient, Concept of Random Error, Different types of regression Analysis, Concept ANOVA (Analysis of Variance), How to read ANOVA table, How to learn R square (Interpreted R square), Concept of Adjusted R Square, Concept of F test, Concept of Information Value, Concept of WOE, Concept of Variable inflation Factors. Learn Basic statistics for Business Analytics By this video you can Start Learn statistics for Data Science and Business analytics easily and effectively. These statistics are useful when at the time of running linear regression, Logistic regression statistics models. For Statistical Data Exploration you may need to see Meager of central tendency and Data Spread in Statistics. By Understanding Mean, Mode, Median, Sleekness, Kurtosis, Variance, Standard deviation. Learn Basic statistics for Business Analytics To understand statistical relationship between variables you can use Covariance, Correlation coefficient, Regression , ANOVA (Analysis of Variance) . Learn Basic statistics for Business Analytics To understand Strength of stastical relationship between variables you can use R square, Adjusted R square, F test. If you want to understand variable importance in your stastical model you can use Information value (IV) and Weight of evidence (WOE) Concept. Information value and Weight of evidence mostly used in Logistic Regression Analysis. Learn Basic statistics for Business Analytics Variable inflation factors (VIF) is used for understanding, It is the stastical method to understand variable importance. What is the importance of this variable statically in the Regression model? By VIF we check Correlation between variable. Learn Basic statistics for Business Analytics At last I have explained when to use ANOVA, When to Use Linear regression and when to use Logistic regression. Learn Basic statistics for Business Analytics Thank you So much for watching this video, Hope I can add some value in your Journey as a Statistician, Business Analytics professional and Data Scientist professional. Blogger : http://koustav.analyticsanalysis.busi... google plus: https://plus.google.com/u/0/115750715 facebook link: https://www.facebook.com/koustav.biswas.31945?ref=bookmarks website: https://www.analyticsanalysisbusiness.com
Tutorial: Statistics and Data Analysis
 
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Ethan Meyers, Hampshire College - MIT BMM Summer Course 2018
BroadE: Statistical methods of data analysis
 
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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.
Views: 7285 Broad Institute
Choosing which statistical test to use - statistics help.
 
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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: 786797 Dr Nic's Maths and Stats
Introduction to Statistics..What are they? And, How Do I Know Which One to Choose?
 
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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: 240186 The Doctoral Journey
Data Analysis:  Clustering and Classification (Lec. 1, part 1)
 
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Supervised and unsupervised learning algorithms
Views: 71056 Nathan Kutz
Introduction to Statistical Methods of Analysis (Geography)
 
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Subject:Geography Paper: Quantitative techniques in geography
Views: 2867 Vidya-mitra
Your Survey Closed, Now What? Quantitative Analysis Basics
 
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This webinar provides an overview of basic quantitative analysis, including the types of variables and statistical tests commonly used by Student Affairs professionals. Specifically discussed are the basics of Chi-squared tests, t-tests, and ANOVAs, including how to read an SPSS output for each of these tests.
Views: 21582 CSSLOhioStateU
R programming for beginners – statistic with R (t-test and linear regression) and dplyr and ggplot
 
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R programming for beginners - This video is an introduction to R programming. I have another channel dedicated to R teaching: https://www.youtube.com/c/rprogramming101 In this video I provide a tutorial on some statistical analysis (specifically using the t-test and linear regression). I also demonstrate how to use dplyr and ggplot to do data manipulation and data visualisation. Its R programming for beginners really and is filled with graphics, quantitative analysis and some explanations as to how statistics work. If you’re a statistician, into data science or perhaps someone learning bio-stats and thinking about learning to use R for quantitative analysis, then you’ll find this video useful. Importantly, R is free. If you learn R programming you’ll have it for life. This video was sponsored by the University of Edinburgh. Find out more about their programmes at http://edin.ac/2pTfis2 This channel focusses on global health and public health - so please consider subscribing if you’re someone wanting to make the world a better place – I’d love to you join this community. I have videos on epidemiology, study design, ethics and many more.
Choosing a Statistical Test
 
