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Facebook scandal: Mark Zuckerberg admits errors over Cambridge Analytica data mining scandal
 
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Subscribe to France 24 now: http://f24.my/youtubeEN FRANCE 24 live news stream: all the latest news 24/7 http://f24.my/YTliveEN Facebook chief Mark Zuckerberg vowed Wednesday to "step up" to fix problems at the social media giant, as it fights a snowballing scandal over the hijacking of personal data from millions of its users. Visit our website: http://www.france24.com Subscribe to our YouTube channel: http://f24.my/youtubeEN Like us on Facebook: https://www.facebook.com/FRANCE24.English Follow us on Twitter: https://twitter.com/France24_en
Views: 858 FRANCE 24 English
Data Mining with Weka (1.6: Visualizing your data)
 
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Data Mining with Weka: online course from the University of Waikato Class 1 - Lesson 6: Visualizing your data http://weka.waikato.ac.nz/ Slides (PDF): http://goo.gl/IGzlrn https://twitter.com/WekaMOOC http://wekamooc.blogspot.co.nz/ Department of Computer Science University of Waikato New Zealand http://cs.waikato.ac.nz/
Views: 65850 WekaMOOC
Back Propagation in Neural Network with an example
 
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understanding how the input flows to the output in back propagation neural network with the calculation of values in the network. the example is taken from below link refer this https://mattmazur.com/2015/03/17/a-step-by-step-backpropagation-example/ for full example
Views: 93911 Naveen Kumar
Integrating Human Behavior Modeling and Data Mining  to Predict Human Errors- Numerical Typing
 
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Including Packages ======================= * Base Paper * Complete Source Code * Complete Documentation * Complete Presentation Slides * Flow Diagram * Database File * Screenshots * Execution Procedure * Readme File * Addons * Video Tutorials * Supporting Softwares Specialization ======================= * 24/7 Support * Ticketing System * Voice Conference * Video On Demand * * Remote Connectivity * * Code Customization ** * Document Customization ** * Live Chat Support * Toll Free Support * Call Us:+91 967-774-8277, +91 967-775-1577, +91 958-553-3547 Shop Now @ http://clickmyproject.com Get Discount @ https://goo.gl/lGybbe Chat Now @ http://goo.gl/snglrO Visit Our Channel: http://www.youtube.com/clickmyproject Mail Us: [email protected]
Views: 48 Clickmyproject
Evaluating Regression Models: RMSE, RSE, MAE, RAE
 
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My web page: www.imperial.ac.uk/people/n.sadawi
Views: 24863 Noureddin Sadawi
Introduction to Data Mining: Missing & Duplicated Data
 
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In part three of the introduction to Data Quality, we discuss missing values and duplicated data -- At Data Science Dojo, we believe data science is for everyone. Our in-person data science training has been attended by more than 3600+ employees from over 742 companies globally, including many leaders in tech like Microsoft, Apple, and Facebook. -- Learn more about Data Science Dojo here: https://hubs.ly/H0f8M3Z0 See what our past attendees are saying here: https://hubs.ly/H0f8M400 -- Like Us: https://www.facebook.com/datascienced... Follow Us: https://plus.google.com/+Datasciencedojo Connect with Us: https://www.linkedin.com/company/data... Also find us on: Google +: https://plus.google.com/+Datasciencedojo Instagram: https://www.instagram.com/data_scienc... -- Vimeo: https://vimeo.com/datasciencedojo
Views: 4931 Data Science Dojo
Accuracy and Precision
 
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To see all my Chemistry videos, check out http://socratic.org/chemistry This is an easy to understand introduction to accuracy and precision. We'll play "guess my age," and look at bulls eyes representation of accuracy and precision.
Views: 211232 Tyler DeWitt
Lecture 04 - Error and Noise
 
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Error and Noise - The principled choice of error measures. What happens when the target we want to learn is noisy. Lecture 4 of 18 of Caltech's Machine Learning Course - CS 156 by Professor Yaser Abu-Mostafa. View course materials in iTunes U Course App - https://itunes.apple.com/us/course/machine-learning/id515364596 and on the course website - http://work.caltech.edu/telecourse.html Produced in association with Caltech Academic Media Technologies under the Attribution-NonCommercial-NoDerivs Creative Commons License (CC BY-NC-ND). To learn more about this license, http://creativecommons.org/licenses/by-nc-nd/3.0/ This lecture was recorded on April 12, 2012, in Hameetman Auditorium at Caltech, Pasadena, CA, USA.
Views: 158909 caltech
Overfitting
 
