In this video you will learn 35 varieties of regression equations which includes but not limited to
- Simple Linear Regression
-Multiple Linear Regression
-Logistic Regression
-Probit regression
-Cox regression
-Non Linear Regression
-Polynomial Regression
Lasso regression
Ridge Regression
Bayesian Regression
ANalytics Study Pack : http://analyticuniversity.com/
Analytics University on Twitter : https://twitter.com/AnalyticsUniver
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Logistic Regression in R: https://goo.gl/S7DkRy
Logistic Regression in SAS: https://goo.gl/S7DkRy
Logistic Regression Theory: https://goo.gl/PbGv1h
Time Series Theory : https://goo.gl/54vaDk
Time ARIMA Model in R : https://goo.gl/UcPNWx
Survival Model : https://goo.gl/nz5kgu
Data Science Career : https://goo.gl/Ca9z6r
Machine Learning : https://goo.gl/giqqmx
Data Science Case Study : https://goo.gl/KzY5Iu
Big Data & Hadoop & Spark: https://goo.gl/ZTmHOA

Views: 5382
Analytics University

In this video you'll learn the hierarchical representation of Regression Models.
Regression models are primarily classified into 2 categories:
- Univariate
- Multivariate
Univariate Regression model is the simplest form of statistical analysis
Multivariate Regression model is where the response variable is affected by more than one predictor variable.
They can be further classified as Liner and Non-Linear models.
You will also learn about "Simple Linear Regression"
Click Here For More Details: www.simplilearn.com/big-data-and-analytics/business-analytics-foundation-r-tools-training

Views: 5276
Simplilearn

This updated vidcast discusses the conceptual underpinnings of different types of model building in multiple analysis: simultaneous regression, hierarchical regression and stepwise regression. This improved vidcast offers clearer exposition of the concepts and a more readable Powerpoint slide.

Views: 10571
Ray Cooksey

Very basic overview of the different types of regression analysis and why regression analysis is needed. This is not for statisticians but for medical students, residents and clinicians wanting a very basic overview of what regression analysis is.

Views: 11206
Terry Shaneyfelt

The most simple and easiest intuitive explanation of regression analysis. Check out this step-by-step explanation of the key concepts of regression analysis.
It is assumed the viewer has little background in statistics. This lecture is suitable for tertiary students struggling with statistics.
****
Are you a business that needs some help data analytics? Contact me at www.taleum.co
I provide technical consulting and training seminars.
**** DID YOU LIKE THIS VIDEO? ****
Check out my full video on Simple Regressions: https://www.youtube.com/watch?v=38iNlkzF1sE
***
Come and check out my complete and comprehensive course on HYPOTHESIS TESTING! Click on the link below for a FREE PREVIEW and a MASSIVE discount (only for my Youtube students):
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This is a complete course that covers all the topics (such as the central limit theorem, p-values, hypothesis tests using proportions, and so much more) in a structured, step-by-step manner, coupled with a bucket load of practice exam questions and video worked solutions. I assume the viewer has zero background in statistics. You also get to post questions in the discussion forum and I will answer them right away!
https://www.udemy.com/simplestats/?couponCode=123
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SUBSCRIBE at: https://www.youtube.com/subscription_center?add_user=quantconceptsedu
**** Check out some of our other mini-lectures:
Check out my 30 min lecture on Hypothesis Testing!
https://www.youtube.com/watch?v=lCuB2nEaBwM
Simple Introduction to Hypothesis Testing:
http://www.youtube.com/watch?v=yTczWL7qJ-Y
A Simple Rule to Correctly Setting Up the Null and Alternate Hypotheses:
https://www.youtube.com/watch?v=R2hxisYFKxM&feature=youtu.be
The Easiest Introduction to Regression Analysis:
http://www.youtube.com/watch?v=k_OB1tWX9PM
Super Easy Tutorial on Calculating the Probability of a Type 2 Error:
https://www.youtube.com/watch?v=L9rX8kTd8PI&feature=youtu.be
Check out my latest Youtube video on the Endogeneity Bias and the 2-Stage Least Squares regression: https://www.youtube.com/watch?v=HBr3376ttOg
**
Keywords: statistics, statistics help, statistics tutor, statistics tuition, hypothesis testing, regression analysis, university help, stats help, simple regression, multiple regression, econometrics

