Search results “Linear regression analysis download”

Download file from “Highline BI 348 Class” section: https://people.highline.edu/mgirvin/excelisfun.htm
Learn:
1) (00:11) Forecasting using Regression when we see a trend and belief the trend will extend into the future. Will will predict outside the Experimental Region with the Assumption is that trend continues into future.
2) (00:53) Forecast a Trend using Simple Liner Regression. We use the Data Analysis Regression Feature.
3) (03:22) Learn how to use FORECAST function.
4) (08:57) Forecast a Seasonal Pattern using Multiple Regression and three Categorical Variables for quarter using Multiple Linear Regression. We use the Data Analysis Regression Feature.
5) (12:12) VLOOKUP & MATCH functions with Mixed Cell References to populate new categorical variable columns with the Boolean ones and zeroes.
6) (19:53) Forecast a Trend with a Seasonal Pattern using Multiple Regression and three Categorical Variables for quarter and one quantitative variable using Multiple Linear Regression. We use the Data Analysis Regression Feature.
7)
Download Excel File Not: After clicking on link, Use Ctrl + F (Find) and search for “Highline BI 348 Class” or for the file name as seen at the beginning of the video.

Views: 60027
ExcelIsFun

Step-by-step Example of Running a Regression: we learn to do regression using R statistics software. I'll walk through the code for running a multivariate regression - plus we'll run a number of slightly more complicated examples to ensure it's all clear.
Outline:
• We will walk through the R-Software Code to run a multivariate regression.
• Talk about the lm() Function in R
• We'll discuss complicating our regression model a bit
--o Add an Interaction Effect
--o Add a quadratic variable to a regression.
How to interpret regression coefficient results: https://sites.google.com/site/curtiskephart/ta/econ113/interpreting-beta
______________________
More Econometrics Resources: https://sites.google.com/site/curtiskephart/ta/econ113
R-Code: http://bit.ly/Ta5LHl
Data: https://sites.google.com/site/curtiskephart/data/wagedata.csv
How to load data: http://www.youtube.com/watch?v=VLtazaiYo-c
Download R: http://www.r-project.org/

Views: 39501
economicurtis

Download file from “Highline BI 348 Class” section: https://people.highline.edu/mgirvin/excelisfun.htm
Learn about how to automatically creates statistics for Linear Regression using the Data Analysis Regression feature. See how to automatically create statistics such as: Correlation, R Squared, Standard Error, Slope, Intercept, SST, SSR, SSE, F Test, Test Statistic, t Test Statistics, p-values, predicted vales, residuals, Residual plots and more.
Install: File, Options, Add-ins, Data Analysis Toolpak.
Download Excel File Not: After clicking on link, Use Ctrl + F (Find) and search for “Highline BI 348 Class” or for the file name as seen at the beginning of the video.

Views: 24181
ExcelIsFun

OnRamps students: you can download the data set that is used in this tutorial by following this link: https://onramps.instructure.com/courses/1825963/files/89410493/download?wrap=1

Views: 1024
OnRamps Computer Science

http://zavodilo.com/en/mt4

Views: 390
Metatrader

Download excel file to go with video: http://www.codible.com/pages/84
Analyze stock price data using Microsoft Excel to plot returns, and plot a regression line between the stock returns.
Some good books on Excel and Finance:
Financial Modeling - by Benninga:
http://amzn.to/2tByGQ2
Principles of Finance with Excel - by Benninga:
http://amzn.to/2uaCyo6

Views: 74568
Codible

Download file from “Highline BI 348 Class” section: https://people.highline.edu/mgirvin/excelisfun.htm
Learn How to do Hypothesis Testing to Test the significance to a linear relationship using the Data Analysis Regression feature.
Download Excel File Not: After clicking on link, Use Ctrl + F (Find) and search for “Highline BI 348 Class” or for the file name as seen at the beginning of the video.

