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Basic Excel Business Analytics #56: Forecasting with Linear Regression: Trend & Seasonal Pattern
 
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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: 57240 ExcelIsFun
stock returns regression in excel
 
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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: 69529 Codible
Do a Linear Regression (with free R Statistics Software)
 
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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: 38929 economicurtis
Big Data 5: Linear Regression
 
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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
An Introduction to Linear Regression Analysis
 
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Tutorial introducing the idea of linear regression analysis and the least square method. Typically used in a statistics class. Playlist on Linear Regression http://www.youtube.com/course?list=ECF596A4043DBEAE9C Like us on: http://www.facebook.com/PartyMoreStudyLess Created by David Longstreet, Professor of the Universe, MyBookSucks http://www.linkedin.com/in/davidlongstreet
Views: 590397 statisticsfun
Basic Excel Business Analytics #48: Data Analysis Regression feature
 
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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: 23284 ExcelIsFun
CIMA BA2 - Regression analysis
 
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CIMA BA2 - Regression analysis Free lectures for the CIMA BA2 Fundamentals of Management Accounting To benefit from this lecture, visit opentuition.com to download the free lectures notes used in the lecture and access all our free resources including all BA2 lectures, practice tests and Ask the Tutor Forums. http://opentuition.com/cima/cima-ba2/ Please go to opentuition forums to post questions to CIMA BA2 Tutor, we do not provide support on youtube. *** Complete list of free CIMA BA2 lectures is available on http://opentuition.com/cima/cima-ba2/ ***
Views: 1707 OpenTuition
Basic Excel Business Analytics #51: Testing Significance of Regression Relationship with p-value
 
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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: 12605 ExcelIsFun
Linear Regression in R (R Tutorial 5.1)
 
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Learn how to fit a linear regression model in R and produce summaries and ANOVA table for it. You will learn to use "lm", "summary", "abline", "coef", "confint", "level", "anova", and "col" commands. 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 topics addressed in this video: 0:00:07 When is it appropriate to fit a simple linear regression model for our data? 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 simple linear regression 0:01:36 how to let R know which variable is X variable and which one is Y variable in simple linear regression model 0:01:45 how to ask for the summary of the simple linear regression model in R using the "summary" command including estimates for intercept, test statistics, p-values and estimates of the slope,etc. 0:02:27 "residual standard error" (a measure of the variation of observations around the regression line) in R 0:02:53 how to ask for the attributes of the simple linear regression model in R using the "attributes" command 0:03:06 how to extract certain attributes from the simple linear regression model in R using the dollar sign ($) 0:03:40 how to add a regression line to a plot in R using "abline" command 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 using the "coef" command 0:04:11 how to produce confidence intervals for model's coefficients in R using the "confint" command 0:04:21 how to change the level of confidence using the "level" argument within the "confint" command 0:04:38 how to produce the ANOVA table for the linear regression model using the "anova" command 0:04:47 explore the relationship between ANOVA table and the f-test of the linear regression sumamry 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
Views: 170031 MarinStatsLectures
Basic Excel Business Analytics #50: Introduction to Multiple Regression, Data Analysis Regression
 
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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: 7122 ExcelIsFun
Transformation/Linear Regression in KyPlot (freeware)
 
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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: 1513 Brandon Schamp
Forecasting Using Linear Regression
 
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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: 5404 George Atento
Multiple Regression Example
 
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From example on pg. 203 of the course packet. To follow along, download the excel workbook from Blackboard.
Views: 1171 AEM3100BusStats
Linear Regression Analysis in R
 
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Download the data set here: https://drive.google.com/open?id=0Bz9Gf6y-6XtTVFFwcFdtZk5IUGs
R Tutorial #10 - Linear Regression
 
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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: 171167 Tutorlol
Download linear regression indicator for Metatrader
 
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http://zavodilo.com/en/mt4
Views: 343 Metatrader
Logistic Regression Using Excel
 
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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: 156314 Data Analysis Videos
Conduct and interpret regression analysis in seconds
 
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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.
How to Conduct a Multiple Regression study using Minitab 17
 
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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: 6398 RMK Six Sigma
Multiple regression - Checking Assumptions - for Beginners
 
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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: 77215 weislearners
How to do Linear Regression with Excel Data Analysis Toolpak
 
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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: 105291 Matt Macarty
1 Multiple Linear Regression - An Introduction
 
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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: 31442 Lee Rusty Waller
Linear regression- multicolinearity
 
