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: 179377
George Ingersoll

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: 463690
Eugene O'Loughlin

In this video, I will be talking about a parametric regression method called “Linear Regression” and it's extension for multiple features/ covariates, "Multiple Regression". You will gain an understanding of how to estimate coefficients using the least squares approach (scalar and matrix form) - fundamental for many other statistical learning methods.
If you thought this content was useful, SHARE it with your friend – you know, the one with the stats exam tomorrow and trying to binge watch YouTube tutorials. SUBSCRIBE to my channel for more amazing content!
More on Matrix Calculus: https://atmos.washington.edu/~dennis/MatrixCalculus.pdf

Views: 46930
CodeEmporium

I demonstrate how to perform a multiple regression in SPSS. This is the in-depth video series. I cover all of the main elements of a multiple regression analysis, including multiple R, R squared, model development (via stepwise method), intercept, unstandardized beta weights, standardized beta weights, semi-partial correlation, standard errors, as well as basic heteroscedasticity tests.

Views: 493971
how2stats

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: 142109
Dr. Todd Grande

A guide to solving Anderson Sweeney & Williams 11e Chapter 15 Problem 7, using Microsoft Excel. The dataset is titled "Laptop.xlsx".

Views: 36198
Jason Delaney

Applied Multivariate Statistical Modeling by Dr J Maiti,Department of Management, IIT Kharagpur.For more details on NPTEL visit http://nptel.ac.in

Views: 15752
nptelhrd

This video moves us from simple linear regression to multiple regression. I discuss the differences introduced by increasing the number of regressors, and we cover:
- the multiple regression model
- the regression equation and estimated regression equation
- the least-squares approach
- the SST, SSE, and SSR
- the R-squared and adjusted R-squared

Views: 128742
Jason Delaney

stepwise multiple regression example

Views: 53109
Math Guy Zero

It's easy to run a regression in Excel. The output contains a ton of information but you only need to understand a few key data points to make sense of your regression.
You need the Analysis Toolpak add-in to run regressions. It comes with Excel but you may need to load it if you don't see Data Analysis under the Data toolbar.
Produced by Sara Silverstein
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Views: 10067
BI Excel

This video provides an example of interpreting multiple regression output in excel. The data set comes from Andy Field's "Discovering Statistics Using SPSS" (2009, 3rd Edition).

Views: 326589
TheWoundedDoctor

In this video, I present an example of a multiple regression analysis of website visit duration data using both quantitative and qualitative variables. Variables used include gender, browser, mobile/non-mobile, and years of education. Gender and mobile each require a single dummy variable, while browser requires several dummy variables. I also present models that include interactions between the dummy variables and years of education to analyze intercept effects, slope effects, and fully interacted models. In short, I cover:
- multiple category qualitative variables
- dummy variables
- intercept effects
- slope effects
- dummy interactions
I hope you find it useful! Please let me know if you have any questions!
--Dr. D.

Views: 241500
Jason Delaney

I address the issue of what sample size you need to conduct a multiple regression analysis.

