Search results “Statistical analysis of data example”

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

Views: 95615
LinkedIn Learning

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: 206921
The Doctoral Journey

The kind of graph and analysis we can do with specific data is related to the type of data it is. In this video we explain the different levels of data, with examples.
Subtitles in English and Spanish.

Views: 748370
Dr Nic's Maths and Stats

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.

Views: 646216
Dr Nic's Maths and Stats

Here are a few of the many ways to look at data. Which is your favorite?
Practice this lesson yourself on KhanAcademy.org right now:
https://www.khanacademy.org/math/cc-sixth-grade-math/cc-6th-data-statistics/dot-plot/e/intro-to-simple-data?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/dot-plot/v/frequency-tables-and-dot-plots?utm_source=YT&utm_medium=Desc&utm_campaign=6thgrade
Missed the previous lesson? https://www.khanacademy.org/math/cc-sixth-grade-math/cc-6th-geometry-topic/cc-6th-polygons-in-the-coordinate-plane/v/constructing-polygon-on-coordinate-plane-example?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 offers practice exercises, instructional videos, and a personalized learning dashboard that empower learners to study at their own pace in and outside of the classroom. We tackle math, science, computer programming, history, art history, economics, and more. Our math missions guide learners from kindergarten to calculus using state-of-the-art, adaptive technology that identifies strengths and learning gaps. We've also partnered with institutions like NASA, The Museum of Modern Art, The California Academy of Sciences, and MIT to offer specialized content.
For free. For everyone. Forever. #YouCanLearnAnything
Subscribe to Khan AcademyÂÃÂªs 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: 401687
Khan Academy

Please watch: "logistic regression case study"
https://www.youtube.com/watch?v=M9Reulcqb2g --~--
Learn Basic statistics for Business Analytics
Business Analytics and Data Science is 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.
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Views: 41259
Analytics Analysis Business

(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: 2372
AUBGTalks

Views: 65712
David Russell

What makes a question a "statistical question"?
Practice this lesson yourself on KhanAcademy.org right now:
https://www.khanacademy.org/math/cc-sixth-grade-math/cc-6th-data-statistics/cc-6-statistical-questions/e/statistical-questions?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/histograms/v/histograms-intro?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/dot-plot/v/frequency-tables-and-dot-plots?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 offers practice exercises, instructional videos, and a personalized learning dashboard that empower learners to study at their own pace in and outside of the classroom. We tackle math, science, computer programming, history, art history, economics, and more. Our math missions guide learners from kindergarten to calculus using state-of-the-art, adaptive technology that identifies strengths and learning gaps. We've also partnered with institutions like NASA, The Museum of Modern Art, The California Academy of Sciences, and MIT to offer specialized content.
For free. For everyone. Forever. #YouCanLearnAnything
Subscribe to Khan AcademyÂÃÂªs 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: 423152
Khan Academy

Analysis is often done as duplicates. The error seen within the duplicates is a handle on overall errors. I was in a research group that developed this technique.

Views: 1370
mrphysh

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.

Views: 193384
Global Health with Greg Martin

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: 422910
J David Eisenberg

Short animation displaying the main types of data in Statistics.
Try these questions for some practice.
https://youtu.be/VKLFfeVNI4I
https://youtu.be/MSs5NIn54-s
https://youtu.be/i5Vob7VIlwg
https://youtu.be/EjLJwX8E3Ms

Views: 29377
LegaC - Mathematics Resources

QUANTITATIVE METHODS TIME SERIES ANALYSIS

Views: 178354
Adhir Hurjunlal

Use simple data analysis techniques in SPSS to analyze survey questions.

Views: 772628
Claus Ebster

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/

Views: 178722
Southampton Education School

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: 15751
CSSLOhioStateU

Amir H. Ghaseminejad explains an example for designing a process consistant with six sigma strategy.

Views: 120778
Amir H. Ghaseminejad

Download files: http://people.highline.edu/mgirvin/excelisfun.htm
Topics in this video:
1. (00:43) Categorical Data vs. Quantitative Data
2. (02:00) Scales of Measurement (Levels of Measurement): Nominal, Ordinal, Interval, Ratio
3. (14:42) Cross Sectional Data vs. Time Series Data
4. (15:48) Graphical Display of types of Data
5. (16:22) How to Enter Data into the spreadsheet and use the Auto Complete (Auto Text) to your benefit
6. (18:50) How to create a new Excel Workbook to do your Homework from the Textbook

Views: 32074
ExcelIsFun

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 AcademyÂÃÂªs 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: 1821726
Khan Academy

Supervised and unsupervised learning algorithms

Views: 55428
Nathan Kutz

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: 69982
Enthought

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: 590243
statisticsfun

Purchase the spreadsheet (formulas included!) that's used in this tutorial for $5: https://gum.co/satisfactionsurvey
-----
Soar beyond the dusty shelf report with my free 7-day course: https://depictdatastudio.teachable.com/p/soar-beyond-the-dusty-shelf-report-in-7-days/ Most "professional" reports are too long, dense, and jargony. Transform your reports with my course. You'll never look at reports the same way again.