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This video is part of the Udacity course "Machine Learning for Trading". Watch the full course at https://www.udacity.com/course/ud501
Views: 14432 Udacity
k means clustering example HD
 
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This is not my work! Please give credits to the original author: https://vimeo.com/110060516 To calculate means from cluster centers: For example, if a cluster contains three data points such as {32,65}, {16,87} and {17,60}, the mean of this cluster is (32+16+17)/3 and (65+87+60)/3.
Views: 150825 Iulita
Precision, Recall & F-Measure
 
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In this video, we discuss performance measures for Classification problems in Machine Learning: Simple Accuracy Measure, Precision, Recall, and the F (beta)-Measure. We explain the concepts in detail, highlighting differences between the terms, introducing Confusion Matrices, and analyzing real world examples. If you like the video, please SHARE. Don't forget to like, comment and SUBSCRIBE on your way out! If you have any questions, feel free to contact me. Email: [email protected]
Views: 10929 CodeEmporium
Linear Regression - Machine Learning Fun and Easy
 
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Linear Regression - Machine Learning Fun and Easy ►FREE YOLO GIFT - http://augmentedstartups.info/yolofreegiftsp ►KERAS Course - https://www.udemy.com/machine-learning-fun-and-easy-using-python-and-keras/?couponCode=YOUTUBE_ML Hi and welcome to a new lecture in the Fun and Easy Machine Learning Series. Today I’ll be talking about Linear Regression. We show you also how implement a linear regression in excel Linear regression attempts to model the relationship between two variables by fitting a linear equation to observed data. One variable is considered to be an explanatory variable, and the other is considered to be a dependent variable. Dependent Variable – Variable who’s values we want to explain or forecast Independent or explanatory Variable that Explains the other variable. Values are independent. Dependent variable can be denoted as y, so imagine a child always asking y is he dependent on his parents. And then you can imagine the X as your ex boyfriend/girlfriend who is independent because they don’t need or depend on you. A good way to remember it. Anyways Used for 2 Applications To Establish if there is a relation between 2 variables or see if there is statistically signification relationship between the two variables- • To see how increase in sin tax has an effect on how many cigarettes packs are consumed • Sleep hours vs test scores • Experience vs Salary • Pokemon vs Urban Density • House floor area vs House price Forecast new observations – Can use what we know to forecast unobserved values Here are some other examples of ways that linear regression can be applied. • So say the sales of ROI of Fidget spinners over time. • Stock price over time • Predict price of Bitcoin over time. Linear Regression is also known as the line of best fit The line of best fit can be represented by the linear equation y = a + bx or y = mx + b or y = b0+b1x You most likely learnt this in school. So b is is the intercept, if you increase this variable, your intercept moves up or down along the y axis. M is your slope or gradient, if you change this, then your line rotates along the intercept. Data is actually a series of x and y observations as shown on this scatter plot. They do not follow a straight line however they do follow a linear pattern hence the term linear regression Assuming we already have the best fit line, We can calculate the error term Epsilon. Also known as the Residual. And this is the term that we would like to minimize along all the points in the data series. So say if we have our linear equation but also represented in statisitical notation. The residual fit in to our equation as shown y = b0+b1x + e ------------------------------------------------------------ Support us on Patreon ►AugmentedStartups.info/Patreon Chat to us on Discord ►AugmentedStartups.info/discord Interact with us on Facebook ►AugmentedStartups.info/Facebook Check my latest work on Instagram ►AugmentedStartups.info/instagram Learn Advanced Tutorials on Udemy ►AugmentedStartups.info/udemy ------------------------------------------------------------ To learn more on Artificial Intelligence, Augmented Reality IoT, Deep Learning FPGAs, Arduinos, PCB Design and Image Processing then check out http://augmentedstartups.info/home Please Like and Subscribe for more videos :)
Views: 115099 Augmented Startups
Error Quiz - Intro to Machine Learning
 
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This video is part of an online course, Intro to Machine Learning. Check out the course here: https://www.udacity.com/course/ud120. This course was designed as part of a program to help you and others become a Data Analyst. You can check out the full details of the program here: https://www.udacity.com/course/nd002.
Views: 3034 Udacity
Linear Regression - Least Squares Criterion  Part 1
 