Views: 15911
how2stats

Check out our new Excel Data Analysis text:
https://www.amazon.com/dp/B076FNTZCV
This video illustrates how to perform a multiple regression statistical analysis in Microsoft Excel using the Data Analysis Toolpak.
Multiple Regression
Regression
R-Squared
ANOVA table
Regression Weight
Beta Weight
Predicted Value
YouTube Channel (Quantitative Specialists): https://www.youtube.com/user/statisticsinstructor
Subscribe today!
Video Transcript: and if you recall, if we use an alpha .05, which is what we typically use and we'll also use in this example. If this p-value is less than .05, then that indicates the test is significant. So this value is significant because .0004 is definitely less than .05. So this indicates that the R-squared of .50 is significantly greater than zero. So in other words, the variables SAT score, social support, and gender, once again taken as a group, predict a significant amount of variance in college GPA. And we could write that up as follows. We could say the overall regression model was significant, and then we have F 3, 26 and that comes from right here, 3 and 26, = 8.51, which is the F value here reported in the table, p is less than .001, and I said that because this value is smaller than .001. And I also put the R-squared here. R-squared = .50, and that of course came from right here. So you'll often see results written up like this, in a research article or what have you. So this is one way to express the results of the ANOVA table. So if you're reading a research article on multiple regression and you see this information here, most likely, this first part here is corresponding to the results of the ANOVA table. OK so these first two tables, as I had said earlier, they assess how well our three predictors, taken as a set, did at predicting first-year college GPA. Moving to our last table, this is where we look at the individual predictors. Whether SAT score, on its own, social support, on its own, and gender, once again on its own, are these three variables significant predictors of college GPA. Now it may be that one of them is significant, two of them are, or all three of them are significant, but that's what this table assesses. So as we did before, we'll use alpha .05, once again. So we're going to assess each of these values against .05. And notice that SAT score, this p-value definitely is less than .05, so SAT is significant. Social support, this p-value, while fairly close, is also less than .05, so social support is significant as well. But notice gender, .66, that's definitely not less than .05, so gender is not significant. And that's really not that surprising because males and females don't typically differ significantly in their college GPA, in their first year, or in all four years for that matter. But I wanted to include this variable gender in this model as well, so you can see an example of a non-significant result. So once again this table is looking at the predictors individually, so this indicates here that SAT score is a significant predictor of college GPA, social support is also a significant predictor of college GPA, but gender is not a significant predictor. Now in this table here what we're assessing is whether these predictors account for a significant amount of unique variance in college GPA. So in other words what that means is that SAT scores significantly predicts college GPA, so it accounts for a separate, significant part of college GPA than social support, which is also significant, but it accounts for a unique part of college GPA that SAT does not account for. So if a test is significant here, that means that the variable accounts for a significant amount of variance in college GPA uniquely to itself. And that's an important point to note here, and that's frequently confused with multiple regression. So, a scenario, if these two predictors were completely and perfectly correlated at 1.0, in other words they're really getting at the exact same thing in college GPA, then neither of these would be significant if that was the case, because neither of them would be accounting for any unique information in college GPA whatsoever. They would be totally redundant and they would both not be significant. So if a predictor is significant here, as these both are, then that tells us that they account for a significant amount of unique variance in college GPA. So to wrap it all up here, to summarize, our regression overall was significant as we see that in the ANOVA table, and the amount of variance that was accounted for, when the three predictors were taken as a group, was 50% of the variance, or half of the variance, which was pretty good. When we looked at the predictors individually, SAT score was a significant predictor of college GPA, as was social support, but gender was not significant. This concludes the video on multiple regression in Microsoft Excel. Thanks for watching.

Views: 25836
Quantitative Specialists

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.
The next important point is about communication for launching of my website:
I am very glad to announce a launching of my website related to Lean, Six Sigma and Personality development coaching. Please visit to my website by clicking on this link and add your valuable views and comments regarding it.
https://www.learnandapply.org/
I have also created recommendation page to share secret of my success with you. You can visit this link to review it.
https://www.learnandapply.org/recommondations
For the best audio recording experience, you can visit Samson product by visiting this link:
https://amzn.to/2HkvFva
For better management of your time and skills, you must read 'Managing Oneself' book:
https://amzn.to/2Hmf0Ht
I am sure you will like this video as well as initiatives I have started.

Views: 149374
LEARN & APPLY: Lean & Six Sigma

Tutorial on how to calculate Multiple Linear Regression using SPSS. I show you how to calculate a regression equation with two independent variables. I also show you how to create a Pearson r correlation matrix using output from SPSS.
Playlist on Using SPSS For Multiple Linear Regression
http://www.youtube.com/playlist?list=PLWtoq-EhUJe2Z8wz0jnmrbc6S3IwoUPgL
Like MyBookSucks on Facebook at
http://www.MyBookSucks.Com/PartyMoreStudyLess
Created by David Longstreet, Professor of the Universe, MyBookSucks
http://www.linkedin.com/in/davidlongstreet

Views: 198186
statisticsfun

We take the model that was presented in the first video and run the linear regression in excel. We look at the p values and do hypothesis testing. We then extend the model to a multiple regression model.

Views: 112883
pomscm

Tutorial introducing the idea of linear regression analysis and the least square method. Typically used in a statistics class.
Playlist on Linear Regression
http://www.youtube.com/course?list=ECF596A4043DBEAE9C
Like us on: http://www.facebook.com/PartyMoreStudyLess
Created by David Longstreet, Professor of the Universe, MyBookSucks
http://www.linkedin.com/in/davidlongstreet

Views: 740737
statisticsfun

Regression Analysis by Dr. Soumen Maity,Department of Mathematics,IIT Kharagpur.For more details on NPTEL visit http://nptel.ac.in

Views: 43962
nptelhrd

Course web page: http://web2.slc.qc.ca/pcamire/

Views: 505995
[email protected]