Views: 332243
Ann K. Emery

Download files: http://people.highline.edu/mgirvin/excelisfun.htm
Topics in this video:
1. (00:21) What Is / Are Statistics? Define Statistics
2. (02:42) Descriptive Statistics and Inferential Statistics
3. (05:00) Collect, analyze and present raw data on Yahoo Stock Price: Search the internet, download, create formulas and crate a Chart and present the information in a useful way.
4. (06:05) Get Data from http://finance.yahoo.com/
5. (06:43) Copy and paste data from internet into an Excel spreadsheet. Learn about Paste Special, Text
6. (07:28) Download a “.csv” file. “.csv” and “.txt” file types (file extensions) are text files. When you open them, they look like Excel, but it is not. We can copy and paste into a real Excel file. Text files are common files types that allow raw data to be transferred from one system to another. Text files are intermediate steps between the original Database and your Excel Spreadhseet.
7. (07:46) Control Panel, Folder Options, View Tab, Uncheck “Hide extensions for known file types”
8. (08:47) Highlight whole table keyboard is Ctrl + *
9. (09:50) Calculate maximum stock price with MAX function, minimum stock price with MIN and Mean using the AVERAGE function.
10. (11:30) Keyboard shortcuts for highlighting ranges that are not next to each other (noncontiguous ranges): Ctrl + Mouse Click
11. (12:15) Create Line Chart from Historical Data
12. (13:58) Page Setup
13. (17:52) Example of Inferential Statistics

Views: 36494
ExcelIsFun

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: 136652
YaleUniversity

In this video Dr. Ziene Mottiar, DIT, discusses issues around analyzing data and writing the analysing chapter. The difference between Findings and Analysis chapters is also discussed. This video is useful for anyone who is writing a dissertation or thesis.

Views: 63056
ZieneMottiar

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: 6222
Simplilearn

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: 6628
Broad Institute

Get the full course at: http://www.MathTutorDVD.com
The student will learn the big picture of what a hypothesis test is in statistics. We will discuss terms such as the null hypothesis, the alternate hypothesis, statistical significance of a hypothesis test, and more.
In this step-by-step statistics tutorial, the student will learn how to perform hypothesis testing in statistics by working examples and solved problems.

Views: 981033
mathtutordvd

Excel file: https://dl.dropboxusercontent.com/u/561402/TTEST.xls
In this video Paul Andersen explains how to run the student's t-test on a set of data. He starts by explaining conceptually how a t-value can be used to determine the statistical difference between two samples. He then shows you how to use a t-test to test the null hypothesis. He finally gives you a separate data set that can be used to practice running the test.
Do you speak another language? Help me translate my videos:
http://www.bozemanscience.com/translations/
Music Attribution
Intro
Title: I4dsong_loop_main.wav
Artist: CosmicD
Link to sound: http://www.freesound.org/people/CosmicD/sounds/72556/
Creative Commons Atribution License
Outro
Title: String Theory
Artist: Herman Jolly
http://sunsetvalley.bandcamp.com/track/string-theory
All of the images are licensed under creative commons and public domain licensing:
1.3.6.7.2. Critical Values of the Student’s-t Distribution. (n.d.). Retrieved April 12, 2016, from http://www.itl.nist.gov/div898/handbook/eda/section3/eda3672.htm
File:Hordeum-barley.jpg - Wikimedia Commons. (n.d.). Retrieved April 11, 2016, from https://commons.wikimedia.org/wiki/File:Hordeum-barley.jpg
Keinänen, S. (2005). English: Guinness for strenght. Retrieved from https://commons.wikimedia.org/wiki/File:Guinness.jpg
Kirton, L. (2007). English: Footpath through barley field. A well defined and well used footpath through the fields at Nuthall. Retrieved from https://commons.wikimedia.org/wiki/File:Footpath_through_barley_field_-_geograph.org.uk_-_451384.jpg
pl.wikipedia, U. W. on. ([object HTMLTableCellElement]). English: William Sealy Gosset, known as “Student”, British statistician. Picture taken in 1908. Retrieved from https://commons.wikimedia.org/wiki/File:William_Sealy_Gosset.jpg
The T-Test. (n.d.). Retrieved April 12, 2016, from http://www.socialresearchmethods.net/kb/stat_t.php