Views: 386169
Dave Your Tutor

Hello Friends,
By considering all your valuable comments, I am advancing topic of “Nonlinear Regression” in this video, mainly comments from the video on “Correlation and Regression”. This video is mainly focused on Regression analysis, it’s types and Nonlinear Regression in very detailed along with practical example.
Nonlinear Regression analysis is used to mathematically describe the nonlinear relationship between a response variable and one or more predictor variables. Specifically, use nonlinear regression instead of “ordinary least squares regression” when you cannot adequately model the relationship with linear parameters.
I am going to explain this tool with practical example for easy understanding and better clarity. This video contains following topics:
1) What is Regression Analysis and their types?
2) Brief explanation all types of Regression Analysis methods
3) When to use Nonlinear Regression Analysis?
4) Data considerations for Nonlinear Regression
5) Nonlinear Regression Analysis with Practical Example in Microsoft Excel
6) Interpretation of results from Regression analysis including R-Square, Significance F and p-values, Coefficients, Residuals and Best Fit Model for Nonlinear Regression
I am sure, you will like it. Please add your valuable comments and tell me where you are using these tools & techniques and your experiences during practical application.
Apart from this video, please visit to my website by clicking on this link below. We will be there to remove roadblocks in your success by designing and delivering training as per your need and hand-holding support in execution of Lean and Six Sigma initiatives-
https://www.learnandapply.org/
I have also created recommendation page on this website to avail you a great knowledge to improve your personal as well as professional life. You can visit this link to review it.
https://www.learnandapply.org/recommondations
And finally thank you for watching…

Views: 311
LEARN & APPLY: Lean & Six Sigma

Today we're going to introduce one of the most flexible statistical tools - the General Linear Model (or GLM). GLMs allow us to create many different models to help describe the world - you see them a lot in science, economics, and politics. Today we're going to build a hypothetical model to look at the relationship between likes and comments on a trending YouTube video using the Regression Model. We'll be introducing other popular models over the next few episodes.
Crash Course is on Patreon! You can support us directly by signing up at http://www.patreon.com/crashcourse
Thanks to the following Patrons for their generous monthly contributions that help keep Crash Course free for everyone forever:
Mark Brouwer, Kenneth F Penttinen, Trevin Beattie, Satya Ridhima Parvathaneni, Erika & Alexa Saur, Glenn Elliott, Justin Zingsheim, Jessica Wode, Eric Prestemon, Kathrin Benoit, Tom Trval, Jason Saslow, Nathan Taylor, Brian Thomas Gossett, Khaled El Shalakany, Indika Siriwardena, SR Foxley, Sam Ferguson, Yasenia Cruz, Eric Koslow, Caleb Weeks, D.A. Noe, Shawn Arnold, Malcolm Callis, Advait Shinde, William McGraw, Andrei Krishkevich, Rachel Bright, Mayumi Maeda, Kathy & Tim Philip, Jirat, Ian Dundore
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Support Crash Course on Patreon: http://patreon.com/crashcourse
CC Kids: http://www.youtube.com/crashcoursekids

