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Views: 6314
5 Minutes Engineering

Description of categorical variables and a comparison to quantitative and ordinal variables.

Views: 24960
Stephanie Glen

Linear Regression - Machine Learning Fun and Easy
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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
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Views: 124826
Augmented Startups

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: 848576
Dr Nic's Maths and Stats

My web page:
www.imperial.ac.uk/people/n.sadawi

Views: 16514
Noureddin Sadawi

This video discusses the role of the Adjusted R-Squared in helping us determine which variables should be used in multiple regression models.
TABLE OF CONTENTS:
00:00 Introduction
00:10 The Variable Selection Problem
00:53 Strategies for Variable Selection
01:38 Data for Example: Vehicle’s MPG
02:48 Strategy 1
03:48 Model 1: Using All Variables
04:32 Model 2: Removing Acceleration
05:04 Model 3: Removing Acceleration and Cylinders
05:27 Problems with Strategy 1
06:18 Objectives of Regressions
08:06 Fixing or Adjusting the R2
08:48 The Adjusted R-Squared
09:57 Model 1: Using All Variables
10:38 Model 2: Removing Acceleration
11:06 Model 3: Removing Acceleration and Cylinders
11:38 Model 2 is our Best Model with the Max. R2
11:48 You are not done…

Views: 32860
dataminingincae

Learn how to make predictions using Simple Linear Regression. To do this you need to use the Linear Regression Function (y = a + bx) where "y" is the dependent variable, "a" is the y intercept, "b" is the slope of the regression line, and "x" is the independent variable.
This video also shows you how to determine the slope (b) of the regression line, and the y intercept (a).
In order to determine the slope of a line you will need to first determine the Pearson Correlation Coefficient - this is described in a separate video (https://www.youtube.com/watch?v=2SCg8Kuh0tE).

Views: 438938
Eugene O'Loughlin

Decision Tree (CART) - Machine Learning Fun and Easy
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Decision tree is a type of supervised learning algorithm (having a pre-defined target variable) that is mostly used in classification problems. A tree has many analogies in real life, and turns out that it has influenced a wide area of machine learning, covering both classification and regression (CART).
So a decision tree is a flow-chart-like structure, where each internal node denotes a test on an attribute, each branch represents the outcome of a test, and each leaf (or terminal) node holds a class label. The topmost node in a tree is the root node.
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Views: 125215
Augmented Startups

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: 22374
Bharatendra Rai

Overview of multiple regression including the selection of predictor variables, multicollinearity, adjusted R-squared, and dummy variables.
If you find these videos useful, I hope that you will consider signing up for my online statistics workshop on Udemy, which contains additional videos and lots of problems to help you apply and reinforce the important concepts: https://www.udemy.com/statshelp/?couponCode=coefficient

Views: 173617
George Ingersoll

Provides perturbation analysis with r, and includes,
- linear model and vif
- Perturbation Analysis with Numerical Independent Variables
- Perturbation Analysis with Numerical & Categorical Independent Variables
- Making Estimates Repeatable
- Reclassification Probabilities
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: 1130
Bharatendra Rai

This video will show you how to make a simple scatter plot. Remember to put your independent variable along the x-axis, and you dependent variable along the y-axis. For more videos please visit http://www.mysecretmathtutor.com

Views: 194727
MySecretMathTutor

Learn the difference between Nominal, ordinal, interval and ratio data. http://youstudynursing.com/
Research eBook on Amazon: http://amzn.to/1hB2eBd
Check out the links below and SUBSCRIBE for more youtube.com/user/NurseKillam
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Quantitative researchers measure variables to answer their research question.
The level of measurement that is used to measure a variable has a significant impact on the type of tests researchers can do with their data and therefore the conclusions they can come to. The higher the level of measurement the more statistical tests that can be run with the data. That is why it is best to use the highest level of measurement possible when collecting information.
In this video nominal, ordinal, interval and ratio levels of data will be described in order from the lowest level to the highest level of measurement. By the end of this video you should be able to identify the level of measurement being used in a study. You will also be familiar with types of tests that can be done with each level.
To remember these levels of measurement in order use the acronym NOIR or noir.
The nominal level of measurement is the lowest level. Variables in a study are placed into mutually exclusive categories. Each category has a criteria that a variable either has or does not have. There is no natural order to these categories.
The categories may be assigned numbers but the numbers have no meaning because they are simply labels. For example, if we categorize people by hair color people with brown hair do not have more or less of this characteristic than those with blonde hair.
Nominal sounds like name so it is easy to remember that at a nominal level you are simply naming categories.
Sometimes researchers refer to nominal data as categorical or qualitative because it is not numerical.
Ordinal data is also considered categorical. The difference between nominal and ordinal data is that the categories have a natural order to them. You can remember that because ordinal sounds like order.
While there is an order, it is also unknown how much distance is between each category.
Values in an ordinal scale simply express an order.
All nominal level tests can be run on ordinal data.
Since there is an order to the categories the numbers assigned to each category can be compared in limited ways beyond nominal level tests. It is possible to say that members of one category have more of something than the members of a lower ranked category. However, you do not know how much more of that thing they have because the difference cannot be measured.
To determine central tendency the categories can be placed in order and a median can now be calculated in addition to the mode.
Since the distance between each category cannot be measured the types of statistical tests that can be used on this data are still quite limited. For example, the mean or average of ordinal data cannot be calculated because the difference between values on the scale is not known.
Interval level data is ordered like ordinal data but the intervals between each value are known and equal. The zero point is arbitrary. Zero simply represents an additional point of measurement.
For example, tests in school are interval level measurements of student knowledge. If you scored a zero on a math test it does not mean you have no knowledge. Yet, the difference between a 79 and 80 on the test is measurable and equal to the difference between an 80 and an 81.
If you know that the word interval means space in between it makes remembering what makes this level of measurement different easy.
Ratio measurement is the highest level possible for data. Like interval data, Ratio data is ordered, with known and measurable intervals between each value. What differentiates it from interval level data is that the zero is absolute. The zero occurs naturally and signifies the absence of the characteristic being measured. Remember that Ratio ends in an o therefore there is a zero.
Typically this level of measurement is only possible with physical measurements like height, weight and length.
Any statistical tests can be used with ratio level data as long as it fits with the study question and design.