Views: 13396
ExcelIsFun

made with ezvid, free download at http://ezvid.com Simple linear regression using Data Analysis Add In in Excel 2010

Views: 626
Alex Vernon

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: 635612
statisticsfun

In this tutorial on Python for Data Science, You will learn about Multiple linear regression Model using Scikit learn and pandas in Python. You will learn about how to check missing data and Correlation.
This is the 30th Video of Python for Data Science Course! In This series I will explain to you Python and Data Science all the time! It is a deep rooted fact, Python is the best programming language for data analysis because of its libraries for manipulating, storing, and gaining understanding from data. Watch this video to learn about the language that make Python the data science powerhouse. Jupyter Notebooks have become very popular in the last few years, and for good reason. They allow you to create and share documents that contain live code, equations, visualizations and markdown text. This can all be run from directly in the browser. It is an essential tool to learn if you are getting started in Data Science, but will also have tons of benefits outside of that field. Harvard Business Review named data scientist "the sexiest job of the 21st century." Python pandas is a commonly-used tool in the industry to easily and professionally clean, analyze, and visualize data of varying sizes and types. We'll learn how to use pandas, Scipy, Sci-kit learn and matplotlib tools to extract meaningful insights and recommendations from real-world datasets.
Download Link for Cars Data Set:
https://www.4shared.com/s/fWRwKoPDaei
Download Link for Enrollment Forecast:
https://www.4shared.com/s/fz7QqHUivca
Download Link for Iris Data Set:
https://www.4shared.com/s/f2LIihSMUei
https://www.4shared.com/s/fpnGCDSl0ei
Download Link for Snow Inventory:
https://www.4shared.com/s/fjUlUogqqei
Download Link for Super Store Sales:
https://www.4shared.com/s/f58VakVuFca
Download Link for States:
https://www.4shared.com/s/fvepo3gOAei
Download Link for Spam-base Data Base:
https://www.4shared.com/s/fq6ImfShUca
Download Link for Parsed Data:
https://www.4shared.com/s/fFVxFjzm_ca
Download Link for HTML File:
https://www.4shared.com/s/ftPVgKp2Lca

Views: 15725
TheEngineeringWorld

Download file from “Highline BI 348 Class” section: https://people.highline.edu/mgirvin/excelisfun.htm
Learn:
1) (00:13) Estimated Multiple Regression Equation.
2) (00:58) Example 1: Predict Annual Credit Card Charges based on Annual Income (x2) and Number of Years Post High School Eduction (x2)
3) (07:31) Example 2: Predict Risk of Stroke based on Age (x1), Blood Pressure (x2) and Smoking (Categorical Variable) (X3)
Download Excel File Not: After clicking on link, Use Ctrl + F (Find) and search for “Highline BI 348 Class” or for the file name as seen at the beginning of the video.

Views: 7291
ExcelIsFun

The Multiple Linear Regression video series is available for FREE as an iTune book for download on the iPad. The ISBN number is 978-1-62407-066-6. The title is "Multiple Linear Regression." Waller and Lumadue are the authors. The iTune text provides accompanying narrative and the SPSS readouts used in the video series.
The textbook can be obtained from:
https://itunes.apple.com/us/book/multiple-linear-regression/id657084933?ls=1
This video examines the process for writing research questions for a multiple linear regression analysis.

Views: 7633
Lee Rusty Waller

Download the data set here: https://drive.google.com/open?id=0Bz9Gf6y-6XtTVFFwcFdtZk5IUGs

Views: 373
The Data Science Show

The data set used in this video is taken from my book 'Six Sigma Statistics using Minitab 17'. You can work along with the video by downloading the data set from www.rmksixsigma.com.

Views: 7149
RMK Six Sigma

This video can be used in conjunction with the "Multiple Regression - The Basics" video (http://youtu.be/rKQzjjWHm_A).
In this video, I show you how to check multiple regression assumptions in a few steps using IBM SPSS.
Although it is not exactly the same as SPSS, you can download a free program, PSPP, that is similar to SPSS: https://www.gnu.org/software/pspp/get.html. It is close enough to SPSS that you should be able to follow along with this video using PSPP.
I used materials from the following books for this video:
a. Lind, D, Marchal, W, & Wathen, S. (2012). Statistical Techniques in Business and Economics (15th Edition). Boston: McGraw-Hill. ISBN-13: 978-0-07-340180-5 (Textbook web resources: http://www.mhhe.com/lind15e)
b. Field, A. (2013). Discovering Statistics Using IBM SPSS Statistics (4th Edition). London: Sage Publications Ltd. ISBN-13: 978-1446249185
To add the ability to increase the playback speed of YouTube videos, go to the link below and click on the link to request the HTML5 viewer. It will allow you to change the speed of playback by clicking on the gear icon in the bottom right of your YouTube video screen (the same gear you use to change the quality). You should do this - playing my videos at 1.5 speed makes them seem better. :+)
https://www.youtube.com/html5