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In common health care research, some hypothesis tests are more common than others. How do you decide, between the common tests, which one is the right one for your research? Thank you to the Statistical Learning Center for their excellent video on the same topic. https://www.youtube.com/rulIUAN0U3w
Views: 396822 Erich Goldstein
Statistics For Data Science | Data Science Tutorial | Simplilearn
 
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Statistics is primarily an applied branch of mathematics, which tries to make sense of observations in the real world. Statistics is generally regarded as one of the pillars of data science. Data Science Certification Training - R Programming: https://www.simplilearn.com/big-data-and-analytics/data-scientist-certification-sas-r-excel-training?utm_campaign=Data-Statistics-Lv0xcdeXaGU&utm_medium=SC&utm_source=youtube What are the course objectives? This course will enable you to: 1. Gain a foundational understanding of business analytics 2. Install R, R-studio, and workspace setup. You will also learn about the various R packages 3. Master the R programming and understand how various statements are executed in R 4. Gain an in-depth understanding of data structure used in R and learn to import/export data in R 5. Define, understand and use the various apply functions and DPLYP functions 6. Understand and use the various graphics in R for data visualization 7. Gain a basic understanding of the various statistical concepts 8. Understand and use hypothesis testing method to drive business decisions 9. Understand and use linear, non-linear regression models, and classification techniques for data analysis 10. Learn and use the various association rules and Apriori algorithm 11. Learn and use clustering methods including K-means, DBSCAN, and hierarchical clustering Who should take this course? There is an increasing demand for skilled data scientists across all industries which makes this course suited for participants at all levels of experience. We recommend this Data Science training especially for the following professionals: IT professionals looking for a career switch into data science and analytics Software developers looking for a career switch into data science and analytics Professionals working in data and business analytics Graduates looking to build a career in analytics and data science Anyone with a genuine interest in the data science field Experienced professionals who would like to harness data science in their fields Who should take this course? There is an increasing demand for skilled data scientists across all industries which makes this course suited for participants at all levels of experience. We recommend this Data Science training especially for the following professionals: 1. IT professionals looking for a career switch into data science and analytics 2. Software developers looking for a career switch into data science and analytics 3. Professionals working in data and business analytics 4. Graduates looking to build a career in analytics and data science 5. Anyone with a genuine interest in the data science field 6. Experienced professionals who would like to harness data science in their fields For more updates on courses and tips follow us on: - Facebook : https://www.facebook.com/Simplilearn - Twitter: https://twitter.com/simplilearn Get the android app: http://bit.ly/1WlVo4u Get the iOS app: http://apple.co/1HIO5J0
Views: 39654 Simplilearn
Introduction to Advanced Statistical Techniques and Its Applications | Data Analysis -Great Learning
 