Views: 329811
NurseKillam

Dr. Manishika Jain in this lecture explains the meaning of variables and explain the 28 types of variables:
Attribute or Quality
Differ in magnitude
Control of Variables
IV, DV, Mediating Variable
IV: Characteristic of experiment that is manipulated
DV: Variable measured
Mediating/intervening – hypothetical concept explain relation b/w variables (Parent’s status - child’s status by education)
Confounding – extra variable (effect of activity on obesity – AGE)
Dig! Dig!
Effect of noise on test score
IQ varies with age
Quantitative vs. Qualitative
Quantitative: Numbers (Interval/ratio)
Qualitative: attitude (good or bad) – can be compared not measured (nominal/ordinal)
Variables based on Scaling
Continuous, Discrete & Categorical Variable
Absolute vs. Relative
Absolute: Meaning doesn’t imply reference to property of others
Relative: Relationship b/w persons and objects
Global, Relational & Contextual
Global: Only to the level at which they are defined
Relational: Relationship of a unit
Contextual: Super-unit (all at lower level receive same value) – disaggregation
Analytical & Structural: From lower level data – aggregation
Active: Can be manipulated – experimental
Attribute: Pre-existing quality
Binary/Dichotomous: pass/fail (0 - 1)
Endogenous
Exogenous
Dummy – record categorical variable in series of binary variable
Latent – cannot be observed (intelligence)
Manifest – indicates presence of latent (IQ score)
Polychotomous – with 2 or more possible values
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Variables @0:16
Control of Variables @0:38
IV, DV, Mediating Variable @4:26
Quantitative vs. Qualitative @6:28
Quantitative @6:31
Qualitative @6:53
Variables based on Scaling @7:16
Continuous, Discrete & Categorical Variable @11:10
Absolute vs. Relative @12:28
Global, Relational & Contextual @13:05
#Contextual #Categorical #Categorical #Scaling #Measured #Confounding #Hypothetical #Mediating #Independent #Variables #Manishika #Examrace
For IAS Psychology postal Course refer - http://www.examrace.com/IAS/IAS-FlexiPrep-Program/Postal-Courses/Examrace-IAS-Psychology-Series.htm

Views: 72822
Examrace

A quick look at the differences between continuous data and discrete data including examples.