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Thanks to all of you who support me on Patreon. You da real mvps! $1 per month helps!! :) https://www.patreon.com/patrickjmt !! Linear Regression - Least Squares Criterion. In this video I just give a quick overview of linear regression and what the 'least square criterion' actually means. In the second video, I will actually use my data points to find the linear regression / model.
Views: 424320 patrickJMT
Tasks with multiple instances, mapping relevant data and communicating errors
 
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Explanation on how to implement tasks with multiple instances and the various ways in which we can determine the number of instances that will be executed.
Views: 32 GeneXus™
Cross Validation
 
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Watch on Udacity: https://www.udacity.com/course/viewer#!/c-ud262/l-312357973/m-438108645 Check out the full Advanced Operating Systems course for free at: https://www.udacity.com/course/ud262 Georgia Tech online Master's program: https://www.udacity.com/georgia-tech
Views: 86662 Udacity
More Data Mining with Weka (1.3: Comparing classifiers)
 
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More Data Mining with Weka: online course from the University of Waikato Class 1 - Lesson 3: Comparing classifiers http://weka.waikato.ac.nz/ Slides (PDF): http://goo.gl/Le602g https://twitter.com/WekaMOOC http://wekamooc.blogspot.co.nz/ Department of Computer Science University of Waikato New Zealand http://cs.waikato.ac.nz/
Views: 16204 WekaMOOC
Bootstrap Resampling
 
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This video provides an introduction to the technique of bootstrap resampling, which is a computational method of measuring the error in a statistic's estimator.
Views: 100557 Nick Hand
Weka Tutorial 12: Cross Validation Error Rates (Model Evaluation)
 
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In this tutorial, Weka experimenter is used to find out the error rates of every iteration in a k-fold setup. These error rates for every iteration is required to find out overfitting, best performing classification model and mostly to find out the statistical significance test for k-fold setup.
Views: 19271 Rushdi Shams
【TOSHIBA】「Data mining」Productivity improvement at the manufacturing site
 
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Using Artificial Intelligence--or AI--to analyze “Big Data”and automatically identify the causes of manufacturing failures. Productivity improves dramatically.
Data Mining with Weka (4.2: Linear regression)
 
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Data Mining with Weka: online course from the University of Waikato Class 4 - Lesson 2: Linear regression http://weka.waikato.ac.nz/ Slides (PDF): http://goo.gl/augc8F https://twitter.com/WekaMOOC http://wekamooc.blogspot.co.nz/ Department of Computer Science University of Waikato New Zealand http://cs.waikato.ac.nz/
Views: 41215 WekaMOOC
How to Clean Up Raw Data in Excel
 
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Al Chen (https://twitter.com/bigal123) is an Excel aficionado. Watch as he shows you how to clean up raw data for processing in Excel. This is also a great resource for data visualization projects. Subscribe to Skillshare’s Youtube Channel: http://skl.sh/yt-subscribe Check out all of Skillshare’s classes: http://skl.sh/youtube Like Skillshare on Facebook: https://www.facebook.com/skillshare Follow Skillshare on Twitter: https://twitter.com/skillshare Follow Skillshare on Instagram: http://instagram.com/Skillshare
Views: 76235 Skillshare
productronica 2017 - Process Optimizing through Data Mining and Machine Learning
 