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#AdvancedStatisticalTechniques | Learn more about our analytics programs: http://bit.ly/2EjCWZh This tutorial helps you understand the following advanced statistical techniques and its applications. - Analysis of Variance (ANOVA) - Linear Regression Analysis - Principal Component Analysis (PCA) - Factor Analysis #AdvancedStatiscs #Tutorial #GreatLearning #ANOVA #PCS #FactorAnalysis ----------------------------------------- Great Learning is an online and hybrid learning company that offers high-quality, impactful, and industry-relevant programs to working professionals like you. These programs help you master data-driven decision-making regardless of the sector or function you work in and accelerate your career in high growth areas like Big Data and Analytics. PG Program in Business Analytics (PGP-BABI): 12-month program with classroom training on weekends + online learning covering analytics tools and techniques and their application in business. PG Program in Big Data Analytics (PGP-BDA): 12-month program with classroom training on weekends + online learning covering big data analytics tools and techniques, machine learning with hands-on exposure to big data tools such as Hadoop, Python, Spark, Pig etc. PGP-Data Science & Engineering: 6-month weekend and classroom program allowing participants enables participants in learning conceptual building of techniques and foundations required for analytics roles. PG Program in Cloud Computing: 6-month online program in Cloud Computing & Architecture for technology professionals who want their careers to be cloud-ready. Business Analytics Certificate Program (BACP): 6-month online data analytics certification enabling participants to gain in-depth and hands-on knowledge of analytical concepts. Watch the video to know ''Why is there so much hype around 'Artificial Intelligence'?'' https://www.youtube.com/watch?v=VcxpBYAAnGM What is Machine Learning & its Applications? https://www.youtube.com/watch?v=NsoHx0AJs-U Do you know what the three pillars of Data Science? Here explaining all about thepillars of Data Science: https://www.youtube.com/watch?v=xtI2Qa4v670 Want to know more about the careers in Data Science & Engineering? Watch this video: https://www.youtube.com/watch?v=0Ue_plL55jU For more interesting tutorials, don't forget to Subscribe our channel: https://www.youtube.com/user/beaconelearning?sub_confirmation=1 Learn More at: https://www.greatlearning.in/ For more updates on courses and tips follow us on: Google Plus: https://plus.google.com/u/0/108438615307549697541 Facebook: https://www.facebook.com/GreatLearningOfficial/ LinkedIn: https://www.linkedin.com/company/great-learning/ - Follow our Blog: https://www.greatlearning.in/blog/?utm_source=Youtube Great Learning has collaborated with the University of Texas at Austin for the PG Program in Artificial Intelligence and Machine Learning and with UT Austin McCombs School of Business for the PG Program in Analytics and Business Intelligence.
Views: 13990 Great Learning
Statistical Analysis of Data - Principles of Measurement - Electronic Instrumentation & Measurement
 
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Statistical Analysis of Data Video Lecture of Principles of Measurement Chapter in Subject Electronic Instrumentation and Measurement for Electrical, Electronics, EXTC & Instrumentation Engineering Students. Watch Previous Videos of Chapter Principles of Measurements:- 1) Sources of Errors in Measurement - Electronic Instrumentation and Measurement - https://www.youtube.com/watch?v=mXlYEplJfM4 2) Methods of Minimizing Errors - Principles of Measurement - Electronic Instrumentation & Measurement - https://www.youtube.com/watch?v=WCI6sBNi_ow Watch Next Videos of Chapter Principles of Measurements:- 1) Types of Errors in Measurement System - Problem 1 - Principles of Measurement - Electronic Instrumentation and Measurement - https://www.youtube.com/watch?v=irlFIPfC9qs 2) Types of Errors in Measurement System - Problem 2 - Principles of Measurement - Electronic Instrumentation and Measurement - https://www.youtube.com/watch?v=aq7QFhFoaBY Access the Complete Playlist of Subject Electronic Instrumentation and Measurement:- http://gg.gg/Electronic-Instrumentation-and-Measurement Access the Complete Playlist of Chapter Principles of Measurements:- http://gg.gg/Principles-of-Measurement Subscribe to Ekeeda Channel to access more videos:- http://gg.gg/Subscribe-Now #ElectronicInstrumentationandMeasurement #ElectronicInstrumentation #ElectronicMeasurement #ElectronicInstrumentsandMeasurement #ElectronicMeasurementVideoTutorials #ElectronicMeasurementTutorials #ElectronicInstrumentationVideoLectures #ElectronicInstrumentationOnlineLectures #ElectronicInstrumentationandMeasurementlectures Thanks For Watching. You can follow and Like us in following social media. Website - http://ekeeda.com Parent Channel - https://www.youtube.com/c/ekeeda Facebook - https://www.facebook.com/ekeeda Twitter - https://twitter.com/Ekeeda_Video LinkedIn- https://www.linkedin.com/company-beta/13222723/ Instgram - https://www.instagram.com/ekeeda_/ Pinterest - https://in.pinterest.com/ekeedavideo You can reach us at [email protected] Happy Learning : )
Views: 2270 Ekeeda
Basic Statistics & Quantitative Analysis I
 
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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: 36294 Campus Labs
Introduction to Data Science with R - Data Analysis Part 1
 