Views: 130820
Fast Math

Hello everyone, In this video, we will be discussing the types of algorithms in Machine Learning.
There are many algorithms in Machine Learning and many more are yet being developed. But all of the Machine Learning algorithms can be classified into 3 main categories. And they are
1) Supervised Learning
2) Un Supervised Learning, and
3) Reinforcement Learning
Supervised learning:
Supervised Learning algorithms are used when the given data has both independent variable/s and target variable. And the task of the supervised learning algorithm is to find the relationship independent variable/s and the target variable.
Let's try to understand this with a simple example. Let's assume we have some data about the hours studied by students and the marks scored by them in the exam. Here the hours studied is the independent variable and marks scored is the target variable. We are required to find what kind of relationship exists between the hours studied and marks scored. If we just plot the given data on the graph, we can see that the marks scored increases as the number of hours studied increases. Also we can see that there is linear relationship between the input and target variables.
Thus the given data has a linear relationship which is of the form y = mx +c
In Supervised learning, again there are two categories:
1) Algorithms for Regression type problems
2) Algorithms for Classification type problems
When the target variables have continuous values, Regression type algorithms can be used. Example, the data of hours studied and marks scored. Here the marks scored is a continuous value like 30, 42.5, 53 etc., Thus, here regression type algorithms should be used.
When the target variables have discrete or fixed set of values, we can use classification type algorithms. For example, if we have the data of hours studied and passed in exam or not, the target variable passed in exam or not has only 2 values pass or fail. Thus, we can use classification type algorithms here.
Some of the popular supervised learning algorithms are linear regression, logistic regression, k nearest neighbours, support vector machines, decision trees, random forests and naïve bayes.
Unsupervised Learning:
Unsupervised Learning algorithms can be used when there are no target variables. In this case we are not trying to predict anything. We are just trying to find the pattern in the data if there are any.
Let's take a simple example to understand this.
We have the data which has two independent variables. One is Gender and other is Interested in either beauty products or gaming products. We don’t have any target variable in this case.
If we can visualize this data, this is how it looks. We can observe some sort of pattern here. Many males are interested in gaming products. Many females are interested in beauty products. Very few females are interested in gaming products and very few males are interested in the beauty products.
Basically when we feed this data to an unsupervised learning algorithm, the algorithm will simply find this pattern and output four clusters from this data and also given some random names to these clusters like cluster 1 , cluster 2 , cluster 3 and cluster 4.
Once the pattern is ready, it can be very helpful. For example, let's assume we have a beauty product for men. We can easily target this product for cluster 4 instead of targeting on all the people. This will reduce the advertising costs significantly.
Some of the popular unsupervised learning algorithms are KMeans Clustering, Heirarchical clustering and Single Linkage Clustering.
Reinforcement learning:
Reinforcement learning is all about letting an agent to interact in an environment. The agent will be rewarded for doing positive work and punished for doing negative work in the environment.
Lets assume you are training your dog to do certain trick. Whenever your dog does the trick right, you will reward it by giving some treat. If the dog does the trick wrong, you will punish it by not giving any treat. The dog will understand this and tries to perform the trick correctly so as to maximize the positive reward.

Views: 511
Art of Engineer

Full lecture: http://bit.ly/D-Tree
Decision trees are interpretable, they can handle real-valued attributes (by finding appropriate thresholds), and handle multi-class classification and regression with minimal changes.

Views: 48752
Victor Lavrenko

An explanation of how to compute the chi-squared statistic for independent measures of nominal data.
For an explanation of significance testing in general, see http://evc-cit.info/psych018/hyptest/index.html
There is also a chi-squared calculator at http://evc-cit.info/psych018/chisquared/index.html

Views: 926330
J David Eisenberg

More Data Mining with Weka: online course from the University of Waikato
Class 4 - Lesson 4: Fast attribute selection using ranking
http://weka.waikato.ac.nz/
Slides (PDF):
http://goo.gl/I4rRDE
https://twitter.com/WekaMOOC
http://wekamooc.blogspot.co.nz/
Department of Computer Science
University of Waikato
New Zealand
http://cs.waikato.ac.nz/

Views: 15235
WekaMOOC

More Data Mining with Weka: online course from the University of Waikato
Class 4 - Lesson 1: Attribute selection using the "wrapper" method
http://weka.waikato.ac.nz/
Slides (PDF):
http://goo.gl/I4rRDE
https://twitter.com/WekaMOOC
http://wekamooc.blogspot.co.nz/
Department of Computer Science
University of Waikato
New Zealand
http://cs.waikato.ac.nz/

Views: 15350
WekaMOOC

Which type of data is best? Attribute (Pass/fail) or Variable data. This video explains...

Views: 2253
Paul Allen

Ml full notes rupees 200 only
ML notes form : https://goo.gl/forms/7rk8716Tfto6MXIh1
Machine learning introduction : https://goo.gl/wGvnLg
Machine learning #2 : https://goo.gl/ZFhAHd
Machine learning #3 : https://goo.gl/rZ4v1f
Linear Regression in Machine Learning : https://goo.gl/7fDLbA
Logistic regression in Machine learning #4.2 : https://goo.gl/Ga4JDM
decision tree : https://goo.gl/Gdmbsa
K mean clustering algorithm : https://goo.gl/zNLnW5
Agglomerative clustering algorithmn : https://goo.gl/9Lcaa8
Apriori Algorithm : https://goo.gl/hGw3bY
Naive bayes classifier : https://goo.gl/JKa8o2

Views: 32144
Last moment tuitions

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: 466301
Bozeman Science

Also known as a "Goodness of Fit" test, use this single sample Chi-Square test to determine if there is a significant difference between Observed and Expected values. This video shows a step-by-step method for calculating Chi-square.

Views: 385410
Eugene O'Loughlin

Basic introduction to correlation - how to interpret correlation coefficient, and how to chose the right type of correlation measure for your situation.
0:00 Introduction to bivariate correlation
2:20 Why does SPSS provide more than one measure for correlation?
3:26 Example 1: Pearson correlation
7:54 Example 2: Spearman (rhp), Kendall's tau-b
15:26 Example 3: correlation matrix
I could make this video real quick and just show you Pearson's correlation coefficient, which is commonly taught in a introductory stats course. However, the Pearson's correlation IS NOT always applicable as it depends on whether your data satisfies certain conditions. So to do correlation analysis, it's better I bring together all the types of measures of correlation given in SPSS in one presentation.
Watch correlation and regression: https://youtu.be/tDxeR6JT6nM
-------------------------
Correlation of 2 rodinal variables, non monotonic
This question has been asked a few times, so I will make a video on it. But to answer your question, monotonic means in one direction. I suggest you plot the 2 variables and you'll see whether or not there is a monotonic relationship there. If there is a little non-monotonic relationship then Spearman is still fine. Remember we are measuring the TENDENCY for the 2 variables to move up-up/down-down/up-down together. If you have strong non-monotonic shape in the plot ie. a curve then you could abandon correlation and do a chi-square test of association - this is the "correlation" for qualitative variables. And since your 2 variables are ordinal, they are qualitative.
Good luck