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Florian Schwarz: "A warm welcome to productronica 2017. The special shows here are a big highlight – because that's where you can experience electronics manufacturing live!" "Founded in April 2017: the research fab Microelectronics Germany. This is where research capacities all over the country are bundled together and connected, to give the fab more weight internationally as a centre for microelectronics."   "Ah, Dr. Olowinsky. Hello!"    "Laser microwelding. What exactly are we looking at here?"   Dr. Alexander Olowinsky: "Laser microwelding is an established method in electronics and precision engineering for creating electrical and mechanical connections.Here you can see a laser beam melting material – and that's what creates the connection. In this particular version, the laser head contains the beam guidance, beam forming and mechanical pressing combined, for a flexible manufacturing process."   Florian Schwarz: "And what are the areas of application?"   Dr. Alexander Olowinsky: "What you see here: classic battery technology, production of battery modules and of battery packs, production of electrical connections,all the way to printed circuit board technology, because we need to create connections there too."   Florian Schwarz: "Dr. Olowinsky, thanks a lot!" Florian Schwarz: "From microelectronics to the special show devoted to hardware data mining.With me now is Ulf Oestermann, business developer at Fraunhofer IZM.Good morning!"   FlorianSchwarz: "Mr. Oestermann, what's the connection between microelectronics and hardware data mining?"   Ulf Oestermann: "The research fab Microelectronics Germany supposed to develop technologies and processes for the future. And they then have to be ported into mass production and scaled, so that they're ready to use there. That's exactly what hardware data mining is all about – showing what data records accumulate at what location in the individual process steps, and how robust they have to be in order to be used."   Florian Schwarz: "So we're talking about 'digging' data? Can we take a closer look?"   Ulf Oestermann: "Sure. No problem."   Ulf Oestermann: "Based on the data matrix code, you can immediately establish when this subassembly was manufactured, at what temperature, and in what humidity, and then conclusions can be drawn about possible errors."   Florian Schwarz: "I guess it helps save on resources – only having to replace individual components?"   Ulf Oestermann: "It's showing how thick wire is bonded. A very, very large number of wires are needed to get a high current density in the contact."   Florian Schwarz: "Mr. Oestermann, thanks very much for the tour. Hardware data mining. I'm going to the VDMA now to see what's being done with the data. And you? Back to work?"   Ulf Oestermann: "That's right!"   Florian Schwarz: "Ok - thanks. Ciao! We've just mined and collected the data. The data has to go somewhere, it has to be processed. And that brings me to the special show of the VDMA: "Smart-Data-Future Manufacturing."   "With me now is Mr. Müller from the VDMA. I've just taken a look round your stand. There's a lot of data being generated here. What's going to be done with it?"    Daniel Müller: "In the next stage, it's simply stored in various cloud systems, to make the long-term data actually usable. For models, for instance – like predictive maintenance."   Florian Schwarz: "Smart Data. How do you see the future of that?"   Daniel Müller: "A very exciting future topic is machinelearning - where companies try to make machines learn. So they can avoid errors, or correct them, all by themselves."   Florian Schwarz: "Wow. Thank you very much, Mr. Müller! Smart Data Future Manufacturing – it's a topic we're going to keep a close eye on. Well, that's all from productronica 2017. I'm already looking forward to 2019! Goodbye!"
Views: 316 productronica
Logistic Regression with R: Categorical Response Variable at Two Levels (2018)
 
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Provides an example of student college application for carrying out logistic regression analysis with R. Data: https://goo.gl/VEBvwa R File: https://goo.gl/PdRktk Machine Learning videos: https://goo.gl/WHHqWP Includes, - use of a categorical binary output variable - data partition - logistic regression model - prediction - equation for prediction - misclassification errors for training and test data - confusion matrix for training and test data - goodness-of-fit test R is a free software environment for statistical computing and graphics, and is widely used by both academia and industry. R software works on both Windows and Mac-OS. It was ranked no. 1 in a KDnuggets poll on top languages for analytics, data mining, and data science. RStudio is a user friendly environment for R that has become popular.
Views: 20794 Bharatendra Rai
Advanced Data Mining with Weka (1.5: Lag creation, and overlay data)
 
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Advanced Data Mining with Weka: online course from the University of Waikato Class 1 - Lesson 5: Lag creation, and overlay data http://weka.waikato.ac.nz/ Slides (PDF): https://goo.gl/JyCK84 https://twitter.com/WekaMOOC http://wekamooc.blogspot.co.nz/ Department of Computer Science University of Waikato New Zealand http://cs.waikato.ac.nz/
Views: 2581 WekaMOOC
Data Mining with Weka (3.2: Overfitting)
 
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Data Mining with Weka: online course from the University of Waikato Class X - Lesson X: Overfitting http://weka.waikato.ac.nz/ Slides (PDF): http://goo.gl/1LRgAI https://twitter.com/WekaMOOC http://wekamooc.blogspot.co.nz/ Department of Computer Science University of Waikato New Zealand http://cs.waikato.ac.nz/
Views: 26506 WekaMOOC
MMIS 643 Data Mining Assignment 3 Solutions
 