Views: 81995
CrashCourse

Supervised and unsupervised learning algorithms

Views: 71421
Nathan Kutz

We can use scatter plots to understand the relationships between variables, but it is applied only for obvious relationships like Temperature and Viscosity. Sometimes, it is not possible to comment about relationship between variables only looking at the graph.
“CORRELATION & REGRESSION” are very important mathematical concepts to define relationship between variables. This is the topic for video.
I have tried to explain these concepts with the help of practical examples which will be very easy to understand. I have also explained the procedure about how to create a “CORRELATION & REGRESSION ANALYSIS” in Microsoft Excel. Everything is with steps, snapshots and examples, which will be very easy to understand.
I have also covered statistics part like how to read and understand “SIGNIFICANCE F and P-values”
I am sure, you will liked it.
Apart from this video, please visit to my website by clicking on this link and if you think, we can support you in achieving your goals, please feel free to contact us. We will guarantee your success by designing and delivering training as per your need and hand-holding support in execution of Lean, Six Sigma & Personality Development initiatives-
https://www.learnandapply.org/
I have also created recommendation page on this website to avail you a great knowledge to improve your personal as well as professional life. You can visit this link to review it.
https://www.learnandapply.org/recommondations
And finally thank you for watching…

Views: 232001
LEARN & APPLY: Lean & Six Sigma

This video discusses what are dummy variables, how do we construct them, and how do we interpret their coefficients in a multiple or multivariate linear regression.
TABLE OF CONTENTS:
00:00 Introduction
00:10 Continuous vs. Categorical Variables
01:48 The Problem with Categorical Variables
02:52 Dummy Variables: Representing Categories
03:48 Data for Example: Professors’ Salaries
05:27 Exploring the Data
08:48 Representing Two Categories: Qualification
09:39 Data Table with Professional Dummy
10:02 Regressing Salary on Qualification
11:50 Comparing Salaries by Qualification
13:02 Qualification Regression Result
13:44 Interpreting the Coefficients for Qualification
14:45 More than Two Categories: Rank
15:43 Regressing Salary on Ranks
17:52 Data Table with Rank Dummies
18:19 Rank Regression Result
18:48 Interpreting the Coefficients for Qualification
21:14 Rank Regression Result (excluding assistant)
22:45 How do we interpret these?

Views: 114764
dataminingincae

Paper: Multivariate Analysis
Module name: Introduction toMultivariate Analysis
Content Writer: Souvik Bandyopadhyay

Views: 67229
Vidya-mitra

Subscribe to the OpenIntroOrg channel to stay up-to-date!
This video was created by OpenIntro (openintro.org) and provides an overview of the content in Section 7.3 of OpenIntro Statistics, which is a free online statistics textbook with a $10 paperback option on Amazon.
This video covers the types of outliers that can be encountered in linear regression. For more details, see Section 7.3 of OpenIntro Statistics, which may be downloaded for free online.

Views: 9503
OpenIntroOrg

Seven different statistical tests and a process by which you can decide which to use.
The tests are:
Test for a mean,
test for a proportion,
difference of proportions,
difference of two means - independent samples,
difference of two means - paired,
chi-squared test for independence and
regression.
This video draws together videos about Helen, her brother, Luke and the choconutties.
There is a sequel to give more practice choosing and illustrations of the different types of test with hypotheses.

Views: 789304
Dr Nic's Maths and Stats

In this lecture, I show which types of statistical models should be used when; the most important decision concerns the explanatory variables: When these are continuous, the analysis type will be regression; however, when these are factors, then we will conduct an analysis of variance. Overall, I show that both analyses are special examples of what is called a Linear Statistical Model. I briefly introduce linear statistical models. Later lectures will cover this in greater detail.

Views: 42131
Christoph Scherber

Views: 214
Patrick McKnight

Students get mixed up in the language of significance tests for regression because there's more than one type. Whatever econometrics/stats package you are using, Stata, Eviews, R, SPSS, SAS, Minitab - even crummy old Excel, watch this video if you are unsure about what testing the significance of ....means.

Views: 11174
Phil Chan

This video demonstrates how to interpret multiple regression output in SPSS. This example includes two predictor variables and one outcome variable. Unstandardized and standardized coefficients are reviewed.

Views: 165921
Dr. Todd Grande

This video explains the process of creating a scatterplot in SPSS and conducting simple linear regression.