Views: 506769
Phil Chan

Predict who survives the Titanic disaster using Excel.
Logistic regression allows us to predict a categorical outcome using categorical and numeric data. For example, we might want to decide which college alumni will agree to make a donation based on their age, gender, graduation date, and prior history of donating. Or we might want to predict whether or not a loan will default based on credit score, purpose of the loan, geographic location, marital status, and income. Logistic regression will allow us to use the information we have to predict the likelihood of the event we're interested in. Linear Regression helps us answer the question, "What value should we expect?" while logistic regression tells us "How likely is it?"
Given a set of inputs, a logistic regression equation will return a value between 0 and 1, representing the probability that the event will occur. Based on that probability, we might then choose to either take or not take a particular action. For example, we might decide that if the likelihood that an alumni will donate is below 5%, then we're not going to ask them for a donation. Or if the probability of default on a loan is above 20%, then we might refuse to issue a loan or offer it at a higher interest rate.
How we choose the cutoff depends on a cost-benefit analysis. For example, even if there is only a 10% chance of an alumni donating, but the call only takes two minutes and the average donation is 100 dollars, it is probably worthwhile to call.

Views: 174840
Data Analysis Videos

PWatch the next lesson: https://www.khanacademy.org/math/probability/random-variables-topic/binomial_distribution/v/visualizing-a-binomial-distribution?utm_source=YT&utm_medium=Desc&utm_campaign=ProbabilityandStatistics
Missed the previous lesson?
https://www.khanacademy.org/math/probability/random-variables-topic/expected-value/v/law-of-large-numbers?utm_source=YT&utm_medium=Desc&utm_campaign=ProbabilityandStatistics
Probability and statistics on Khan Academy: We dare you to go through a day in which you never consider or use probability. Did you check the weather forecast? Busted! Did you decide to go through the drive through lane vs walk in? Busted again! We are constantly creating hypotheses, making predictions, testing, and analyzing. Our lives are full of probabilities! Statistics is related to probability because much of the data we use when determining probable outcomes comes from our understanding of statistics. In these tutorials, we will cover a range of topics, some which include: independent events, dependent probability, combinatorics, hypothesis testing, descriptive statistics, random variables, probability distributions, regression, and inferential statistics. So buckle up and hop on for a wild ride. We bet you're going to be challenged AND love it!
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
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Views: 951722
Khan Academy

Introduction
Heart Diseases remain the biggest cause of deaths for the last two epochs.
Recently computer technology develops software to assistance doctors in making decision of heart disease in the early stage. Diagnosing the heart disease mainly depends on clinical and obsessive data.
Prediction system of Heart disease can assist medical experts for predicting heart disease current status based on the clinical data of various patients.
In this project, the Heart disease prediction using classification algorithm Naive Bayes, and Random Forest is discussed.
Naive Bayes Algorithm
The Naive Bayes classification algorithm is a probabilistic classifier. It is based on probability models that incorporate strong independence assumptions.
Naive Bayes is a simple technique for constructing classifiers models that assign class labels to problem instances.
It assume that the value of a particular feature is independent of the value of any other feature, given the class variable. For example, a fruit may be considered to be an apple if it is red, round, and about 10 cm in diameter. A naive Bayes classifier considers each of these features to contribute independently to the probability that this fruit is an apple, regardless of any possible correlations between the color, roundness, and diameter features.
Random Forest Technique
In this technique, a set of decision trees are grown and each tree votes for the most popular class, then the votes of different trees are integrated and a class is predicted for each sample.
This approach is designed to increase the accuracy of the decision tree, more trees are produced to vote for class prediction. This approach is an ensemble classifier composed of some decision trees and the final result is the mean of individual trees results.
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E2MATRIX RESEARCH LAB

Explained K means Clustering Algorithm With Best Example In Quickest And Easiest way Ever in Hindi.
GOOD NEWS FOR COMPUTER ENGINEERS
INTRODUCING
5 MINUTES ENGINEERING
SUBJECT :-
Artificial Intelligence(AI)
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Data mining and Warehouse(DMW)
Data analytics(DA)
Mobile Communication(MC)
Computer networks(CN)
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Operating system
System programming (SPOS)
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EACH AND EVERY TOPIC OF EACH AND EVERY SUBJECT (MENTIONED ABOVE) IN COMPUTER ENGINEERING LIFE IS EXPLAINED IN JUST 5 MINUTES.
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Views: 11722
5 Minutes Engineering

This video looks at the difference between discrete and continuous variables. It includes 6 examples.