Views: 80152
weislearners

The Multiple Linear Regression video series is available for FREE as an iTune book for download on the iPad. The ISBN number is 978-1-62407-066-6. The title is "Multiple Linear Regression." Waller and Lumadue are the authors. The iTune text provides accompanying narrative and the SPSS readouts used in the video series.
The textbook can be obtained from:
https://itunes.apple.com/us/book/multiple-linear-regression/id657084933?ls=1
This video introduces the concept of multiple linear regression

Views: 32529
Lee Rusty Waller

Linear regression is the single most useful method in any analyst's toolbox. jamovi makes it easy to conduct both simple and sophisticated regression analyses. To download the free course files, visit https://datalab.cc/tools/jamovi. Thanks for visiting and let us know what you'd like to see!

Views: 169
datalabcc

Excel Techniques for Statistics DGGB 6820 - Summer 2010
Multiple Regression... a lot more X...
(Please be aware that the audio is a little out of sync)
Download the Excel Workbook at:
https://docs.google.com/leaf?id=0B3MhRfj-HDlqNTA0NDhmYWMtN2I5My00ZTJkLWJiNjYtZTE2MjEyMmU5NDZl&hl=en&authkey=CPaHl8kI
Download the Stats Notes at:
https://docs.google.com/viewer?a=v&pid=explorer&chrome=true&srcid=0B3MhRfj-HDlqNGNhNDc2ZWUtOTg4MS00OWFlLTk4NjgtMmE0OGIyYTkxNDRh&hl=en&authkey=CPizo4IN

Views: 57052
FordhamStats

Part 10 of my series about the statistical programming language R! In this video I show how a linear regression line can be added to your data-plot. Also I show how you can add lines to your plot manually. Finally you will learn how to generate normal-distributed random values and a line will be generated that fits those random numbers best.

Views: 174672
Tutorlol

From example on pg. 203 of the course packet.
To follow along, download the excel workbook from Blackboard.

Views: 1337
AEM3100BusStats

A demonstration of how to transform a dependent variable to meet the normality requirements of linear regression analysis, and perform a linear regression, all using KyPlot, a freeware graphing and statistics program; Download KyPlot free here: http://www.pricelesswarehome.org/WoundedMoon/win32_freeware.html

Views: 1525
Brandon Schamp

Create your account and use your FREE daily pass now:
https://analyze.intellectusstatistics.com/create_account/?key=dTN5qHEd9YT0ESKS
Conduct and interpret a regression analysis in seconds using Intellectus Statistics. Students no longer need to struggle through spss tutorials, and spend hours trying to decipher spss output.
Intellectus Statistics provides output as a narrative interpretation of the analysis conducted in English prose, complete with APA tables and figures.
Try it for yourself with a one day pass. Upload your data, conduct your regression analysis, and download your fully interpreted editable word document.
Don't have data? No problem, we provide example data sets for you to practice with, learn the application, and learn how to conduct and interpret your analysis.
Get you daily pass here, and try it out for yourself: https://analyze.intellectusstatistics.com/create_account/?key=dTN5qHEd9YT0ESKS
Don't have data? Don't worry! We provide example data sets already uploaded.