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

Views: 346585
dataminingincae

Multiple Linear Regression Model in R; Fitting the model and interpreting the outcomes!
Practice Dataset: (https://bit.ly/2rOfgEJ); Linear Regression Concept and with R (https://bit.ly/2z8fXg1)
More Statistics and R Programming Tutorial (https://goo.gl/4vDQzT)
Learn how to fit and interpret output from a multiple linear regression model in R and produce summaries.
▶︎ You will learn to use "lm", "summary", "cor", "confint" functions.
▶︎ You will also learn to use "plot" function for producing residual and QQ plots in R.
▶︎ We recommend that you first watch our video on simple linear regression concept (https://youtu.be/vblX9JVpHE8) and in R (https://youtu.be/66z_MRwtFJM)
▶︎▶︎Download the dataset here: https://statslectures.com/r-scripts-datasets
▶︎▶︎Like to support us? You can Donate https://statslectures.com/support-us or Share our Videos and help us reach more people!
◼︎ Table of Content:
0:00:07 Multiple Linear Regression Model
0:00:32 How to fit a linear model in R? using the "lm" function
0:00:36 How to access the help menu in R for multiple linear regression
0:01:06 How to fit a linear regression model in R with two explanatory or X variables
0:01:19 How to produce and interpret the summary of linear regression model fit in R
0:03:16 How to calculate Pearson's correlation between the two variables in R
0:03:26 How to interpret the collinearity between two variables in R
0:03:49 How to create a confidence interval for the model coefficients in R? using the "confint" function
0:03:57 How to interpret the confidence interval for our model's coefficients in R
0:04:13 How to fit a linear model using all of the X variables in R
0:04:27 how to check the linear regression model assumptions in R? by examining plots of the residuals or errors using the "plot(model)" function
►► Watch More:
►Linear Regression Concept and with R https://bit.ly/2z8fXg1
►R Tutorials for Data Science https://bit.ly/1A1Pixc
►Getting Started with R (Series 1): https://bit.ly/2PkTneg
►Graphs and Descriptive Statistics in R (Series 2): https://bit.ly/2PkTneg
►Probability distributions in R (Series 3): https://bit.ly/2AT3wpI
►Bivariate analysis in R (Series 4): https://bit.ly/2SXvcRi
►Linear Regression in R (Series 5): https://bit.ly/1iytAtm
►ANOVA Concept and with R https://bit.ly/2zBwjgL
►Linear Regression Concept and with R https://bit.ly/2z8fXg1
► Intro to Statistics Course: https://bit.ly/2SQOxDH
►Statistics & R Tutorials: Step by Step https://bit.ly/2Qt075y
This video is a tutorial for programming in R Statistical Software for beginners, using RStudio.
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Our Team:
Content Creator: Mike Marin (B.Sc., MSc.) Senior Instructor at UBC.
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These videos are created by #marinstatslectures to support some courses at The University of British Columbia (UBC) (#IntroductoryStatistics and #RVideoTutorials for Health Science Research), although we make all videos available to the everyone everywhere for free.
Thanks for watching! Have fun and remember that statistics is almost as beautiful as a unicorn!

Views: 218249
MarinStatsLectures- R Programming & Statistics

Learn via an example how to regress data to a straight line. For more videos and resources on this topic, please visit http://nm.mathforcollege.com/topics/linear_regression.html

Views: 48121
numericalmethodsguy

I address the issue of what sample size you need to conduct a multiple regression analysis. (Part 2)

Views: 8313
how2stats

Introduction to multiple regression in r. The data set is discussed and exploratory data analysis is performed here using correlation matrix and scatterplot matrix.

Views: 41457
Jalayer Academy

https://github.com/codebasics/py/tree/master/ML/2_linear_reg_multivariate
(Exercise is at the end of the ipynb notebook so just open that file and read through)
In this machine learning tutorial with python, we will write python code to predict home prices using multivariate linear regression in python (using sklearn linear_model). Home prices are dependant on 3 independant variables: area, bedrooms and age. Pandas dataframe is used to fill missing values first and then use that dataset to train a multivariate regression model.You can use exercise at the end to consolidate your understanding on whatever you have learnt in this machine learning tutorial.
Website: http://codebasicshub.com/
Facebook: https://www.facebook.com/codebasicshub
Twitter: https://twitter.com/codebasicshub
Google +: https://plus.google.com/106698781833798756600

Views: 21911
codebasics

For introductory statistics. Apologies for the background music, and for the fact that I will never have time to re-record this.
The dataset can be found here: https://drive.google.com/file/d/12wk1zlFiJmw6d1gUm3Dh4TORkE2cWt97/view?usp=sharing
It orginates from "Introduction to Linear Regression Analysis" by Montgomery, et. al.