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A. A neural network typically starts out with random coefficients (weights); hence, it produce essentially random predications when presented with its first case. What is the key ingredients by which the net (neural network) evolves to produce a more accurate predication? (Please answer your question as clearly and concisely as possible.) (10 points) B. Consider the Boston Housing Data file (The schema of the data file is given on page 27 in Table 2.2 of the textbook.). (40 points) a. Study the Neural Networks Prediction example from the URL: http://www.solver.com/xlminer/help/neural-networks-classification-intro, and following the example step by step. b. Using XLMINER’s neural network routine to fit a model using XLMINER default values for neural network parameters by using the predictors such as CRIM, ZN, INDUS, CHAS, NOX, RM, AGE, DIS, RAD to classify the value of CAT.MEDV. i. Record the RMS errors for the training data and the validation data, and observe the lift charts for repeating the process, changing the number of epochs to 300, 3000, 10,000, 20,000. ii. What happens to RMS error for the training data set as the number of epochs increases? iii. What happens to RMS error for the validation data set as the number of epochs increases? iv. Comments on the appropriate number of epochs for the model. Note: (Please use the Prediction Option of the Neural Network in order to get RMS) C. For Association Rule Mining, please define the following terms: (10 points) a. Support b. Confidence c. Lift D. Study the Association Mining example from the URL: http://www.solver.com/xlminer/help/associationrules. E. Problem 13. 3 on page 277-278 of the textbook, Data Mining for Business Intelligence: Concepts, Techniques, and Applications in Microsoft Office Excel with XLMiner, 2 edition, 2010, by Galit Shmueli, Nitin R. Patel, and Peter C. Bruce, ISBN: 978-0-470-52682-8. The data file is attached. (40 points) Note: 1. The data files are posted along Written Assignment #3.
Views: 336 Libraay Downloads
A Time Efficient Approach for Detecting Errors in Big Sensor Data on Cloud
 
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2015 IEEE Transaction on Cloud Computing For More Details::Contact::K.Manjunath - 09535866270 http://www.tmksinfotech.com and http://www.bemtechprojects.com Bangalore - Karnataka
Views: 836 manju nath
An Introduction to Linear Regression Analysis
 
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Tutorial introducing the idea of linear regression analysis and the least square method. Typically used in a statistics class. Playlist on Linear Regression http://www.youtube.com/course?list=ECF596A4043DBEAE9C Like us on: http://www.facebook.com/PartyMoreStudyLess Created by David Longstreet, Professor of the Universe, MyBookSucks http://www.linkedin.com/in/davidlongstreet
Views: 686674 statisticsfun
London Crime Analysis - Errors and all!
 
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A recording of a data exploration of crime information for London. This first chart shows specific crime types in each of the most criminal neighborhoods and compares them to the average. This video shows the unedited* iterative process of how I code in R, errors and all... in hopes of dispelling the myth of the perfect analysis. It's frequently ugly how we get results... might as well stop pretending to be perfect**! * I do edit the videos to remove long silences and personal information... but nothing more than that. ** There are many people that are infinitely better than me, so not everyone struggles so much. I have no idea if my experience is normal or not... I just know that I'm not as good as the final product looks. I welcome any suggestions on how to improve, constructive criticism, and all feedback, but please be nice! My ego might shatter if you tell me you know more than me ;) Github repo link TBD
Views: 37 Amit Kohli
Logistic Regression: Part 2 ("Data Mining for Business Intelligence")
 
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On odds, probabilities, and the logit function
Views: 1965 Galit Shmueli
Mod-01 Lec-03 A classification of forecast errors
 
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Dynamic Data Assimilation: an introduction by Prof S. Lakshmivarahan,School of Computer Science,University of Oklahoma.For more details on NPTEL visit http://nptel.ac.in
Views: 799 nptelhrd
droidcon 2013: Big Data analysis of Android errors; Panagiotis Papadopoulos, BugSense
 
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What BigData analysis tells us about Android errors;Panagiotis Papadopoulos, BugSense We report how we used software telemetry data to analyze the causes of api failures in Android applications. We got a sample of 100GB worth of crash data that thousands applications send to a BugSense service. We processed that data to extract more than a million stack traces, stitching together parts of chained exceptions, and established heuris- tic rules to draw the border between applications and the api calls. The most common crash causes can be attributed to memory exhaustion, race conditions or deadlocks, and missing or corrupt resources. http://de.droidcon.com/2013/sessnio/what-bigdata-analysis-tells-us-about-android-errors
Views: 235 droidcon
Exploring GIS: Spatial data sources
 
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An overview of the issues related to spatial data sources for use in a geographic information system. The discussion include: common sources of GIS data, assessing data quality, errors, uncertainty, GPS and Census data.
Views: 6212 GIS VideosTV
7.  Error Messages - External Inputs
 