Views: 708
Intellectus Statistics

Download StatPlus here: http://www.analystsoft.com/en/products/statplusmacle/

Views: 2615
julianlecalvez

Create your account and purchase your 2-day pass for only $5.25: https://analyze.intellectusstatistics.com/create_account/?key=m8KzB4FSoaHubjZn
Conduct and interpret a regression analysis in seconds using Intellectus Statistics. Students no longer need to struggle through spss tutorials, and spend hours trying to decipher spss output.
Intellectus Statistics provides output as a narrative interpretation of the analysis conducted in English prose, complete with APA tables and figures.
Try it for yourself with a one day pass. Upload your data, conduct your regression analysis, and download your fully interpreted editable word document.
Don't have data? No problem, we provide example data sets for you to practice with, learn the application, and learn how to conduct and interpret your analysis.
Get you daily pass here, and try it out for yourself: https://analyze.intellectusstatistics.com/create_account/?key=m8KzB4FSoaHubjZn
Don't have data? Don't worry! We provide example data sets already uploaded.

Views: 164
Intellectus Statistics

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: 371076
Eugene O'Loughlin

http://alphabench.com/data/linear-regression.html
Demonstration of linear regression in Excel using the data analysis toolpak, with a discussion of the output generated from the regression tool. This video was shot in Excel 2007, but the technique is the same for Excel 2008, 2010, 2013 and 2016. All statistics and interpretations are the same regardless of Excel version. It would work for Excel 2011 if Microsoft hadn't removed the Data Analysis Toolpak from that version. By the way, if you are trying to do this in Excel 2011 for MAC OS you can download a free companion software called StatPlusLE.
Linear regression is one of the most common statistical techniques in use for making predictions and forecasting behavior.
This Tutorial explains the notable statistics and how to use the linear model in making predictions.
See our visual take on regression:
http://alphabench.com/data/visual-linear-regression.html

Views: 106011
Matt Macarty

Mplus Short Course Topic 11: Regression and Mediation Analysis
Part 1 - Linear Regression with an Interaction
Link to handouts associated with this segment (slides 7-11):
http://www.statmodel.com/download/Aug16_JH_Slides.zip
NOTE: For more information or to engage in discussion about the topics covered in this video, please visit www.statmodel.com.

Views: 1704
Mplus

Learn how to include a categorical variable (a factor or qualitative variable) in a regression model in R. You will also learn how to interpret the model coefficients. The video provides a tutorial for programming in R Statistical Software for beginners.
You can access and download the "LungCapData" dataset here:
Excel format: https://bit.ly/LungCapDataxls
Tab Delimited Text File: https://bit.ly/LungCapData
Here is a brief overview of the topics addressed in this video:

Views: 62435
MarinStatsLectures-R Programming & Statistics

This video demonstrates how to use Andrew Hayes' Process macro to test for and probe a significant interaction effect when carrying out moderated multiple regression.
Data for this demonstration can be obtained here: https://drive.google.com/open?id=1Ymd-hdEbnQcbpNlyrmw8F_KV0w46Ijb8
Go here to download a copy of the Process macro to follow along with the video: http://www.processmacro.org/download.html
You can download an instructional Powerpoint on the topic here as well: https://drive.google.com/file/d/1u4XuS1e80dp8P0-1l8T4IlxoCJfz_qdC/view

Views: 4830
Mike Crowson

Download file: https://people.highline.edu/mgirvin/ExcelIsFun.htm
1. Formula for slope is derived from the expression minSUM(y observed value -- y Predicted value)^2 using differential calculus. See text page 667.
2. Calculate Slope and Y-Intercept for Regression Line long hand.
3. Calculate Slope using the SLOPE Function
4. Calculate the y-Intercept using the INTERCEPT Function
5. Slope = Rise Over Run = For every one unit of x, how far does y move?
6. Y-intercept = y value where x = zero. = point at which line crosses axis
7. Use slope and y-intercept to create estimated simple linear regression equation (line or model)
8. From sample data, the slope and y-intercept are point estimates for the population parameters for slope and y-intercept
9. Use estimated simple linear regression line to make predictions
10. Be careful when making predictions with the estimated simple linear regression equation (line or model) when the x values are outside the range of the sample data. Why? Because the data may show a linear relationship over the range of sample data, but may show some other relationship outside that sampled range.
11. See how to use FORECAST function to make predictions.
This is for the Highline Community College Busn 210 Statistical Analysis for Business and Economics taught by Michael Girvin