Views: 270151
Research By Design

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: 524792
Phil Chan

Here, I quickly explain to you what classification, regression, and clustering are all about.

Views: 100
Shriram Vasudevan

Unit 9 (part 2) Statistics
Day 7 Types of Regression

Views: 227
Jennifer Koeppel-Keenan

Understanding why correlation does not imply causality (even though many in the press and some researchers often imply otherwise)
Practice this lesson yourself on KhanAcademy.org right now:
https://www.khanacademy.org/math/probability/statistical-studies/types-of-studies/e/types-of-statistical-studies?utm_source=YT&utm_medium=Desc&utm_campaign=ProbabilityandStatistics
Watch the next lesson: https://www.khanacademy.org/math/probability/statistical-studies/types-of-studies/v/analyzing-statistical-study?utm_source=YT&utm_medium=Desc&utm_campaign=ProbabilityandStatistics
Missed the previous lesson?
https://www.khanacademy.org/math/probability/statistical-studies/types-of-studies/v/types-statistical-studies?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: 678803
Khan Academy

Everyone needs to understand regression! Its a useful data science technique that allows us to understand the relationship between different variables. In this video, we'll play the role of a newly hired data analyst at a genetics company trying to find the relationship between advertising mediums (TV, newspaper, radio) and ticket sales to our newly opened theme park. Along the way, we'll learn about 5 types of regression models (linear, non-linear, multiple, lasso, and ridge). Expect math, code, and layers of explanation. Enjoy!
Code for this video:
https://github.com/llSourcell/ISL-Ridge-Lasso
Please Subscribe! And Like. And comment. Thats what keeps me going.
Want more education? Connect with me here:
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More learning resources:
https://www.youtube.com/watch?v=XdM6ER7zTLk
https://www.analyticsvidhya.com/blog/2017/06/a-comprehensive-guide-for-linear-ridge-and-lasso-regression/
http://statisticsbyjim.com/regression/choose-linear-nonlinear-regression/
https://hbr.org/2015/11/a-refresher-on-regression-analysis
http://blog.minitab.com/blog/adventures-in-statistics-2/how-to-interpret-regression-analysis-results-p-values-and-coefficients
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Views: 26115
Siraj Raval

This video is on Panel Data Analysis. Panel data has features of both Time series data and Cross section data. You can use panel data regression to analyse such data, We will use Fixed Effect Panel data regression and Random Effect panel data regression to analyse panel data. We will also compare with Pooled OLS , Between effect & first difference estimation
For Analytics study packs visit : https://analyticuniversity.com
Time Series Video : https://www.youtube.com/watch?v=Aw77aMLj9uM&t=2386s
Logistic Regression using SAS: https://www.youtube.com/watch?v=vkzXa0betZg&t=7s
Logistic Regression using R : https://www.youtube.com/watch?v=nubin7hq4-s&t=36s
Support us on Patreon : https://www.patreon.com/user?u=2969403

Views: 80802
Analytics University

In this video, I show how to use R to fit a linear regression model using the lm() command. I also introduce how to plot the regression line and the overall arithmetic mean of the response variable, and I briefly explain the use of diagnostic plots to inspect the residuals. Basic features of the R interface (script window, console window) are introduced.
The R code used in this video is:
data(airquality)
names(airquality)
#[1] "Ozone" "Solar.R" "Wind" "Temp" "Month" "Day"
plot(Ozone~Solar.R,data=airquality)
#calculate mean ozone concentration (na´s removed)
mean.Ozone=mean(airquality$Ozone,na.rm=T)
abline(h=mean.Ozone)
#use lm to fit a regression line through these data:
model1=lm(Ozone~Solar.R,data=airquality)
model1
abline(model1,col="red")
plot(model1)
termplot(model1)
summary(model1)