Views: 52955
Matthias Kullowatz

This Linear Regression in Machine Learning video will help you understand the basics of Linear Regression algorithm - what is Linear Regression, why is it needed and how Simple Linear Regression works with solved examples, Linear regression analysis, applications of Linear Regression and Multiple Linear Regression model. At the end, we will implement a use case on profit estimation of companies using Linear Regression in Python. This Machine Learning tutorial is ideal for beginners who want to understand Data Science algorithms as well as Machine Learning algorithms.
Below topics are covered in this Linear Regression Machine Learning Tutorial:
1. Introduction to Machine Learning
2. Machine Learning Algorithms
3. Applications of Linear Regression
4. Understanding Linear Regression
5. Multiple Linear Regression
6. Usecase - Profit estimation of companies
What is Machine Learning: Machine Learning is an application of Artificial Intelligence (AI) that provides systems the ability to automatically learn and improve from experience without being explicitly programmed.
Subscribe to our channel for more Machine Learning Tutorials: https://www.youtube.com/user/Simplilearn?sub_confirmation=1
Machine Learning Articles: https://www.simplilearn.com/what-is-artificial-intelligence-and-why-ai-certification-article?utm_campaign=Linear-Regression-NUXdtN1W1FE&utm_medium=Tutorials&utm_source=youtube
To gain in-depth knowledge of Machine Learning, check our Machine Learning certification training course: https://www.simplilearn.com/big-data-and-analytics/machine-learning-certification-training-course?utm_campaign=Linear-Regression-NUXdtN1W1FE&utm_medium=Tutorials&utm_source=youtube
#MachineLearningAlgorithms #Datasciencecourse #DataScience #SimplilearnMachineLearning #MachineLearningCourse
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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.
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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
- - - - - - -
Who should take this Machine Learning Training Course?
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
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For more updates on courses and tips follow us on:
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Get the Android app: http://bit.ly/1WlVo4u
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Views: 51678
Simplilearn

This video provides a simple example of doing multiple linear regression analysis in R and includes,
- developing a linear model
- comparing full and reduced model using ANOVA
- Prediction
- Confidence interval
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: 31159
Bharatendra Rai

Introduction to Quality Science : Minitab Video Tutorials

Views: 32863
Dragonfly Statistics

Views: 7962
Statistics

Regression Line Example
Watch the next lesson: https://www.khanacademy.org/math/probability/regression/regression-correlation/v/second-regression-example?utm_source=YT&utm_medium=Desc&utm_campaign=ProbabilityandStatistics
Missed the previous lesson?
https://www.khanacademy.org/math/probability/regression/regression-correlation/v/proof-part-4-minimizing-squared-error-to-regression-line?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: 677237
Khan Academy

Views: 8473
ProfThacker

A short tutorial on calculating a multiple regression in SPSS (also known as PASW) using the simple defaults. Also compares results with bivariate correlations in earlier tutorial.

Views: 324826
Barton Poulson

This video documents how to perform a multivariate regression in Excel.

Views: 22751
GSBS 6002

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: 332625
Christoph Scherber

This video demonstrates how to conduct and interpret a multiple linear regression (multiple regression) using Microsoft Excel data analysis tools. Multiple regressions return the contribution of multiple predictor variables on one outcome variable. Predicted values for the outcome variable are calculated using the estimated regression equation.

Views: 28028
Dr. Todd Grande

Links of Data set and case study used in the above video.
1.https://drive.google.com/open?id=1nGSNNDfc7iNvHjCS_K5hhWKa1sWdJciX
2.https://drive.google.com/open?id=1ItFLk_EDQly6eVbqACEP7z8mTgMLmlUO
3.https://drive.google.com/open?id=1w-s5obJ0Dpxvz7gLKgbOTB6kcwiA7ccy

Views: 3726
Dr. Shailesh Kaushal

From an existing multiple regression output produced with Excel 2007, I show you how to make point predictions and approximate 95% prediction intervals. The basic package of Excel does not have a routine for making predictions intervals, so I suggest a method of inflating the residual standard deviation statistic by 10% to get an approximate standard error of prediction.