Views: 37037
ExcelIsFun

Website + download source code @ http://www.zaneacademy.com | Linear Regression w/ Python & Normal Equation (Tutorial 01) @ https://youtu.be/lEMk6n7rUqo

Views: 996
zaneacademy

Download file: https://people.highline.edu/mgirvin/ExcelIsFun.htm
Topics:
1. Plotting Two variables: Don't use Line Chart, Use Scatter Chart
2. Plotting the point on the chart that graphs the relationship between two variables: Move along x axis a given amount and then along the y axis a certain amount.
3. Independent, Predictor Variable = x
4. Dependent, Predicted Variable = y
5. Scatter Diagram with proper x and y axis labels to see if there is a relationship between two variables.
6. Direct, Positive Relationship: As x increases, y increases
7. Indirect, Negative Relationship: As x increases, y decreases
8. No relationship: no pattern can be seen
9. Add Trendline with linear equation and coefficient of determination (goodness of fit: of the total variation, how much can model explain?)
This is for the Highline Community College Busn 210 Statistical Analysis for Business and Economics taught by Michael Girvin

Views: 50225
ExcelIsFun

Simple Linear Regression in R ; For more Statistics and R Programming Tutorials: https://goo.gl/4vDQzT; Simple Linear Regression Concept and Terminology: https://goo.gl/VhWmVD ;Dataset: https://goo.gl/tJj5XG
How to fit a Linear Regression Model in R, Produce Summaries and ANOVA table for it.
◼︎ What to Expect in this R Tutorial:
►In this R video tutorial you will learn When to use a regression model, and how to use the “lm” command in R to fit a linear regression model for your data
► Here you will also learn to produce summaries for your regression model using “summary” command in R statistics software; these summaries can include intercept, test statistic, p value, and estimates of the slope for your linear regression model
► in this tutorial, you will also become familiar with the Residual Error: a measure of the variation of observations in regression line
► You will also learn to ask R programming software for the attributes of the simple linear regression model using "attributes" command, extract certain attributes from the regression model using the dollar sign ($), add a regression line to a plot in R using "abline" command and change the color or width of the regression line.
► this R tutorial will show you how to get the simple linear regression model's coefficient using the "coef" command or produce confidence intervals for the regression model using "confint" commands; moreover, you will learn to change the level of confidence using the "level" argument within the "confint" command.
►You will also learn to produce the ANOVA table for the linear regression model using the "anova" command, explore the relationship between ANOVA table and the f-test of the regression summary, and explore the relationship between the residual standard error of the linear regression summary and the square root of the mean squared error or mean squared residual from the ANOVA table.
► ►You can access and download the dataset here:
https://statslectures.com/r-stats-datasets
► ► Watch this Statistics Tutorial on the concept and terminology for Simple Linear Regression Model https://youtu.be/vblX9JVpHE8
◼︎ Table of Content:
0:00:07 When to fit a simple linear regression model?
0:01:11 How to fit a linear regression model in R using the "lm" command
0:01:14 How to access the help menu in R for any command
0:01:36 How to let R know which variable is X and which one is Y when fitting a regression model
0:01:45 How to ask for the summary of the simple linear regression model in R including estimates for intercept, test statistic, p-values and estimates of the slope.
0:02:27 Residual standard error (residual error) in R
0:02:53 How to ask for the attributes of the simple linear regression model in R
0:03:06 How to extract certain attributes from the simple linear regression model in R
0:03:40 How to add a regression line to a plot in R
0:03:52 How to change the color or width of the regression line in R
0:04:07 How to get the simple linear regression model's coefficient in R
0:04:11 How to produce confidence intervals for model's coefficients in R
0:04:21 How to change the level of confidence for model's coefficients in R
0:04:38 How to produce the ANOVA table for the linear regression in R
0:04:47 Explore the relationship between ANOVA table and the f-test of the linear regression summary
0:04:55 Explore the relationship between the residual standard error of the linear regression summary and the square root of the mean squared error or mean squared residual from the ANOVA table
♠︎♣︎♥︎♦︎To learn more:
Subscribe: https://goo.gl/4vDQzT
website: http://statslectures.com
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Our Team:
Content Creator: Mike Marin (B.Sc., MSc.) Senior Instructor at UBC.
Producer: Ladan Hamadani (B.Sc., BA., MPH)
These #RTutorial are created by #marinstatslectures to support the statistics course (#SPPH400) at The University of British Columbia(UBC) although we make all videos available to the public for free.