Views: 25055
Marty Brandl

Machine Learning - Part 1 - UI5CN Core
https://www.ui5cn.com/courses/project-core
Machine Learning Algorithms can be classified into 3 types
Supervised Learning, Unsupervised Learning and Reinforcement Learning.
In Machine Learning we can solve 5 types of different problems:
1. Classification
2. Anomaly Detection
3. Regression
4. Clustering
5. Reinforcement Learning
1. Classification
In machine learning and statistics, classification is the problem of identifying to which of a set of categories (sub-populations) a new observation belongs, on the basis of a training set of data containing observations (or instances) whose category membership is known. An example would be assigning a given email into "spam" or "non-spam" classes or assigning a diagnosis to a given patient as described by observed characteristics of the patient (gender, blood pressure, presence or absence of certain symptoms, etc.). Classification is an example of pattern recognition.
2. Anomaly Detection
Three broad categories of anomaly detection techniques exist. Unsupervised anomaly detection techniques detect anomalies in an unlabeled test data set under the assumption that the majority of the instances in the dataset are normal by looking for instances that seem to fit least to the remainder of the data set. Supervised anomaly detection techniques require a data set that has been labelled as "normal" and "abnormal" and involves training a classifier (the key difference to many other statistical classification problems is the inherent unbalanced nature of outlier detection). Semi-supervised anomaly detection techniques construct a model representing normal behaviour from a given normal training dataset and then testing the likelihood of a test instance to be generated by the learnt model.
3. Regression
Regression analysis is a set of statistical processes for estimating the relationships among variables. It includes many techniques for modelling and analyzing several variables when the focus is on the relationship between a dependent variable and one or more independent variables (or 'predictors'). More specifically, regression analysis helps one understand how the typical value of the dependent variable (or 'criterion variable') changes when any one of the independent variables is varied, while the other independent variables are held fixed.
4.Clustering
Cluster analysis or clustering is the task of grouping a set of objects in such a way that objects in the same group (called a cluster) are more similar (in some sense) to each other than to those in other groups (clusters). It is the main task of exploratory data mining, and a common technique for statistical data analysis, used in many fields, including machine learning, pattern recognition, image analysis, information retrieval, bioinformatics, data compression, and computer graphics.
5. Reinforcement Learning
Reinforcement learning (RL) is an area of machine learning inspired by behaviourist psychology, concerned with how software agents ought to take actions in an environment so as to maximize some notion of cumulative reward. The problem, due to its generality, is studied in many other disciplines, such as game theory, control theory, operations research, information theory, simulation-based optimization, multi-agent systems, swarm intelligence, statistics and genetic algorithms. In the operations research and control literature, reinforcement learning is called approximate dynamic programming, The approach has been studied in the theory of optimal control, though most studies are concerned with the existence of optimal solutions and their characterization, and not with learning or approximation.

Views: 1686
UI5 Community Network

Most of the datasets you'll find will have more than 3 dimensions. How are you supposed to understand visualize n-dimensional data? Enter dimensionality reduction techniques. We'll go over the the math behind the most popular such technique called Principal Component Analysis.
Code for this video:
https://github.com/llSourcell/Dimensionality_Reduction
Ong's Winning Code:
https://github.com/jrios6/Math-of-Intelligence/tree/master/4-Self-Organizing-Maps
Hammad's Runner up Code:
https://github.com/hammadshaikhha/Math-of-Machine-Learning-Course-by-Siraj/tree/master/Self%20Organizing%20Maps%20for%20Data%20Visualization
Please Subscribe! And like. And comment. That's what keeps me going.
I used a screengrab from 3blue1brown's awesome videos: https://www.youtube.com/channel/UCYO_jab_esuFRV4b17AJtAw
More learning resources:
https://plot.ly/ipython-notebooks/principal-component-analysis/
https://www.youtube.com/watch?v=lrHboFMio7g
https://www.dezyre.com/data-science-in-python-tutorial/principal-component-analysis-tutorial
https://georgemdallas.wordpress.com/2013/10/30/principal-component-analysis-4-dummies-eigenvectors-eigenvalues-and-dimension-reduction/
http://setosa.io/ev/principal-component-analysis/
http://sebastianraschka.com/Articles/2015_pca_in_3_steps.html
https://algobeans.com/2016/06/15/principal-component-analysis-tutorial/
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Views: 75461
Siraj Raval

If you found this useful, look for my ebook on Amazon, Straightforward Statistics using Excel and Tableau.
Mutliple regression with a dummy variable as an independent variable. Uses Excel. Converts a categorical variables into a dummy coded [0,1] using Excel's =if() tool. Interpretation of estimated regression equation

Views: 163205
Stephen Peplow

Provides illustration of healthcare analytics using multinomial logistic regression and cardiotocographic data.
R file: https://goo.gl/ty2Jf2
Data: https://goo.gl/kMAh8U
Includes,
- steps for preparing data for the analysis
- use of nnet package in r
- calculation of probabilities using coefficients from the model
- estimating probabilities using the model
- developing confusion matrix
- calculation of misclassification error
Logistic regression is an important tool for developing classification or predictive analytics models related to analyzing big data or working in data science field.
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: 46972
Bharatendra Rai

