Search results “Statistical methods and data analysis”
Tutorial: Statistics and Data Analysis
Ethan Meyers, Hampshire College - MIT BMM Summer Course 2018
Learn Basic statistics for Business Analytics
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
Statistics For Data Science | Data Science Tutorial | Simplilearn
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: 43819 Simplilearn
Introduction to Statistics..What are they? And, How Do I Know Which One to Choose?
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: 247387 The Doctoral Journey
Choosing which statistical test to use - statistics help.
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: 813346 Dr Nic's Maths and Stats
BroadE: Statistical methods of data analysis
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: 7382 Broad Institute
Introduction to Advanced Statistical Techniques and Its Applications | Data Analysis -Great Learning
#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: 14626 Great Learning
Statistics intro: Mean, median, and mode | Data and statistics | 6th grade | Khan Academy
This is a fantastic intro to the basics of statistics. Our focus here is to help you understand the core concepts of arithmetic mean, median, and mode. Practice this lesson yourself on KhanAcademy.org right now: https://www.khanacademy.org/math/cc-sixth-grade-math/cc-6th-data-statistics/mean-and-median/e/calculating-the-mean?utm_source=YT&utm_medium=Desc&utm_campaign=6thgrade Watch the next lesson: https://www.khanacademy.org/math/cc-sixth-grade-math/cc-6th-data-statistics/mean-and-median/v/mean-median-and-mode?utm_source=YT&utm_medium=Desc&utm_campaign=6thgrade Missed the previous lesson? https://www.khanacademy.org/math/cc-sixth-grade-math/cc-6th-data-statistics/histograms/v/interpreting-histograms?utm_source=YT&utm_medium=Desc&utm_campaign=6thgrade Grade 6th on Khan Academy: By the 6th grade, you're becoming a sophisticated mathemagician. You'll be able to add, subtract, multiply, and divide any non-negative numbers (including decimals and fractions) that any grumpy ogre throws at you. Mind-blowing ideas like exponents (you saw these briefly in the 5th grade), ratios, percents, negative numbers, and variable expressions will start being in your comfort zone. Most importantly, the algebraic side of mathematics is a whole new kind of fun! And if that is not enough, we are going to continue with our understanding of ideas like the coordinate plane (from 5th grade) and area while beginning to derive meaning from data! (Content was selected for this grade level based on a typical curriculum in the United States.) About Khan Academy: Khan Academy is a nonprofit with a mission to provide a free, world-class education for anyone, anywhere. We believe learners of all ages should have unlimited access to free educational content they can master at their own pace. We use intelligent software, deep data analytics and intuitive user interfaces to help students and teachers around the world. Our resources cover preschool through early college education, including math, biology, chemistry, physics, economics, finance, history, grammar and more. We offer free personalized SAT test prep in partnership with the test developer, the College Board. Khan Academy has been translated into dozens of languages, and 100 million people use our platform worldwide every year. For more information, visit www.khanacademy.org, join us on Facebook or follow us on Twitter at @khanacademy. And remember, you can learn anything. For free. For everyone. Forever. #YouCanLearnAnything Subscribe to Khan Academy‰Ûªs 6th grade channel: https://www.youtube.com/channel/UCnif494Ay2S-PuYlDVrOwYQ?sub_confirmation=1 Subscribe to Khan Academy: https://www.youtube.com/subscription_center?add_user=khanacademy
Views: 1949507 Khan Academy
Statistics full Course for Beginner | Statistics for Data Science
In this comprehensive #statistics course you will learn about fundamental concept of statistics which is beginner friendly. This statistics course will walk you through everything you need to know about #statistics for #MachineLearning. The following topic of statistics has been discussed extensively in this course: Author of this videos: Monika Wahi Visit YouTube Channel: https://www.youtube.com/channel/UCCHcm7rOjf7Ruf2GA2Qnxow *********************** Topics Covered: - What is statistics (0:00) - Sampling - Experimental design - Frequency histogram and Distribution - Frequency table and stem and leaf - Time Series, Bar and Pie Graphs - Measures of central tendency - Measure of variation - Scatter diagrams and linear correlation - Linear regression and coefficient - Normal distribution and empirical rule - Z-score and probabilities - Sampling Distributions and the Central limit - Estimating mean when sigma is known ******************************************* Join our community and stay up to date with computer science ******************** Join our FB Group: https://www.facebook.com/groups/cslesson/ Like our FB Page: https://www.facebook.com/cslesson/ Visit Website : https://datasciencedata.com/ ****************************************** statistics for machine learning book pdf statistics for machine learning coursera advanced statistics for machine learning statistics for machine learning packt pdf statistics for machine learning udemy statistics for machine learning tutorial statistics for machine learning online course statistical methods for machine learning pdf
Views: 189578 My CS
Introduction to Quantitative Data Analysis and Statistics
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: 4627 David Russell
Choosing a Statistical Test
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: 413313 Erich Goldstein
Data analysis methods summary
How to perform a multivariate data analysis? What are the questions to answer? How to handle missing values?