Views: 330360
Bozeman Science

Eirik Helno Herø and Cansu Birgen
Virtual Simulation Lab seminar series
http://www.virtualsimlab.com

Views: 1102
Virtual Simulation Lab

In this video you will learn the different types of sampling techniques that you can use while building predictive models or data science models. You can use Probability Samples – Each member of the population has a non zero probability of getting selected in the sample
Examples : Random Sampling, Systematic Sampling & Stratified sampling
Non Probability Samples- Members are selected from the population in non random way
Convenience sampling, Judgement sampling, Quota Sampling, & Snowball Sampling
ANalytics Study Pack : https://analyticuniversity.com
Analytics University on Twitter : https://twitter.com/AnalyticsUniver
Analytics University on Facebook : https://www.facebook.com/AnalyticsUniversity
Logistic Regression in R: https://goo.gl/S7DkRy
Logistic Regression in SAS: https://goo.gl/S7DkRy
Logistic Regression Theory: https://goo.gl/PbGv1h
Time Series Theory : https://goo.gl/54vaDk
Time ARIMA Model in R : https://goo.gl/UcPNWx
Survival Model : https://goo.gl/nz5kgu
Data Science Career : https://goo.gl/Ca9z6r
Machine Learning : https://goo.gl/giqqmx
Data Science Case Study : https://goo.gl/KzY5Iu
Big Data & Hadoop & Spark: https://goo.gl/ZTmHOA

Views: 3026
Analytics University

Download files: http://people.highline.edu/mgirvin/excelisfun.htm
Install Data Analysis Add-in For Amazing Excel Statistical Tools in Excel 2013.

Views: 35452
ExcelIsFun

Watch Sample Class recording:
http://www.edureka.co/statistics-essentials-for-analytics?utm_source=youtube&utm_medium=referral&utm_campaign=statistics-tut-1
Statistics is the study of the collection, analysis, interpretation, presentation, and organization of data.In applying statistics to, e.g., a scientific, industrial, or societal problem,
it is conventional to begin with a statistical population or a statistical model process to be studied. Populations can be diverse topics such as "all persons living in a country" or "every atom composing a crystal".
Statistics deals with all aspects of data including the planning of data collection in terms of the design of surveys and experiments.
The video covers following topics:
1.Exploratory Analysis
2.Few termenologie son Statstics
3.Variance
4.Standard Deviation
5.Inquartile Range
Edureka is a New Age e-learning platform that provides Instructor-Led Live, Online classes for learners who would
prefer a hassle free and self paced learning environment, accessible from any part of the world.
The topics related to Statistics & Probability have extensively been covered in our course 'Statistics Essentials for Analytics’.
For more information, please write back to us at [email protected]
Call us at US: 1800 275 9730 (toll free) or India: +91-8880862004

Views: 73392
edureka!

We review what the main goals of regression models are, see how the linear regression models tie to the concept of linear equations, and learn to interpret the coefficients of a simple linear regression model with an example.
TABLE OF CONTENTS:
00:00 Simple Linear Regression
00:17 Objectives of Regressions
02:54 Variable’s Roles
03:30 The Magic: A Linear Equation
04:21 Linear Equation Example
05:24 Changing the Intercept
06:02 Changing the Slope
07:00 But the world is not linear!
07:44 Simple Linear Regression Model
08:25 Linear Regression Example
09:16 Data for Example
09:46 Simple Linear Regression Model
10:17 Regression Result
11:02 Interpreting the Coefficients
12:38 Estimated vs. Actual Values

Views: 248993
dataminingincae

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;
*it surprises you;
*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.
3.10. 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
This tutorial showed how to focus on segments in the transcripts and how to put codes together and create categories. However, it is important to remember that 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.
Good luck with your study.
Text and video (including audio) © Kent Löfgren, Sweden

Views: 641617
Kent Löfgren

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: 60964
rasmusab

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/

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Dr Nic's Maths and Stats

Statistical Analysis and Data Mining
This webinar discuss the ability to analysis statistical performance information from historical market data based on any trading indicator or concept.

Views: 2684
TradeStation

Updated video 2018: SPSS for Beginners - Introduction https://youtu.be/_zFBUfZEBWQ
This video provides an introduction to SPSS/PASW. It shows how to navigate between Data View and Variable View, and shows how to modify properties of variables.