Views: 350319
Christoph Scherber

Business Analytics and Data Science are almost same concept. For both we need to learn Statistics. In this video I tried to create value on most used statistical methods for Data Science or Business Analytics for Statistical model Building.
Statistics is the study of the collection, analysis, interpretation, presentation, and organization of data. In applying statistics any can handle a scientific, industrial, or societal problem. I value your time and effort that is why I have capture almost 20 statically concept in this video.
Learn Basic statistics for Business Analytics
Here I have capture how to learn Mean, how to learn Mode, How to learn median, Concept of Sleekness, Concept of Kurtosis, learn Variables, concept of Standard deviation, Concept of Covariance, Concept of correlation, Concept of regression, How to read regression formula, how to read regression graph, Concept of Intercept, Concept of slope coefficient, Concept of Random Error, Different types of regression Analysis, Concept ANOVA (Analysis of Variance), How to read ANOVA table, How to learn R square (Interpreted R square), Concept of Adjusted R Square, Concept of F test, Concept of Information Value, Concept of WOE, Concept of Variable inflation Factors.
Learn Basic statistics for Business Analytics
By this video you can Start Learn statistics for Data Science and Business analytics easily and effectively.
These statistics are useful when at the time of running linear regression, Logistic regression statistics models.
For Statistical Data Exploration you may need to see Meager of central tendency and Data Spread in Statistics. By Understanding Mean, Mode, Median, Sleekness, Kurtosis, Variance, Standard deviation.
Learn Basic statistics for Business Analytics
To understand statistical relationship between variables you can use Covariance, Correlation coefficient, Regression , ANOVA (Analysis of Variance) .
Learn Basic statistics for Business Analytics
To understand Strength of stastical relationship between variables you can use R square, Adjusted R square, F test.
If you want to understand variable importance in your stastical model you can use Information value (IV) and Weight of evidence (WOE) Concept. Information value and Weight of evidence mostly used in Logistic Regression Analysis.
Learn Basic statistics for Business Analytics
Variable inflation factors (VIF) is used for understanding, It is the stastical method to understand variable importance. What is the importance of this variable statically in the Regression model? By VIF we check Correlation between variable.
Learn Basic statistics for Business Analytics
At last I have explained when to use ANOVA, When to Use Linear regression and when to use Logistic regression.
Learn Basic statistics for Business Analytics
Thank you So much for watching this video, Hope I can add some value in your Journey as a Statistician, Business Analytics professional and Data Scientist professional.
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Views: 86011
Analytics Analysis Business

All videos here: http://www.zstatistics.com/
The first video in a series of 5 explaining the fundamentals of regression.
Please note that in my videos I use the abbreviations:
SSR = Sum of Squares due to the Regression
SSE = Sum of Squares due to Error.
Intro: 0:00
Y-hat line: 2:26
Sample error term, e: 3:47
SSR, SSE, SST: 8:40
R-squared intro: 9:43
Population error term, ε: 12:11
Second video here: http://www.youtube.com/watch?v=4otEcA3gjLk
Ever wondered WHY you have to SQUARE the error terms??
Here we deal with the very basics: what is regression? How do we establish a relationship between two variables? Why must we SQUARE the error terms? What exactly is SSE, SSR and SST? What is the difference between a POPULATION regression function and a SAMPLE regression line? Why are there so many different types of error terms??
Enjoy.

Views: 657792
zedstatistics

This video shows how to use SPSS to conduct a Correlation and Regression Analysis. A simple null hypothesis is tested as well. The regression equation is explained despite the result of the hypothesis conclusion.
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MORE VIDEOS:
Watch Using Excel to find the Correlation Coefficient r here: https://youtu.be/y3bgaLwdm50
Watch ANOVA in SPSS here: https://youtu.be/Bx9ry1vBbTM
Watch Sampling Distribution of Sample Means here: https://youtu.be/anGsd2l5YpM
Watch Using Excel Charts to calculate Regression Equation here: https://youtu.be/qZjTtnyaV70
Watch Using Excel to calculate Regression Equation here: https://youtu.be/LDC0p9iZY8g
Watch ANOVA in Microsoft Excel (One-Way) here: https://youtu.be/WhBkgWL3_3k
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Views: 242606
Agron Kaci

You will learn about four types of link functions you can use while building logistic regression model.