Views: 179289
MarinStatsLectures-R Programming & Statistics

A brief video demonstrating how to run Multiple Regression analysis in SigmaXL Version 6.1. This example is taken from the SigmaXL Version 6.1 Workbook - P. 221.
Download the SigmaXL Version 6.1 Workbook here:
http://www.sigmaxl.com/New/Release/6.1/SigmaXL%20Version%206.1%20Workbook.zip

Views: 1593
SigmaXL Inc.

Learn how to include interaction or effect modification in a regression model in R. You will also learn how to interpret the model coefficients. The video provides a tutorial for programming in R Statistical Software for beginners.
You can access and download the "LungCapData" dataset here:
Excel format: https://bit.ly/LungCapDataxls
Tab Delimited Text File: https://bit.ly/LungCapData
You can access and download the R code here:
Click here to open with R : http://bit.ly/1H4RLWr
Click here for the text file: http://bit.ly/1JK7QXF

Views: 74708
MarinStatsLectures-R Programming & Statistics

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" commands. You will also learn the "plot" command for producing residual and QQ plots. It will be helpful to first review our video on simple linear regression. The video provides a tutorial for programming in R Statistical Software for beginners.
You can access and download the "LungCapData" dataset here:
Excel format: https://bit.ly/LungCapDataxls
Tab Delimited Text File: https://bit.ly/LungCapData
Here is a quick overview of the topic addressed in this video:
0:00:07 why use Multiple Linear Regression Model
0:00:32 using the "lm" command to fit a linear model
0:00:36 how to access the help menu in R for multiple linear regression by typing "help"
0:01:06 fitting a linear regression model using Age and Height as the explanatory or X variables
0:01:19 producing and interpreting the summary of linear regression model fit in R
0:03:16 how to calculate Pearson's correlation between the two variables
0:03:26 how to interpret the collinearity between two variables
0:03:49 how to create a confidence interval for the model coefficients using the "confint" command
0:03:57 interpreting the confidence interval for our model's coefficients
0:04:13 fitting a linear model using all of the X variables
0:04:27 how to check the model assumptions by examining plots of the residuals or errors using the "plot(model)" command

Views: 200545
MarinStatsLectures-R Programming & Statistics

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: 282296
dataminingincae

In this video you will learn how to perform multiple regression analysis using SAS. Logistic regression is a classification algorithm that you can use to classify binary data. This is a machine learning algorithm that is similar to decision tree, bagging, random forest, Support vector machine learning which is an important data science algorithm.
For Training & Study packs on Analytics/Data Science/Big Data, Contact us at [email protected]
Find all free videos & study packs available with us here:
ANalytics Study Pack : http://analyticsuniversity.com/
Analytics University on Twitter : https://twitter.com/AnalyticsUniver
Analytics University on Facebook : https://www.facebook.com/AnalyticsUniversity
Logistic Regression in R: https://goo.gl/S7DkRy
Logistic Regression in SAS: https://goo.gl/S7DkRy
Logistic Regression Theory: https://goo.gl/PbGv1h
Time Series Theory : https://goo.gl/54vaDk
Time ARIMA Model in R : https://goo.gl/UcPNWx
Survival Model : https://goo.gl/nz5kgu
Data Science Career : https://goo.gl/Ca9z6r
Machine Learning : https://goo.gl/giqqmx
Data Science Case Study : https://goo.gl/KzY5Iu
Big Data & Hadoop & Spark: https://goo.gl/ZTmHOA

Views: 24194
Analytics University

made with ezvid, free download at http://ezvid.com Using MSExcel or Kingsoft Spreadsheets in this example, you can do forecasting using Linear Regression