Views: 5983 François Husson
Excel 2013 Statistical Analysis #01: Using Excel Efficiently For Statistical Analysis (100 Examples)
Download File: https://people.highline.edu/mgirvin/AllClasses/210Excel2013/Ch00/Excel2013StatisticsChapter00.xlsx 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: 439889 ExcelIsFun
Introduction to Statistical Methods of Analysis (Geography)
Subject:Geography Paper: Quantitative techniques in geography
Views: 3217 Vidya-mitra
Statistical Analysis of Data - Principles of Measurement - Electronic Instrumentation & Measurement
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: 3039 Ekeeda
Fundamentals of Qualitative Research Methods: Data Analysis (Module 5)
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: 178489 YaleUniversity
Data Collection: Understanding the Types of Data.
Data falls into several categories. Each type has some pros and cons, and is best suited for specific needs. Learn more in this short video from our Data Collection DVD available at http://www.velaction.com/data-collection-lean-training-on-dvd/.
Views: 158888 VelactionVideos
Licia Verde: Statistical techniques for data analysis in Cosmology. Lecture 1
Essential Cosmology for the Next Generation 2011 was a winter school and research meeting organized by the Berkeley Center for Cosmological Physics and the Instituto Avanzado de Cosmologia in Puerto Vallarta, Mexico from January 10-12, 2011. It was sponsored in part by the Research Corporation for Science Advancement and the US Department of Energy, Office of Science.
Views: 1720 Berkeley Lab
An Introduction to Statistical Methods and Data Analysis Available 2010 Titles Enhance
I created this video with the YouTube Video Editor (https://www.youtube.com/editor)
Views: 36 Elvira Fleming
Introduction to Multivariate Analysis
Paper: Multivariate Analysis Module name: Introduction toMultivariate Analysis Content Writer: Souvik Bandyopadhyay
Views: 71057 Vidya-mitra
Basic Statistics and Data Analysis Tools
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: 12637 Holger Koss
Excel 2013 Statistical Analysis #8: Frequency Distributions, Histograms, Skew, Quantitative Variable
Download file: https://people.highline.edu/mgirvin/AllClasses/210Excel2013/Ch02/Excel2013StatisticsChapter02.xlsx Topics in this video: 1. (00:09) Overview of Frequency Distributions for Quantitative Variable 2. (02:02) Create Frequency Distribution with PivotTable for Grade Data where NUMBERS ARE DECIMALS (important distinction for grouping feature in a PivotTable) 3. (03:08) Grouping Feature in a PivotTable for creating Classes or Categories for a Decimal Quantitative Variable. Class that are created are 0-10, 10-20, 20-30, etc. Extensive Discussion about how to create classes or categories that are NOT Ambiguous. 4. (05:03) Upper Limit for Class/Category is Not Included when the numbers are Decimals. 5. (05:58) Aggregate Function for Number Values defaults to Count when you have Grouped Numbers in the Row area of the PivotTable. 6. (06:32) Double Click PivotTable to Extract Records that match the criteria from the Row area of the PivotTable 7. (09:16) Use Find and Replace feature to create non-ambiguous labels in a Grouped Decimal Number PivotTable. 8. (10:20) Create Histogram for Quantitative Variable (Grouped Numbers) for Grade Data. This Histogram has Frequencies at the top of each column and the gap width is zero. The colors for each column are different. 9. (13:13) Create Frequency Distribution with PivotTable for Grade Data where numbers are WHOLE NUMBERS (important distinction for grouping feature in a PivotTable) 10. (14:33) Methods for determining Number of Classes and Class Width for a Quantitative Variable 11. (18:19) When grouping Whole Numbers in a PivotTable the classes that are created are not ambiguous. We get classes like: 16-22, 23-29, 30-36. Etc. 12. (20:07) Create Histogram for Quantitative Variable (Grouped Numbers) for Age Data. This Histogram has Frequencies in the vertical axis and the gap width is zero. The colors for each column are the same. 13. (22:00) Discussion about Skew, Histogram shape and Histogram distribution of column heights. 14. (25:37) Relative Frequency and Percent Frequency Distribution built with a PivotTable based on Age Data that is shown as a Whole Number. 