Views: 754
Analytics University

This is an introduction to econometrics tutorial. This video is a basic overview and touches on each of these subjects:
1. What is Econometrics?
2. Goals of Econometrics
3. Types of Economic Data
4. The "Simple Linear Regression" (SLR)
5. Causality
This lecture on econometric theory is meant to introduce the student to the concepts of econometrics, as well as provide a basic overview of what the topic of econometrics encompasses.
The next video tutorial on simple linear regressions: http://youtu.be/CBa8frhRKMw
Follow us on Twitter @ https://twitter.com/KeynesAcademy
All video, images, commentary and music is owned by Keynes Academy.

Views: 355369
KeynesAcademy

Linear Regression Analysis, testing for the significance of the independent variables based on regression model for a linear line established by X (Independent Variable) & Y (Dependent Variable), checking for a significant relationship between the variables, understanding the predictor, coefficients, standard error, confidence intervals, T-Stat, P-Value, etc., detailed analysis by Allen Mursau

Views: 39823
Allen Mursau

An introduction to basic panel data econometrics. Also watch my video on "Fixed Effects vs Random Effects". As always, I am using R for data analysis, which is available for free at r-project.org
My Website: http://www.burkeyacademy.com/
Link to the data: http://www.burkeyacademy.com/my-forms/Panel%20Data.xlsx
Link to previous video: http://www.youtube.com/watch?v=ySTb5Nrhc8g
Support this project on Patreon! https://www.patreon.com/burkeyacademy
Or, a one-time donation on PayPal is appreciated! http://paypal.me/BurkeyAcademy
My Website: http://www.burkeyacademy.com/
Talk to me on my SubReddit: https://www.reddit.com/r/BurkeyAcademy/

Views: 207366
BurkeyAcademy

This webinar provides an overview of basic quantitative analysis, including the types of variables and statistical tests commonly used by Student Affairs professionals. Specifically discussed are the basics of Chi-squared tests, t-tests, and ANOVAs, including how to read an SPSS output for each of these tests.

Views: 21709
CSSLOhioStateU

Decision Tree (CART) - 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
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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Please Like and Subscribe for more videos :)

Views: 158100
Augmented Startups

Errors and residuals are not the same thing in regression.The confusion that they are the same is not surprisingly given the way textbooks out there seem to use the words interchangeably. Let me introduce you then to residuals and the error term.

Views: 50779
Phil Chan

What is Regression Testing:
• Regression testing is the process of testing changes to computer programs to make sure that the older programming still works with the new changes.
• Testing the unchanged features to make sure that it is not broken because of the changes (changes means – addition, modification, deletion or defect fixing)
• Re-execution of same test cases in different builds or releases to make sure that changes (addition, modification, deletion or defect fixing) are not introducing defects in unchanged features.
For ex: We have an application where in there are 3 modules in it. Now after some time client comes back saying they want one more module to be incorporated into the existing application.
Now team will start developing and testing the new module/enhancement. Once the development and testing of the new feature is done, testing team will plan for regression testing to make sure that the new module (module 1) which is added to the application does not affect the existing functionality. Hence functionality of the existing modules (module1, 2 & 3) are also tested before releasing the application.
Types of Regression Testing:
1. Unit Regression testing.
2. Regional Regression testing.
3. Full Regression testing.
Unit Regression Testing or Retesting:
In this case only the part where changes are done is retested. For ex. For a minor defect fix only retesting the defect would be sufficient.
Regional Regression testing:
In this case tester will be able to identify the existing areas that are affected due to the new changes in application and hence tester will test only few existing modules as part of regression.
Full Regression Testing:
This is performed in situations where the enhancements are more and tester cannot identify the existing functionalities that are affected due to these new changes then we go for full Regression testing.
Selecting test cases for regression testing
It was found from industry data that good number of the defects reported by customers were due to last minute bug fixes creating side effects and hence selecting the test case for regression testing is an art and not that easy. Effective Regression Tests can be done by selecting following test cases -
• Test cases which have frequent defects
• Functionalities which are more visible to the users
• Test cases which verify core features of the product
• Test cases of Functionalities which has undergone more and recent changes
• All Integration Test Cases
• All Complex Test Cases
• Boundary value test cases
• Sample of Successful test cases
• Sample of Failure test cases
Disadvantages of doing regression testing manually again and again,
• Monotonous job
• Efficiency drops down
• Test execution time is more
• No consistency in test execution
Thus, we go for Automation to solve this problem. When we have more cycles of Regression testing – we go for Automation.
Difference between Re-testing and Regression Testing.
Re-Testing – developer fixes the bug(or makes some changes) and gives the product for testing. We are testing only the fixed bug (or changed areas) i.e, we are testing only the defect fixes. We are re-validating the defect
Regression testing – we are testing the fixed (or changed areas) and also testing the other areas to check if they are broken.
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Ankpro Training