Thanks for watching
Covariance of two variable x and y in Hindi,
Cov(x,y) in Hindi,
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Important Book:
UGC CSIR NET/SET (JRF & LS) Mathematical Sciences
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*Ammaths Tutorials by Maurya A.K.*

Views: 2771
AMMATHS TUTORIALS

Watch on Udacity: https://www.udacity.com/course/viewer#!/c-ud262/l-313488098/m-641939067
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: 14798
Udacity

This Decision Tree in R tutorial video will help you understand what is decision tree, what problems can be solved using decision trees, how does a decision tree work and you will also see a use case implementation in which we do survival prediction using R. Decision tree is one of the most popular Machine Learning algorithms in use today, this is a supervised learning algorithm that is used for classifying problems. It works well classifying for both categorical and continuous dependent variables. In this algorithm, we split the population into two or more homogeneous sets based on the most significant attributes/ independent variables. In simple words, a decision tree is a tree-shaped algorithm used to determine a course of action. Each branch of the tree represents a possible decision, occurrence or reaction. Now let us get started and understand how does Decision tree work.
Below topics are explained in this Decision tree in R tutorial :
1. What is Decision tree?
2. What problems can be solved using Decision Trees?
3. How does a Decision Tree work?
4. Use case: Survival prediction in R
Subscribe to our channel for more Machine Learning Tutorials: https://www.youtube.com/user/Simplilearn?sub_confirmation=1
You can also go through the Slides here: https://goo.gl/WsM21R
Watch more videos on Machine Learning: https://www.youtube.com/watch?v=7JhjINPwfYQ&list=PLEiEAq2VkUULYYgj13YHUWmRePqiu8Ddy
#MachineLearningAlgorithms #Datasciencecourse #DataScience #SimplilearnMachineLearning #MachineLearningCourse
About Simplilearn Machine Learning course:
A form of artificial intelligence, Machine Learning is revolutionizing the world of computing as well as all people’s digital interactions. Machine Learning powers such innovative automated technologies as recommendation engines, facial recognition, fraud protection and even self-driving cars.This Machine Learning course prepares engineers, data scientists and other professionals with knowledge and hands-on skills required for certification and job competency in Machine Learning.
Why learn Machine Learning?
Machine Learning is taking over the world- and with that, there is a growing need among companies for professionals to know the ins and outs of Machine Learning
The Machine Learning market size is expected to grow from USD 1.03 Billion in 2016 to USD 8.81 Billion by 2022, at a Compound Annual Growth Rate (CAGR) of 44.1% during the forecast period.
What skills will you learn from this Machine Learning course?
By the end of this Machine Learning course, you will be able to:
1. Master the concepts of supervised, unsupervised and reinforcement learning concepts and modeling.
2. Gain practical mastery over principles, algorithms, and applications of Machine Learning through a hands-on approach which includes working on 28 projects and one capstone project.
3. Acquire thorough knowledge of the mathematical and heuristic aspects of Machine Learning.
4. Understand the concepts and operation of support vector machines, kernel SVM, naive Bayes, decision tree classifier, random forest classifier, logistic regression, K-nearest neighbors, K-means clustering and more.
5. Be able to model a wide variety of robust Machine Learning algorithms including deep learning, clustering, and recommendation systems
We recommend this Machine Learning training course for the following professionals in particular:
1. Developers aspiring to be a data scientist or Machine Learning engineer
2. Information architects who want to gain expertise in Machine Learning algorithms
3. Analytics professionals who want to work in Machine Learning or artificial intelligence
4. Graduates looking to build a career in data science and Machine Learning
Learn more at https://www.simplilearn.com/big-data-and-analytics/machine-learning-certification-training-course?utm_campaign=Decision-Tree-in-R-HmEPCEXn-ZM&utm_medium=Tutorials&utm_source=youtube
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Views: 2842
Simplilearn

This video is part of an online course, Data Analysis with R. Check out the course here: https://www.udacity.com/course/ud651. 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: 49214
Udacity

Pearson's Chi Square Test (Goodness of Fit)
Watch the next lesson: https://www.khanacademy.org/math/probability/statistics-inferential/chi-square/v/contingency-table-chi-square-test?utm_source=YT&utm_medium=Desc&utm_campaign=ProbabilityandStatistics
Missed the previous lesson?
https://www.khanacademy.org/math/probability/statistics-inferential/chi-square/v/chi-square-distribution-introduction?utm_source=YT&utm_medium=Desc&utm_campaign=ProbabilityandStatistics
Probability and statistics on Khan Academy: We dare you to go through a day in which you never consider or use probability. Did you check the weather forecast? Busted! Did you decide to go through the drive through lane vs walk in? Busted again! We are constantly creating hypotheses, making predictions, testing, and analyzing. Our lives are full of probabilities! Statistics is related to probability because much of the data we use when determining probable outcomes comes from our understanding of statistics. In these tutorials, we will cover a range of topics, some which include: independent events, dependent probability, combinatorics, hypothesis testing, descriptive statistics, random variables, probability distributions, regression, and inferential statistics. So buckle up and hop on for a wild ride. We bet you're going to be challenged AND love it!
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 KhanAcademy’s Probability and Statistics channel:
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Subscribe to KhanAcademy: https://www.youtube.com/subscription_center?add_user=khanacademy