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Part 1 in a in-depth hands-on tutorial introducing the viewer to Data Science with R programming. The video provides end-to-end data science training, including data exploration, data wrangling, data analysis, data visualization, feature engineering, and machine learning. All source code from videos are available from GitHub. NOTE - The data for the competition has changed since this video series was started. You can find the applicable .CSVs in the GitHub repo. Blog: http://daveondata.com GitHub: https://github.com/EasyD/IntroToDataScience I do Data Science training as a Bootcamp: https://goo.gl/OhIHSc
Views: 1018380 David Langer
Statistics and Data Analysis I: Introduction
 
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Niccole Pamphilis, a Lecturer in Quantitative Social Science at the University of Glasgow, describes her ICPSR Summer Program workshop "Statistics and Data Analysis I: Introduction." For more information about this workshop, visit http://www.icpsr.umich.edu/icpsrweb/sumprog/courses/0006 For more information about the ICPSR Summer Program, visit icpsr.umich.edu/sumprog
Introduction to Multivariate Analysis
 
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Paper: Multivariate Analysis Module name: Introduction toMultivariate Analysis Content Writer: Souvik Bandyopadhyay
Views: 66852 Vidya-mitra
Metodi Nikolov - Statistical Analysis of Financial Data [COS497-Spring'14]
 
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(April 2, 2014) Metodi Nikolov, Senior Quantitative Analyst at FinAnalytica, talks about the probability models that a given financial data series follows. The speaker gives information about the kinds of outcomes and answers that can be gotten from the data and how statistics and analysis are performed on it. This lecture was organized for Professor Dimitar Christozov's Data Mining class. More about this talk on our website: http://www.aubg.edu/talks/finanalytica-statistical-analysis-of-financial-data Find us elsewhere on the web: WEBSITE: http://www.aubg.edu/talks FACEBOOK: http://www.facebook.com/AUBGTalks TWITTER: http://twitter.com/AUBGTalks GOOGLE+: http://plus.google.com/113278525844733479649/ Find out more about our awesome university, the American University in Bulgaria: http://www.aubg.edu
Views: 2762 AUBGTalks
Excel 2013 Statistical Analysis #01: Using Excel Efficiently For Statistical Analysis (100 Examples)
 