Learn about Likert Scales in SPSS and how to copy labels from one variable to another in this video. Entering codes for Likert Scales into SPSS is also covered.
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Likert scale SPSS video.
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Video Transcript: In this video we'll take a look at how to enter value labels for a variable which will be review since we've done that before. But then I also want to show you how to apply value labels that were entered for one variable to a number of different variables which can be really useful as it's a great time saver. Here in this data set notice that I have 10 people and I have the variables gender, item 1, 2, 3, 4, and 5. And they answered on what's known as a Likert scale. Now you very well may have heard of a Likert scale before and the first thing is you may have heard of it called LIKE-ERT scale which is very common to call it that but it's actually Likert, so it's pronounced LICK-ERT instead of LIKE-ERT and it was developed by Rensis Likert in the early to middle 1900s he developed the scale. And it's used so commonly, it's used in this 5-point option as you see here, 5 to 1, and we'll talk about that in just a moment. You'll also see it in a 7-point option, it's very commonly used that way. And less commonly so but you'll see it in other ways like 9-point scale and so forth. And it's used with many different kinds of descriptions like definitely true, somewhat true, and so forth; not just agree as you see here. So, in the most traditional use of this scale, which is what we see right here, we have a 5=strongly agree, a 4=agree, 3 is neither agree nor disagree - this is sometimes called neutral - 2 is disagree and then 1 is strongly disagree. On item 1 they would read the following statement: I can turn to others for support when needed. And then what they do is they read that item, they look at these 5 options, and if it's someone who has a lot of support in their network or friendships or what have you, they might answer 5, strongly agree, or 4, agree. And if it's someone who doesn't experience a lot of social support, they might answer a 1 for strongly disagree or a 2 for disagree and so on. So, the first person here in row 1, notice for item 1 they answered a 4, so they answered agree. Item 2 they answered a 5 for strongly agree and so on. If we look down item 1, did anyone answer strongly disagree - let's take a look at that. We're looking for a 1 here, and notice that participant number 9, they answered a 1 on item 1, so they answered strongly disagree, and so on. So what I want to do here is go ahead and enter the value labels for item 1 so we're going to enter these into SPSS that you see here. And then I want to show you how to apply those to the remaining items in a very quick way. First of all, notice that we have gender, if I click on my value labels button here as a review, gender is already coded, I already entered those. But what I don't have entered is item 1, item 2, 3, 4, and 5. And I'd like to go ahead and enter those to have them in the dataset, so if I go back and look at this file at a later time, I'll remember that a 5 corresponded to strongly agree and a 1 corresponded to strongly disagree, so in other words I'll know which direction this scale is scored, and what I mean by that is higher scores indicate greater social support because people strongly agreed with a given item. Whereas lower scores indicated less social support. Since we're looking at entering value labels, let's begin with item 1. So I could either double-click on item 1 or I could go to the variable view tab. Let's go ahead and double-click on item 1 right at the column heading here that's "name". So I double-click on that and notice it takes me to the variable view window. So that's a quick way to get there if you want to access the variable view window. And then we'll go to the "values" column here, click on the "None" cell and then notice the 3 dots appear. So I click on that and then here let's start with
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Likert Scales
Likert
Strongly Agree to Strongly Disagree
Likert in SPSS