15. (27:48) Formulas 16. (27:44) Create Frequency Distribution with Formulas for Grade Data. 17. (30:03) Text Formulas for Category Labels 18. (30:40) COUNTIFS function with Comparative Operators Joined to Lower and Upper Limits from the Cells. 19. (33:17) Relative/Percent Frequency Formula. 20. (34:00) Create Histogram for Grade Data based on Frequency Distribution created with formulas. 21. (35:56) See that we can change the categories be more precise when we use formulas. 22. (38:20) Link Data Labels in Chart to cells in the spreadsheet 23. (39:16) See how formulas allow Frequency Distribution Formulas and Histogram Chart update automatically when raw data change. See different grade distributions with Histogram. 24. (40:45) Summary
Views: 168038 ExcelIsFun
What is Data Processing & Data Analysis & its Methods ? Urdu / Hindi
This Video Give The Basic Concept of What is Data Processing (Data Analysis) And its Methods ? (Urdu / Hindi) My Recommenmd Amazing Gears & Products: 1. Books: https://amzn.to/2Xgbx4L 2. Digital Music: https://amzn.to/2XCmUU2 3. Electronics: https://amzn.to/2XgniIy 4. Computers, Tablets and IT Accessories: https://amzn.to/2XcawL0 My Youtube devices & equipements 1. Boya Microphones: https://amzn.to/2RJ54Jz 2. Dell XPS 15 Laptop: https://amzn.to/2JjoWPU 3. Nikon DSLR Camera: https://amzn.to/2XgrtUx 4. Samsung Galaxy S10: https://amzn.to/2Nlo4iY Follow Me ON: Facebook: https://www.facebook.com/ZPZ-Education-2089120401335326 Dailymotion: https://www.dailymotion.com/za991425 Youtube: https://www.youtube.com/channel/UCwFzeQDf9cGm_ZeTXV_t5SA
Views: 12345 ZPZ Education
Licia Verde: Statistical techniques for data analysis in Cosmology. Lecture 3
Essential Cosmology for the Next Generation 2011 was a winter school and research meeting organized by the Berkeley Center for Cosmological Physics and the Instituto Avanzado de Cosmologia in Puerto Vallarta, Mexico from January 10-12, 2011. It was sponsored in part by the Research Corporation for Science Advancement and the US Department of Energy, Office of Science.
Views: 1011 Berkeley Lab
Introduction to Statistical Analysis
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: 34655 Dr. Bharatendra Rai
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: 74665 Enthought
Statistics and Data Analysis I: Introduction
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
Webinar 8: Methods of data analysis: Advanced and emerging methods of statistical analysis
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: 69 Think Data
PSY 87540 - Statistical Methods and Analysis - Segment 5
This course provides instruction on the calculation, use, and interpretation of descriptive and inferential statistics. The focus of study emphasizes the application and interpretation of statistical tests in conducting research at the graduate level. This course introduces inferential statistics and their application to research design. Both parametric and non-parametric approaches to the analysis of data are discussed. Learning Outcomes: Evaluate course concepts critically and competently through interaction with Learners and Faculty Mentor Integrate course concepts through the use of the Taylor Study Method Assess the concepts underlying appropriate use of various research methodologies Analyze how to recognize the inappropriate or deceptive use of research methodology Compare/contrast the basic assumptions underlying various statistical operations Summarize the consequences of using various methodological approaches Differentiate between the appropriate and inappropriate application and interpretation of research methods and statistics Demonstrate ethical behavior in regard to emerging relevant technologies applicable to psychology http://www.calsouthern.edu/online-psychology-degrees/doctor-psychology-degree/doctor-psychology-courses/psy-87540/ For more information on the School of of Behavioral Sciences at California Southern University, please visit http://www.calsouthern.edu/psychology
Statistics: ANOVA (Analysis of Variance)
An example of solving a problem using ANOVA- Analysis of Variance. All the methods and symbols are as stated in the IGNOU textbook.