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Error Messages - error messages are counted as data elements (DET's), not unique external inquiries. Count one DET for the entire input screen. Multiple Error Messages are similar to recursive values. An error message is part of another elementary process.
Views: 5630 functionpoints
R tutorial: Cross-validation
 
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Learn more about machine learning with R: https://www.datacamp.com/courses/machine-learning-toolbox In the last video, we manually split our data into a single test set, and evaluated out-of-sample error once. However, this process is a little fragile: the presence or absence of a single outlier can vastly change our out-of-sample RMSE. A better approach than a simple train/test split is using multiple test sets and averaging out-of-sample error, which gives us a more precise estimate of true out-of-sample error. One of the most common approaches for multiple test sets is known as "cross-validation", in which we split our data into ten "folds" or train/test splits. We create these folds in such a way that each point in our dataset occurs in exactly one test set. This gives us 10 test sets, and better yet, means that every single point in our dataset occurs exactly once. In other words, we get a test set that is the same size as our training set, but is composed of out-of-sample predictions! We assign each row to its single test set randomly, to avoid any kind of systemic biases in our data. This is one of the best ways to estimate out-of-sample error for predictive models. One important note: after doing cross-validation, you throw all resampled models away and start over! Cross-validation is only used to estimate the out-of-sample error for your model. Once you know this, you re-fit your model on the full training dataset, so as to fully exploit the information in that dataset. This, by definition, makes cross-validation very expensive: it inherently takes 11 times as long as fitting a single model (10 cross-validation models plus the final model). The train function in caret does a different kind of re-sampling known as bootsrap validation, but is also capable of doing cross-validation, and the two methods in practice yield similar results. Lets fit a cross-validated model to the mtcars dataset. First, we set the random seed, since cross-validation randomly assigns rows to each fold and we want to be able to reproduce our model exactly. The train function has a formula interface, which is identical to the formula interface for the lm function in base R. However, it supports fitting hundreds of different models, which are easily specified with the "method" argument. In this case, we fit a linear regression model, but we could just as easily specify method = 'rf' and fit a random forest model, without changing any of our code. This is the second most useful feature of the caret package, behind cross-validation of models: it provides a common interface to hundreds of different predictive models. The trControl argument controls the parameters caret uses for cross-validation. In this course, we will mostly use 10-fold cross-validation, but this flexible function supports many other cross-validation schemes. Additionally, we provide the verboseIter = TRUE argument, which gives us a progress log as the model is being fit and lets us know if we have time to get coffee while the models run. Let's practice cross-validating some models.
Views: 38830 DataCamp
Classification accuracy assessment and errors
 
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Subject: Geology Paper: Remote sensing and GIS and GPS Module: Classification accuracy assessment and errors Content Writer: Dr. Manika Gupta
Views: 2947 Vidya-mitra
Clustering (3): K-Means Clustering
 
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The K-Means clustering algorithm. Includes derivation as coordinate descent on a squared error cost function, some initialization techniques, and using a complexity penalty to determine the number of clusters.
Views: 14869 Alexander Ihler
More Data Mining with Weka (1.2: Exploring the Experimenter)
 
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More Data Mining with Weka: online course from the University of Waikato Class 1 - Lesson 2: Exploring the Experimenter http://weka.waikato.ac.nz/ Slides (PDF): http://goo.gl/Le602g https://twitter.com/WekaMOOC http://wekamooc.blogspot.co.nz/ Department of Computer Science University of Waikato New Zealand http://cs.waikato.ac.nz/
Views: 17588 WekaMOOC
Weka Tutorial 35: Creating Training, Validation and Test Sets (Data Preprocessing)
 
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The tutorial that demonstrates how to create training, test and cross validation sets from a given dataset.
Views: 74726 Rushdi Shams
Sampling & its 8 Types: Research Methodology
 