Views: 1014905
Khan Academy

UI5CN CORE Machine Learning - Part 1
https://www.ui5cn.com/courses/project-core
Machine Learning Algorithms can be classified into 3 types
Supervised Learning, Unsupervised Learning and Reinforcement Learning.
In Machine Learning we can solve 5 types of different problems:
1. Classification
2. Anomaly Detection
3. Regression
4. Clustering
5. Reinforcement Learning
1. Classification
In machine learning and statistics, classification is the problem of identifying to which of a set of categories (sub-populations) a new observation belongs, on the basis of a training set of data containing observations (or instances) whose category membership is known. An example would be assigning a given email into "spam" or "non-spam" classes or assigning a diagnosis to a given patient as described by observed characteristics of the patient (gender, blood pressure, presence or absence of certain symptoms, etc.). Classification is an example of pattern recognition.
2. Anomaly Detection
Three broad categories of anomaly detection techniques exist.Unsupervised anomaly detection techniques detect anomalies in an unlabeled test data set under the assumption that the majority of the instances in the data set are normal by looking for instances that seem to fit least to the remainder of the data set. Supervised anomaly detection techniques require a data set that has been labeled as "normal" and "abnormal" and involves training a classifier (the key difference to many other statistical classification problems is the inherent unbalanced nature of outlier detection). Semi-supervised anomaly detection techniques construct a model representing normal behavior from a given normal training data set, and then testing the likelihood of a test instance to be generated by the learnt model.
3. Regression
Regression analysis is a set of statistical processes for estimating the relationships among variables. It includes many techniques for modeling and analyzing several variables, when the focus is on the relationship between a dependent variable and one or more independent variables (or 'predictors'). More specifically, regression analysis helps one understand how the typical value of the dependent variable (or 'criterion variable') changes when any one of the independent variables is varied, while the other independent variables are held fixed.
4.Clustering
Cluster analysis or clustering is the task of grouping a set of objects in such a way that objects in the same group (called a cluster) are more similar (in some sense) to each other than to those in other groups (clusters). It is the main task of exploratory data mining, and a common technique for statistical data analysis, used in many fields, including machine learning, pattern recognition, image analysis, information retrieval, bioinformatics, data compression, and computer graphics.
5. Reinforcement Learning
Reinforcement learning (RL) is an area of machine learning inspired by behaviorist psychology, concerned with how software agents ought to take actions in an environment so as to maximize some notion of cumulative reward. The problem, due to its generality, is studied in many other disciplines, such as game theory, control theory, operations research, information theory, simulation-based optimization, multi-agent systems, swarm intelligence, statistics and genetic algorithms. In the operations research and control literature, reinforcement learning is called approximate dynamic programming, The approach has been studied in the theory of optimal control, though most studies are concerned with the existence of optimal solutions and their characterization, and not with learning or approximation.

Views: 8179
UI5 Community Network

In statistical modeling, regression analysis is a statistical process for estimating the relationships among variables. It includes many techniques for modeling and analyzing several variables, when the focus is on the relationship between a dependent variable and one or more independent variables (or 'predictors').
Data Science Certification Training - R Programming: https://www.simplilearn.com/big-data-and-analytics/data-scientist-certification-sas-r-excel-training?utm_campaign=Regression-Data-Science-DtOYBxi4AIE&utm_medium=SC&utm_source=youtube
#datascience #datasciencetutorial #datascienceforbeginners #datasciencewithr #datasciencetutorialforbeginners #datasciencecourse
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: 4896
Simplilearn

Sahulat - Social Sciences Research Workshop - Data Analysis vs Data Mining (19 of 60)

Views: 678
Sahulat Microfinance Society

This AZTech training course is designed for all people involved in data mining and data analysis, as well as planning and forecasting, and especially the ones who are inclined to the use of graphical software interface, and their busy schedules are not allowing them to learn or apply programming languages like JAVA, R or Python.
Read More: http://aztechtraining.com/course/spss-essentials
Visit our Website: http://aztechtraining.com/
Senior Consultant: Sasa Kocic