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Download File: https://people.highline.edu/mgirvin/AllClasses/210Excel2013/Ch00/Excel2013StatisticsChapter00.xlsx All Excel Files for All Video files: http://people.highline.edu/mgirvin/excelisfun.htm. Intro To Excel: Store Raw Data, Data Types, Data Analysis, Formulas, PivotTables, Charts, Keyboards, Number Formatting, Data Analysis & More: (00:08) Introduction to class (00:49) Cells, Worksheets, Workbooks, File Names (02:54) Navigating Worksheets & Workbook (03:58) Navigation Keys (04:15) Keyboard move Active Sheet (05:40) Ribbon Tabs (06:25) Add buttons to Quick Access Tool Bar (07:40) What Excel does: Store Raw Data, Make Calculations, Data Analysis & Charting (08:55) Introduction to Data Analysis (10:37) Data Types in Excel: Text, Numbers, Boolean, Errors, Empty Cells (11:16) Keyboard Enter puts content in cell and move selected cell down (13:00) Data Type DEFAULT Alignments (13:11) First Formula. Entering Cell References in formulas (13:35) Keyboard Ctrl + Enter puts content in cell & keep cell selected (14:45) Why we don’t override DEFAULT Alignments (15:05) Keyboard Ctrl + Z is Undo (17:05) Proper Data Sets & Raw Data (24:21) How To Enter Data & Data Labels (24:21) Stylistic Formatting (26:35) AVERAGE Function (27:31) Format Formulas Differently than Raw Data (28:30) Keyboard Ctrl + C is Copy. Keyboard Ctrl + V is Paste (29:59) Use Eraser remove Formatting Only (29:19) Keyboard Ctrl + B adds Bold (29:57) Excel’s Golden Rule (31:43) Keyboard F2 puts cell in Edit Mode (32:01) Violating Excel’s Golden Rule (34:12) Arrow Keys to put cell references in formulas (35:40) Full Discussion about Formulas & Formulas Elements (37:22) SUM function Keyboard is Alt + = (38:22) Aggregate functions (38:50) Why we use ranges in functions (40:56) COUNT & COUNTA functions (42:47) Edit Formula & change cell references (44:18) Absolute & Relative Cell References (45:52) Use Delete Key, Not Right-click Delete (46:40) Fill Handle & Angry Rabbit to copy formula (47:41) Keyboard F4 Locks Cell Reference (make Absolute) (49:45) Keyboard Tab puts content in Cell and move selected Cell to right (50:55) Order of Operation error (52:17) Range Finder to find formula errors (52:34) Lock Cell Reference after you put cell in Edit Mode (53:58) Quickly copy an edited formula down a column (53:07) F2 key in last cell to find formula errors (54:15) Fix incorrect range in function (54:55) SQRT function & Fractional Exponents (57:20) STDEV.P function (58:10) Navigate Large Data Sets (58:48) Keyboard Ctrl + Arrow jumps to bottom of data set (59:42) Keyboard Ctrl + Shift + Arrow selects to bottom of data set (Current Range) (01:01:41) Keyboard Shift + Enter puts content in Cell and move selected Cell up (01:02:55) Counting with conditions or criteria: COUNTIFS function (01:03:43) Keyboard Ctrl + Backspace jumps back to Active Cell (01:05:31) Counting between an upper & lower limit with COUNTIFS (01:07:36) COUNTIFS copied down column (01:10:08) Joining Comparative Operator with Cell Reference in formula (01:12:50) Data Analysis features in Excel (01:13:44) Sorting (01:16:59) Filtering (01:20:39) Introduction to PivotTables (01:23:39) Create PivotTable dialog box (01:24:33) Dragging & dropping Fields to create PivotTable (01:25:31) Dragging Field to Row area creates a Unique List (01:26:17) Outline/Tabular Layout (01:27:00) Value Field Settings dialog to change: Number Formatting, Function, Name (01:28:12) 2nd & 3rd PivotTable examples (01:31:23) What is a Cross Tabulated Report? (01:33:04) Create Cross Tabulated Report w PivotTable (01:35:05) Show PivotTable Field List (01:36:48) How to Pivot the Report (01:37:50) Summarize Survey Data with PivotTable. (01:38:34) Keyboard Alt, N, V opens PivotTable dialog box (01:41:38) PivotTable with 3 calculations: COUNT, MAX & MIN (01:43:25) Count & Count Number calculations in a PivotTable (01:45:30) Excel 2013 Charts to Visually Articulate Quantitative Data (01:47:00) #1 Rule for Charts: No Chart Junk! (01:47:30) Explain chart types: Column, Bar, Pie, Line and X-Y Scatter Chart (01:51:34) Create Column Chart using Recommended Chart feature (01:53:00) Remove Field Buttons from Pivot Chart (01:54:10) Chart Formatting Task Pane (01:54:45) Vary Fill Color by point (01:55:15) Format Axis with Numbers by Formatting Source Data in PivotTable (01:56:02) Add Data Labels to Chart (01:57:28) Copy Chart & Create Bar Chart (01:57:48) Change Chart Type (01:58:15) Change Gap Width. (01:59:17) Create Pie Chart (01:59:23) Do NOT use 3-D Pie (01:59:42) Add % Data Labels to Pie Chart (02:00:25) Create Line Chart From PivotTable (02:01:20) Link Chart Tile to Cell (02:02:20) Move a Chart (02:02:33) Create an X-Y Scatter Chart (02:03:35) Add Axis Labels (02:05:27) Number Formatting to help save time (02:07:24) Number Formatting is a Façade (02:10:27) General Number Format (02:10:52) Percentage Number Formatting (02:14:03) Don’t Multiply Relative Frequency by 100 (02:17:27) Formula for % Change & End Amount
Views: 431483 ExcelIsFun
MATLAB Tools for Scientists: Introduction to Statistical Analysis
 