Views: 1288113
RStatsInstitute

Statistical Analysis of Data by Dr.Shahid,PhD - Research and Thesis
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Views: 190
Research and Thesis

There is a growing interest from the Oil and Gas industry in the application of statistical analysis methods in the integrity management of assets. A major area of application is statistical analysis in the planning and evaluation of inspections carried on out equipment where there is potential for in-service degradation. This area is of growing relevance as operators seek to more effectively manage the risks associated with in-service degradation and developments in inspection technology make available more quantitative information. Statistical analysis allows an improved understanding of asset condition, taking into consideration the capability and limitations of the inspection methods, and this leads to improved decision making. There are substantial benefits to operators with respect to risk management and business performance.
A major area of application of statistical analysis is in the planning and evaluation of sampling inspections where coverage is less than 100%. The aim of sampling inspections is to obtain sufficient information to allow reliable estimates for the regions not inspected. Statistical analysis underpins sampling approaches, in providing the basis for estimates for the uninspected area and also in assessing the reliability of the estimates.
Statistical analysis in support of sampling inspection is now widely applied in practice - it is for example a fundamental requirement of NII of pressure vessels where a Type B strategy per the HOIS Recommended Practice for NII (DNV-RP-G103) applies.
This free webinar will give attendees an overview of statistical analysis as applied to planning and evaluations of inspections in support of integrity management of process equipment. The webinar covers the following topics.
-Introduction to application of statistical methods in integrity management
-Statistical characteristics of in-service corrosion
Basic theory covering extreme value analysis (EVA) and use of wall thickness distributions
-Planning for effective sample inspections
-Key inspection parameters, including coverage, accuracy and probability of detection
-Use of statistical analysis for Type B NII cases
-Example applications and case studies
Your presenter: Mark Stone Bsc Eng (Mech), PhD
Mark is responsible for Sonomatic?s integrity support area and leads a team delivering integrity-related services to clients. He is a Mechanical Engineer with over 25 years experience of working closely with NDT inspection colleagues to ensure better integration of inspection as part of more effective integrity management approaches.
His experience covers planning for reliable inspection approaches and assessment of inspection results from an integrity perspective. A major part of his role covers development of methods to ensure best use is made of inspection data as input to integrity management decision making. This addresses a wide range of applications where statistical analysis is used for planning and evaluation of limited coverage inspections. Examples include development of strategies for non-intrusive inspection of pressure vessels, storage tanks, processing piping and unpiggable pipelines. He is primary author of the HOIS Recommended Practices for Non-intrusive inspection (NII) and Statistical Analysis of Inspection Data.
http://www.sonomatic.com/index.php?page=webinars&objectid=674

Views: 416
Sonomatic

How to conduct an analysis of frequencies and descriptive statistics using SPSS/PASW.

Views: 235716
bernstmj

Download workbook:
http://people.highline.edu/mgirvin/ExcelIsFun.htm
Learn the basics of Data Analysis at Highline Community College Professional Development Day 2012:
Topics in Video:
1. What is Data Analysis? ( 00:53 min mark)
2. How Data Must Be Setup ( 02:53 min mark)
Sort:
3. Sort with 1 criteria ( 04:35 min mark)
4. Sort with 2 criteria or more ( 06:27 min mark)
5. Sort by color ( 10:01 min mark)
Filter:
6. Filter with 1 criteria ( 11:26 min mark)
7. Filter with 2 criteria or more ( 15:14 min mark)
8. Filter by color ( 16:28 min mark)
9. Filter Text, Numbers, Dates ( 16:50 min mark)
10. Filter by Partial Text ( 20:16 min mark)
Pivot Tables:
11. What is a PivotTable? ( 21:05 min mark)
12. Easy 3 step method, Cross Tabulation ( 23:07 min mark)
13. Change the calculation ( 26:52 min mark)
14. More than one calculation ( 28:45 min mark)
15. Value Field Settings (32:36 min mark)
16. Grouping Numbers ( 33:24 min mark)
17. Filter in a Pivot Table ( 35:45 min mark)
18. Slicers ( 37:09 min mark)
Charts:
19. Column Charts from Pivot Tables ( 38:37 min mark)
Formulas:
20. SUMIFS ( 42:17 min mark)
21. Data Analysis Formula or PivotTables? ( 45:11 min mark)
22. COUNTIF ( 46:12 min mark)
23. Formula to Compare Two Lists: ISNA and MATCH functions ( 47:00 min mark)
Getting Data Into Excel
24. Import from CSV file ( 51:21 min mark)
25. Import from Access ( 54:00 min mark)
Highline Community College Professional Development Day 2012
Buy excelisfun products:
https://teespring.com/stores/excelisfun-store

Views: 1451078
ExcelIsFun

© 2018 Quotations on life pictures

Selling in special circumstances. shares you bought at different times and prices in one company shares through an investment club shares after a company merger or takeover employee share scheme shares. Jointly owned shares and investments. If you sell shares or investments that you own jointly with other people, work out the gain for the portion that you own, instead of the whole value. There are different rules for investment clubs. What to do next. Deduct costs. Apply reliefs.