Views: 193
AZTech Training & Consultancy

This "Linear regression in R" video will help you understand what is linear regression, why linear regression, you will see how linear regression works using a simple example and you will also see a use case predicting the revenue of a company using linear regression. Linear Regression is the statistical model used to predict the relationship between independent and dependent variables by examining two factors. The first one is which variables, in particular, are significant predictors of the outcome variable and the second one is how significant is the regression line to make predictions with the highest possible accuracy. Now, lets deep dive into this video and understand what is linear regression.
Below topics are explained in this "Linear Regression in R" video:
1. Why linear regression? ( 00:28 )
2. What is linear regression? ( 03:09 )
3. How linear regression works? ( 03:48 )
4. Use case - Predicting the revenue using linear regression (10:05)
To learn more about Data Science, subscribe to our YouTube channel: https://www.youtube.com/user/Simplilearn?sub_confirmation=1
You can also go through the Slides here: https://goo.gl/HBso29
Watch more videos on Data Science: https://www.youtube.com/watch?v=0gf5iLTbiQM&list=PLEiEAq2VkUUIEQ7ENKU5Gv0HpRDtOphC6
#DataScienceWithR #DataScienceCourse #DataScience #DataScientist #BusinessAnalytics #MachineLearning
Become an expert in data analytics using the R programming language in this data science certification training course. You’ll master data exploration, data visualization, predictive analytics and descriptive analytics techniques with the R language. With this data science course, you’ll get hands-on practice on R CloudLab by implementing various real-life, industry-based projects in the domains of healthcare, retail, insurance, finance, airlines, music industry, and unemployment.
Why learn Data Science with R?
1. This course forms an ideal package for aspiring data analysts aspiring to build a successful career in analytics/data science. By the end of this training, participants will acquire a 360-degree overview of business analytics and R by mastering concepts like data exploration, data visualization, predictive analytics, etc
2. According to marketsandmarkets.com, the advanced analytics market will be worth $29.53 Billion by 2019
3. Wired.com points to a report by Glassdoor that the average salary of a data scientist is $118,709
4. Randstad reports that pay hikes in the analytics industry are 50% higher than IT
The Data Science Certification with R has been designed to give you in-depth knowledge of the various data analytics techniques that can be performed using R. The data science course is packed with real-life projects and case studies, and includes R CloudLab for practice.
1. Mastering R language: The data science course provides an in-depth understanding of the R language, R-studio, and R packages. You will learn the various types of apply functions including DPYR, gain an understanding of data structure in R, and perform data visualizations using the various graphics available in R.
2. Mastering advanced statistical concepts: The data science training course also includes various statistical concepts such as linear and logistic regression, cluster analysis and forecasting. You will also learn hypothesis testing.
3. As a part of the data science with R training course, you will be required to execute real-life projects using CloudLab. The compulsory projects are spread over four case studies in the domains of healthcare, retail, and the Internet. Four additional projects are also available for further practice.
The Data Science with R is recommended for:
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
Learn more at: https://www.simplilearn.com/big-data-and-analytics/data-scientist-certification-sas-r-excel-training?utm_campaign=Linear-Regression-in-R-2Sb1Gvo5si8&utm_medium=Tutorials&utm_source=youtube
For more information about Simplilearn courses, visit:
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Get the Android app: http://bit.ly/1WlVo4u
Get the iOS app: http://apple.co/1HIO5J0

Views: 4455
Simplilearn

Regression analysis is used for estimating the relationships among variables. It is used for modelling and analysing the variables. Learn about the relationship between a dependent variable and one or more independent variables (predictors) from the video. Regression analysis helps to understand the relationship between dependent variable and the independent variables. Learn how to fit a regression line using Least Squares Estimation and minimize the error.
Visit our official website to go deeper into data science topics.
https://statinfer.com/course/machine-learning-with-python-2/curriculum/
Data scientist is called as the sexiest job of the 21st century.
They take an enormous mass of messy data points (unstructured and structured) and use their formidable skills in math, statistics, and programming to clean, massage and organize. But worry not we are here to the rescue and teach you how to be a data scientist, more importantly, upgrade your analytic skills to tackle any problem in the field of data science. Join us on "statinfer.com" for becoming a "scientist in data science"
Our "Machine Learning" course is now available on Udemy
https://www.udemy.com/machine-learning-made-easy-beginner-to-advance-using-r/
Part 1 – Introduction to R Programming.
This is the part where you will learn basic of R programming and familiarize yourself with R environment. Be able to import, export, explore, clean and prepare the data for advance modeling. Understand the underlying statistics of data and how to report/document the insights.
Part 2 – Machine Learning using R
Learn, upgrade and become expert on classic machine learning algorithms like Linear Regression, Logistic Regression and Decision Trees.
Learn which algorithm to choose for specific problem, build multiple model, learn how to choose the best model and be able to improve upon it. Move on to advance machine learning algorithms like SVM, Artificial Neural Networks, Reinforced Learning, Random Forests and Boosting and clustering algorithms like K-means.
Data science youtube playlist.
https://www.youtube.com/statinferanalytics
Facebook link:-
(Visit our facebook page we are sharing data science videos)
https://www.facebook.com/aboutanalytics/
Visit our official website to go deeper into data science topics.
https://statinfer.com

Views: 272
Statinfer Analytics

This video discusses numerical and graphical methods for exploring relationships between two categorical variables, using contingency tables, segmented bar plots, and mosaic plots.

Views: 26640
Mine Çetinkaya-Rundel

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
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