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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: 19220 MATLAB
Microsoft Excel data analysis tool for statistics mean, median, hypothesis, regression
 
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This video covers a few topics using the data analysis tool. After this video you should be able to: a) Find and use data analysis on excel to calculate statistics b) Calculate the mean, median, mode, standard deviation, range and coefficient variation on a variable set of data in excel. c) Conduct a confidence interval in excel. d) Complete a T-test in excel to help complete a hypothesis test. e) Conduct a linear regression analysis output from excel and create a scatter diagram.
Views: 110669 Me ee
Research Methods - Interpreting Inferential Statistics
 
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This A Level / IB Psychology revision video for Research Methods looks at interpreting inferential statistics.
Views: 24834 tutor2u
Statistics 03: Types of statistical models
 
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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: 41957 Christoph Scherber
Fundamentals of Qualitative Research Methods: Data Analysis (Module 5)
 
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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: 172080 YaleUniversity
How to Use SPSS: Choosing the Appropriate Statistical Test
 
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A step-by-step approach for choosing an appropriate statistcal test for data analysis.
Introduction to Statistical Analysis
 
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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: 32886 Bharatendra Rai
Basic Statistics and Data Analysis Tools
 
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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: 12574 Holger Koss
Statistical Data Analysis in Python, SciPy2013 Tutorial, Part 1 of 4
 
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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: 73944 Enthought
Analysing Questionnaires
 
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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/
Webinar 8: Methods of data analysis: Advanced and emerging methods of statistical analysis
 
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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: 65 Think Data
Statistical Analysis And Business Applications | Data Science With Python Tutorial
 
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The Data Science with Python course explores different Python libraries and tools that help you tackle each stage of Data Analytics. Python is a general purpose multi-paradigm programming language for data science that has gained wide popularity-because of its syntax simplicity and operability on different eco-systems. This Python course can help programmers play with data by allowing them to do anything they need with data - data munging, data wrangling, website scraping, web application building, data engineering and more. Python language makes it easy for programmers to write maintainable, large scale robust code The course starts off with a brief introduction to Data Science, statistical concepts pertaining to Data Analytics, and a few basic concepts of Python programming. It then goes on to cover in-depth content for libraries such as NumPy, Pandas, SciPy, scikit-learn, and Matplotlib. The course also tackles important activities such as web scraping and Python integration with Hadoop MapReduce and Spark. Python for Data Science Certification Training: http://www.simplilearn.com/big-data-and-analytics/python-for-data-science-training?utm_campaign=Introduction-Python-Data-Science-ZH13ZXh1_-w&utm_medium=SC&utm_source=youtube For more updates on courses and tips follow us on: - Facebook : https://www.facebook.com/Simplilearn - Twitter: https://twitter.com/simplilearn Get the android app: http://bit.ly/1WlVo4u Get the iOS app: http://apple.co/1HIO5J0
Views: 8949 Simplilearn
Statistical Methods for Bias Adjustment, "Analysis of Missing Data" Professor Takahiro Hoshino
 
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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
Analysis of Variance (ANOVA)
 
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A description of the concepts behind Analysis of Variance. There is an interactive visualization here: http://demonstrations.wolfram.com/VisualANOVA/ but I have not tried it, and this: http://rpsychologist.com/d3-one-way-anova has another visualization
Views: 558343 J David Eisenberg
An Introduction to Linear Regression Analysis
 
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Tutorial introducing the idea of linear regression analysis and the least square method. Typically used in a statistics class. Playlist on Linear Regression http://www.youtube.com/course?list=ECF596A4043DBEAE9C Like us on: http://www.facebook.com/PartyMoreStudyLess Created by David Longstreet, Professor of the Universe, MyBookSucks http://www.linkedin.com/in/davidlongstreet
Views: 793644 statisticsfun
Introduction to Quantitative Data Analysis and Statistics
 
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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: 4393 David Russell