Views: 5940
George Atento

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: 163960
Data Analysis Videos

made with ezvid, free download at http://ezvid.com

Views: 53
Joshua Freistadt

Checking Linear Regression Assumptions in R ;
Dataset: https://goo.gl/tJj5XG; Linear Regression Concept and with R: https://bit.ly/2z8fXg1;
More Statistics and R Programming Tutorials: https://goo.gl/4vDQzT;
How to test linear regression assumptions in R?
In this R tutorial, we will first go over some of the concepts for linear regression like how to add a regression line, how to interpret the regression line (predicted or fitted Y value, the mean of Y given X), how to interpret the residuals or errors (the difference between observed Y value and the predicted or fitted Y value) and the assumptions when fitting a linear regression model.
Then we will discuss the regression diagnostic plots in R, the reason for making diagnostic plots, and how to produce these plots in R; You will learn to check the linearity assumption and constant variance (homoscedasticity) for a regression model with residual plots in R and test the assumption of normality in R with QQ (Quantile Quantile) plots. You will also learn to check the constant variance assumption for data with non-constant variance in R, produce and interpret residual plots, QQ plots, and scatterplots for data with non-constant variance, and produce and interpret residual plots, QQ plots, and scatterplots for data with non-linear relationship in R.
►► Download the dataset here:
https://statslectures.com/r-stats-datasets
►► Watch More:
►Linear Regression Concept and Linear Regression with R Series: https://bit.ly/2z8fXg1
►Simple Linear Regression Concept https://youtu.be/vblX9JVpHE8
►Nonlinearity in Linear Regression https://youtu.be/tOzwEv0PoZk
► R Squared of Coefficient of Determination https://youtu.be/GI8ohuIGjJA
► Linear Regression in R Complete Series https://bit.ly/1iytAtm
■ Table of Content:
0:00:29 Introducing the data used in this video
0:00:49 How to fit a Linear Regression Model in R?
0:01:03 how to produce the summary of the linear regression model in R?
0:01:15 How to add a regression line to the plot in R?
0:01:24 How to interpret the regression line?
0:01:43 How to interpret the residuals or errors?
0:01:53 where to find the Residual Standard Error (Standard Deviation of Residuals) in R
0:02:14 What are the assumptions when fitting a linear regression model and how to check these assumptions
0:03:01 What are the built-in regression diagnostic plots in R and how to produce them
0:03:24 How to use Residual Plot for testing linear regression assumptions in R
0:03:50 How to use QQ-Plot in R to test linear regression assumptions
0:04:33 How to produce multiple plots on one screen in R
0:05:00 How to check constant variance assumption for data with non-constant variance in R
0:05:12 How to produce and interpret a Scatterplot and regression line for data with non-constant variance
0:05:40 How to produce and interpret the Residual plot for data with non-constant variance in R
0:06:02 How to produce and interpret the QQ plot for data with non-constant variance in R
0:06:12 How to produce and interpret a Scatterplot with regression line for data with non-linear relationship in R
0:06:40 How to produce and interpret the Residual plot for a data with non-linear relationship in R
0:06:52 How to produce and interpret the QQ plot for a data with non-linear relationship in R
0:07:02 what is the reason for making diagnostic plots
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Our Team:
Content Creator: Mike Marin (B.Sc., MSc.) Senior Instructor at UBC.
Producer and Creative Manager: Ladan Hamadani (B.Sc., BA., MPH)
These #RTutorials are created by #marinstatslectures to support a course at The University of British Columbia (#UBC) 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: 144176
MarinStatsLectures-R Programming & Statistics

The Multiple Linear Regression video series is available for FREE as an iTune book for download on the iPad. The ISBN number is 978-1-62407-066-6. The title is "Multiple Linear Regression." Waller and Lumadue are the authors. The iTune text provides accompanying narrative and the SPSS readouts used in the video series.
The textbook can be obtained from:
https://itunes.apple.com/us/book/multiple-linear-regression/id657084933?ls=1
This video examines the multiple linear regression model.

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This video shows the wxEmpiricus capabilities to estimate linear regression models and to detect multicolinearity. You can download the wxEmpiricus software from: http://www.empiricus.unican.es
The dataset is available from
http://lib.stat.cmu.edu/datasets/longley

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wxEmpiricus

Covers all aspects of conducting correlation and regression in Excel, using GSS data

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