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Dr. Manishika Jain in this lecture explains the meaning of Sampling & Types of Sampling Research Methodology Population & Sample Systematic Sampling Cluster Sampling Non Probability Sampling Convenience Sampling Purposeful Sampling Extreme, Typical, Critical, or Deviant Case: Rare Intensity: Depicts interest strongly Maximum Variation: range of nationality, profession Homogeneous: similar sampling groups Stratified Purposeful: Across subcategories Mixed: Multistage which combines different sampling Sampling Politically Important Cases Purposeful Sampling Purposeful Random: If sample is larger than what can be handled & help to reduce sample size Opportunistic Sampling: Take advantage of new opportunity Confirming (support) and Disconfirming (against) Cases Theory Based or Operational Construct: interaction b/w human & environment Criterion: All above 6 feet tall Purposive: subset of large population – high level business Snowball Sample (Chain-Referral): picks sample analogous to accumulating snow Advantages of Sampling Increases validity of research Ability to generalize results to larger population Cuts the cost of data collection Allows speedy work with less effort Better organization Greater brevity Allows comprehensive and accurate data collection Reduces non sampling error. Sampling error is however added. Population & Sample @2:25 Sampling @6:30 Systematic Sampling @9:25 Cluster Sampling @ 11:22 Non Probability Sampling @13:10 Convenience Sampling @15:02 Purposeful Sampling @16:16 Advantages of Sampling @22:34 #Politically #Purposeful #Methodology #Systematic #Convenience #Probability #Cluster #Population #Research #Manishika #Examrace For IAS Psychology postal Course refer - http://www.examrace.com/IAS/IAS-FlexiPrep-Program/Postal-Courses/Examrace-IAS-Psychology-Series.htm For NET Paper 1 postal course visit - https://www.examrace.com/CBSE-UGC-NET/CBSE-UGC-NET-FlexiPrep-Program/Postal-Courses/Examrace-CBSE-UGC-NET-Paper-I-Series.htm
Views: 318828 Examrace
Domain driven data mining - Know It ALL 🔊✅
 
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Domain driven data mining will be explored in this video. This video series is something special. We're fully delving into all things everything. This breaks from merely pronouncing and discussing and goes further to deeply understand words and ideas. Link to Amazon.com http://amzn.to/2hFyI1h Link above take you to amazon and then amazon kicks me some money for alerting you to some awesome goods. We thank you for clicking the links. THANK for WATCHING, SUBSCRIBING, LIKING, COMMENTING, SHARING and DONATING!!! It means a lot to my family! PLEASE DONATE via VENMO for MORE EDUCATIONAL CONTENT and ENDEAVORS https://venmo.com/SeeHearSayLearn SeeHearSayLearn.com presents a series of videos to get you speaking and learning languages such as English, Spanish / Espanol, French, German, Albanian, Arabic, and more. We are working hard to get our videos uploaded. We provide you with word pronunciations, definitions, translations, stories, rhymes, riddles, jokes, tongue twisters, and anything that will help bridge the gap between your current fluency to your desired proficiency and understanding. Whether you're just learning or trying to bolster your intellectual quotient into a new stratosphere of concise and succinct communications, allocating the proper verbiage could be paramount to illustrating a picture for the recipient or merely shoving drab nondescript sounds of failure down their auditory meatuses. Run on sentence you say? I'd agree. Utilizing big complicated words isn't usually the most effective form of communication, but adapting your language to your recipient will be the most effective way to transfer your thoughts. Having a wide array of tools for each project will allow you to tailor your message for the most effect and efficient use of your time. To write, read, and listen to language takes fewer words than you might imagine. In each language, you could likely get away with understanding a few thousand words and be completely comfortable with many different language settings. Why even a few hundred can get you quite far. If ever you find any of the words to be inaccurate in any way, which may most often be the pronunciation I want to thank anyone who reaches out to send me a message regarding any errors. I will do my best to read and correct any perceived errors. Be advised that many pronunciation can vary slightly between regions. My congratulations to anyone broadening their word bank in any language. Science is clear that with more word associations languages become easier to learn and has the potential to be a protective buffer against dementia and Alzheimer's Disease. Please visit www.seehearsaylearn.com FACEBOOK FOLLOW https://www.facebook.com/seehearsaylearn TWITTER FOLLOW https://www.twitter.com/seehearsaylearn YOUTUBE SUBSCRIBE https://www.youtube.com/channel/UCeElmCkT1hfDJ7YhLCwxG_g PLEASE DONATE via VENMO for MORE EDUCATIONAL CONTENT and ENDEAVORS https://venmo.com/SeeHearSayLearn THANK for WATCHING, SUBSCRIBING, LIKING, COMMENTING, SHARING and DONATING!!! It means a lot to my family! This video series couldn't do what it does without the help of Wikipedia and its community along with so many other people to thank.
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