Views: 44146 Vectors Academy
Qualitative analysis of interview data: A step-by-step guide
The content applies to qualitative data analysis in general. Do not forget to share this Youtube link with your friends. The steps are also described in writing below (Click Show more): STEP 1, reading the transcripts 1.1. Browse through all transcripts, as a whole. 1.2. Make notes about your impressions. 1.3. Read the transcripts again, one by one. 1.4. Read very carefully, line by line. STEP 2, labeling relevant pieces 2.1. Label relevant words, phrases, sentences, or sections. 2.2. Labels can be about actions, activities, concepts, differences, opinions, processes, or whatever you think is relevant. 2.3. You might decide that something is relevant to code because: *it is repeated in several places; *the interviewee explicitly states that it is important; *you have read about something similar in reports, e.g. scientific articles; *it reminds you of a theory or a concept; *or for some other reason that you think is relevant. You can use preconceived theories and concepts, be open-minded, aim for a description of things that are superficial, or aim for a conceptualization of underlying patterns. It is all up to you. It is your study and your choice of methodology. You are the interpreter and these phenomena are highlighted because you consider them important. Just make sure that you tell your reader about your methodology, under the heading Method. Be unbiased, stay close to the data, i.e. the transcripts, and do not hesitate to code plenty of phenomena. You can have lots of codes, even hundreds. STEP 3, decide which codes are the most important, and create categories by bringing several codes together 3.1. Go through all the codes created in the previous step. Read them, with a pen in your hand. 3.2. You can create new codes by combining two or more codes. 3.3. You do not have to use all the codes that you created in the previous step. 3.4. In fact, many of these initial codes can now be dropped. 3.5. Keep the codes that you think are important and group them together in the way you want. 3.6. Create categories. (You can call them themes if you want.) 3.7. The categories do not have to be of the same type. They can be about objects, processes, differences, or whatever. 3.8. Be unbiased, creative and open-minded. 3.9. Your work now, compared to the previous steps, is on a more general, abstract level. You are conceptualizing your data. STEP 4, label categories and decide which are the most relevant and how they are connected to each other 4.1. Label the categories. Here are some examples: Adaptation (Category) Updating rulebook (sub-category) Changing schedule (sub-category) New routines (sub-category) Seeking information (Category) Talking to colleagues (sub-category) Reading journals (sub-category) Attending meetings (sub-category) Problem solving (Category) Locate and fix problems fast (sub-category) Quick alarm systems (sub-category) 4.2. Describe the connections between them. 4.3. The categories and the connections are the main result of your study. It is new knowledge about the world, from the perspective of the participants in your study. STEP 5, some options 5.1. Decide if there is a hierarchy among the categories. 5.2. Decide if one category is more important than the other. 5.3. Draw a figure to summarize your results. STEP 6, write up your results 6.1. Under the heading Results, describe the categories and how they are connected. Use a neutral voice, and do not interpret your results. 6.2. Under the heading Discussion, write out your interpretations and discuss your results. Interpret the results in light of, for example: *results from similar, previous studies published in relevant scientific journals; *theories or concepts from your field; *other relevant aspects. STEP 7 Ending remark Nb: it is also OK not to divide the data into segments. Narrative analysis of interview transcripts, for example, does not rely on the fragmentation of the interview data. (Narrative analysis is not discussed in this tutorial.) Further, I have assumed that your task is to make sense of a lot of unstructured data, i.e. that you have qualitative data in the form of interview transcripts. However, remember that most of the things I have said in this tutorial are basic, and also apply to qualitative analysis in general. You can use the steps described in this tutorial to analyze: *notes from participatory observations; *documents; *web pages; *or other types of qualitative data. STEP 8 Suggested reading Alan Bryman's book: 'Social Research Methods' published by Oxford University Press. Steinar Kvale's and Svend Brinkmann's book 'InterViews: Learning the Craft of Qualitative Research Interviewing' published by SAGE. Text and video (including audio) © Kent Löfgren, Sweden
Views: 795843 Kent Löfgren
Introduction to Bayesian data analysis - part 1: What is Bayes?
Try my new interactive online course "Fundamentals of Bayesian Data Analysis in R" over at DataCamp: https://www.datacamp.com/courses/fundamentals-of-bayesian-data-analysis-in-r ---- This is part one of a three part introduction to Bayesian data analysis. This first part aims to explain *what* Bayesian data analysis is. See here for part 2: https://youtu.be/mAUwjSo5TJE Here are links to the exercises mentioned in the video: R - https://goo.gl/cxfnYK (if this link does not work for you try http://rpubs.com/rasmusab/257829) Python - https://goo.gl/ceShN5 More Bayesian stuff can be found on my blog: http://sumsar.net. :)
Views: 105261 rasmusab
Business Statistics  - Part 3 (Graphical methods of data analysis and presentation)
This video is Part 3 of Business Statistics. It discusses in detail about various Graphical methods used in data analysis and presentation. Very helpful for various levels of Nepal Bank Limited. So don’t miss it out !!
Views: 1667 MathAddiction
Sampling: Simple Random, Convenience, systematic, cluster, stratified - Statistics Help
This video describes five common methods of sampling in data collection. Each has a helpful diagrammatic representation. You might like to read my blog: https://creativemaths.net/blog/
Views: 814636 Dr Nic's Maths and Stats
Analysing Questionnaires
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/
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
Real Time QPCR Data Analysis Tutorial
In this Bio-Rad Laboratories Real Time Quantitative PCR tutorial (part 1 of 2), you will learn how to analyze your data using both absolute and relative quantitative methods. The tutorial also includes a great explanation of the differences between Livak, delta CT and the Pfaffl methods of analyzing your results. For more videos visit http://www.americanbiotechnologist.com
Views: 363356 americanbiotech
Your Survey Closed, Now What? Quantitative Analysis Basics
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: 22862 CSSLOhioStateU
How to Use SPSS: Choosing the Appropriate Statistical Test
A step-by-step approach for choosing an appropriate statistcal test for data analysis.
Introduction to Data Science with R - Data Analysis Part 1
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: 1065143 David Langer