Views: 128857
ProfTDub

This video explains how hypothesis testing works in practice, using a particular example. Check out https://ben-lambert.com/econometrics-course-problem-sets-and-data/ for course materials, and information regarding updates on each of the courses. Quite excitingly (for me at least), I am about to publish a whole series of new videos on Bayesian statistics on youtube. See here for information: https://ben-lambert.com/bayesian/ Accompanying this series, there will be a book: https://www.amazon.co.uk/gp/product/1473916364/ref=pe_3140701_247401851_em_1p_0_ti

Views: 67429
Ben Lambert

WHAT IS REGRESSION ANALYSIS WITH EXAMPLES IN HINDI

Views: 24484
LearnEveryone

How to use R to calculate multiple linear regression.
http://www.MyBookSucks.Com/R/Multiple_Linear_Regression.R
http://www.MyBookSucks.Com/R
Playlist on on Understanding Multiple Linear Regression Results (Watch videos 2 - 4)
http://www.youtube.com/playlist?list=PLWtoq-EhUJe2Z8wz0jnmrbc6S3IwoUPgL

Views: 56865
statisticsfun

An example on how to calculate R squared typically used in linear regression analysis and least square method.
Like us on: http://www.facebook.com/PartyMoreStudyLess
Link to Playlist on Linear Regression:
http://www.youtube.com/course?list=ECF596A4043DBEAE9C
Link to Playlist on SPSS Multiple Linear Regression:
http://www.youtube.com/playlist?list=PLWtoq-EhUJe2Z8wz0jnmrbc6S3IwoUPgL
Created by David Longstreet, Professor of the Universe, MyBookSucks
http://www.linkedin.com/in/davidlongstreet

Views: 365457
statisticsfun

http://thedoctoraljourney.com/ This tutorial defines a bivariate linear regression, provides examples for when this analysis might be used by a researcher, walks through the process of conducting this analysis, and discusses how to set up an SPSS file and write an APA results section for this analysis.
For more statistics, research and SPSS tools, visit http://thedoctoraljourney.com/.

Views: 13882
The Doctoral Journey

Linear Regression Analysis, (ANOVA) Analysis Of Variance, R-Squared & F-Test, applying to a regression example, understanding the variance testing between total squared error,
explained squared error & residuals squared which is not explained, explaining how to calculate the degrees of freedom, calculating F test based on R-Squared value, etc., detailed discussion by Allen Mursau

Views: 52472
Allen Mursau

Views: 34244
James Donald

** Machine Learning Training with Python: https://www.edureka.co/python **
This Linear Regression Algorithm video is designed in a way that you learn about the algorithm in depth. This video is designed in a way that in the first part you will learn about the algorithm from scratch with its mathematical implementation, then you will drill down to the coding part and implement linear regression using python. Below are the topics covered in this tutorial:
1. What is Regression?
2. Regression Use-case
3. Types of Regression – Linear vs Logistic Regression
4. What is Linear Regression?
5. Finding best-fit regression line using Least Square Method
6. Checking goodness of fit using R squared Method
7. Implementation of Linear Regression Algorithm using Python (from scratch)
8. Implementation of Linear Regression Algorithm using Python (scikit lib)
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How it Works?
1. This is a 5 Week Instructor led Online Course,40 hours of assignment and 20 hours of project work
2. We have a 24x7 One-on-One LIVE Technical Support to help you with any problems you might face or any clarifications you may require during the course.
3. At the end of the training you will be working on a real time project for which we will provide you a Grade and a Verifiable Certificate!
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About the Course
Edureka’s Machine Learning Course using Python is designed to make you grab the concepts of Machine Learning. The Machine Learning training will provide deep understanding of Machine Learning and its mechanism. As a Data Scientist, you will be learning the importance of Machine Learning and its implementation in python programming language. Furthermore, you will be taught Reinforcement Learning which in turn is an important aspect of Artificial Intelligence. You will be able to automate real life scenarios using Machine Learning Algorithms. Towards the end of the course, we will be discussing various practical use cases of Machine Learning in python programming language to enhance your learning experience.
After completing this Machine Learning Certification Training using Python, you should be able to:
Gain insight into the 'Roles' played by a Machine Learning Engineer
Automate data analysis using python
Describe Machine Learning
Work with real-time data
Learn tools and techniques for predictive modeling
Discuss Machine Learning algorithms and their implementation
Validate Machine Learning algorithms
Explain Time Series and it’s related concepts
Gain expertise to handle business in future, living the present
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Why learn Machine Learning with Python?
Data Science is a set of techniques that enable the computers to learn the desired behavior from data without explicitly being programmed. It employs techniques and theories drawn from many fields within the broad areas of mathematics, statistics, information science, and computer science. This course exposes you to different classes of machine learning algorithms like supervised, unsupervised and reinforcement algorithms. This course imparts you the necessary skills like data pre-processing, dimensional reduction, model evaluation and also exposes you to different machine learning algorithms like regression, clustering, decision trees, random forest, Naive Bayes and Q-Learning.
For more information, Please write back to us at [email protected] or call us at IND: 9606058406 / US: 18338555775 (toll free).
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