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Predicting a quantittive outcome from 2+ predictior variables while controlling for potential confounding-covariate variables.

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TheRMUoHP Biostatistics Resource Channel

Session 18: Descriptive Statistics: Summarising and Visualising Data
Fifth Video

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Likert Scale: http://en.wikipedia.org/wiki/Likert_scale
R: http://www.r-project.org/

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In this lecture, we discuss types of variables in datasets. Introduction to Nominal/Categorical, Ordinal and Interval/Ratio/Continuous variables.
We explore the concepts regarding point projection onto n-dimensional space and cartesian geometry applied onto data analysis.
This playlist provides approximately 10 hours of our Analytics Training series. For more information, please visit www.learnanalytics.in . For enquiries drop an email to [email protected] . The training covers basic business statistics concepts and using tools such as SAS, SPSS , Statistica and R using Rattle.
The objective of the training series is to prepare the student for a career in Data Analysis and the Analytics Industry in general. Please visit our website for further details.
If you wish to subscribe to our full Analytics Training module, please visit http://goo.gl/nIJJHg for our paid Youtube Channel which contains additional hours covering more extensive topics including Linear Regression/Logistic Regression, building and testing predictive models using Logistic / Decision Trees and Ensembling. All videos on our paid channel are available without advertisement interruptions and you can enrol for a 14 day free subscription trial. You pay only if you want to continue.
Additionally, you get access to the datasets discussed in the videos and all SAS Codes.

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Learn Analytics

We often turn to our coping mechanisms when dealing with stressful situations. Here are 10 psychological defense mechanisms that people rely on.
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Psych2Go

►Check the below link for detailed post on "What is Regression Testing. When & How We Do Regression Testing"
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Software Testing Material

How to define variables and enter data into SPSS (v20)
ASK SPSS Tutorial Series

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BrunelASK

This video discusses how to interpret the R-squared and the Regression Standard Error to assess model fit: the model's ability to explain the variance in the dependent variable.
TABLE OF CONTENTS:
00:00 Introduction
00:07 Some things we just can’t explain
01:24 Model Fit
03:08 R-Squared ( R2 )
03:54 R2 Examples
04:52 Plot of Consumption vs. Income Model
05:21 R2 in Regression Result
06:06 Regression Standard Error (RSE)
06:52 R2 and Standard Error Examples
07:35 S.E. in Regression Result
07:55 Summary

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dataminingincae

Data Science & Machine Learning - Method Types in Regression Trees - DIY- 16 -of-50
Do it yourself Tutorial
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Regression trees
rpart(Target ~ Predictors, data = DataSets, method=“Types").
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anova – continuous values.
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exp – exponential - Survival method
Hands On – R Machine Learning Ex-12
Extend the hands-on exercise -11
Implement Regression Trees Model using different methods for target variable - Spend using predictor variables Age, Income, Job, Auto Loan Indicator, Gender, Marital Status.
Calculate Mean Square Error for each method.
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Machine learning, data science, R programming, Deep Learning, Regression, Neural Network, R Data Structures, Data Frame, RMSE & R-Squared, Regression Trees, Decision Trees

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BharatiDWConsultancy

Visual explanation on how to read the Coefficient table generated by SPSS. Includes step by step explanation of each calculated value. Includes explanation plus visual explanation. Includes explanation on how to calculate the betas, standard error and standardized coefficients.
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statisticsfun