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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
Views: 3078 wxEmpiricus
Linear Regression Machine Learning Method Using Scikit-learn & Pandas in Python - Tutorial 30
 
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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: 12897 TheEngineeringWorld
How to setup and use linear regression on Excel for Mac
 
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Download StatPlus here: http://www.analystsoft.com/en/products/statplusmacle/
Views: 2588 julianlecalvez
How to Install the Data Analysis ToolPak in Microsoft Excel
 
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Illustrates how to Add-In the Data Analysis ToolPak in Excel. Excel statistics data analysis toolpak. Check out our brand-new Excel Statistics Text: https://www.amazon.com/dp/B076FNTZCV In the text we cover installing the Data Analysis ToolPak and much more. YouTube Channel: https://www.youtube.com/user/statisticsinstructor
Views: 201087 Quantitative Specialists
4 Linear Regression - Writing Research Questions
 
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The linear regression video series is availablefor FREE as an iTune book for download on the iPad. The ISBN is 9781628470420. The title is "Linear Regression". Waller and Lumadue are the authors. The iTune text provides accompanying narrative and the SPSS readouts used in the video series. https://itunes.apple.com/us/book/linear-regression/id656969487?ls=1 This video develops one methodology for writing research questions and hypotheses for a linear regression study.
Views: 1156 Lee Rusty Waller
Linear regression — jamovi
 
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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: 68 datalabcc
Nonlinear Regression in MATLAB
 
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A three parameter (a,b,c) model y = a + b/x + c ln(x) is fit to a set of data with the MATLAB APMonitor toolbox. This tutorial walks through the process of installing the solver, setting up the objective (normalized sum of squared errors), adjusting the parameter values to minimize the SSE, and plotting the results. Download source code from http://apmonitor.com/che263/uploads/Main/MATLAB_nonlinear_regression.zip
Views: 53860 APMonitor.com
Excel 2010 Statistics 90: Linear Regression #4: Calculate Slope, Y-Intercept, Estimated Equation
 
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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: 36930 ExcelIsFun
How to Make Predictions from a Multiple Regression Analysis
 
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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: 119741 ProfTDub
Correlation and Regression in Excel with your GSS Data Download
 
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Covers all aspects of conducting correlation and regression in Excel, using GSS data
Views: 235 Rachel Petrak
Excel 2010 Statistics 87: Linear Regression #1: Scatter Diagram: Relationship Between 2 Variables?
 
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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: 49745 ExcelIsFun
Checking Linear Regression Assumptions in R (R Tutorial 5.2)
 
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How to check the validity of assumptions made when fitting a Linear Regression Model. In this video you will learn how to use residual plots to check the linearity assumption and constant variance assumption and QQ plots to check the assumption of normality. This video provides a beginner introduction to programming in R Statistical Software. 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: 0:00:29 introducing the variables for the example used in this video 0:00:42 how to produce a Scatterplot of the variables using the "plot" command 0:00:49 how to fit a Linear Regression Model using the "lm" command 0:01:03 how to ask R for the summary of the linear regression model using the "summary" command 0:01:15 how to add a regression line to the plot using the "abline" command 0:01:24 how to interpret the regression line (predicted or fitted Y value, the mean of Y given X) 0:01:43 how to interpret the residuals or errors (the difference between observed Y value and the predicted or fitted Y value) 0:01:53 where to find the "Residual Standard Error" aka the "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 Residual plot (one of the regression diagnostic plots in R) and how to use it to check the assumptions for linear regression model 0:03:50 Quantile-Quantile (QQ-Plot)(one of the regression diagnostic plots in R) and how to use it to check the assumptions for linear regression model 0:04:33 how to have R produce multiple plots on one screen using the "mfrow" command 0:05:00 how to check constant variance assumption for data with non-constant variance 0:05:12 producing and interpreting a Scatterplot and regression line for data with non-constant variance 0:05:40 producing and interpreting the Residual plot for data with non-constant variance 0:06:02 producing and interpreting the QQ plot for data with non-constant variance 0:06:12 producing and interpreting a Scatterplot with regression line for data with non-linear relationship 0:06:40 producing and interpreting the Residual plot for a data with non-linear relationship 0:06:52 producing and interpreting the QQ plot for a data with non-linear relationship 0:07:02 what is the reason for making diagnostic plots
Views: 136938 MarinStatsLectures
Java - Linear regression with OLS method
 
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In this tutorial I am giving an implementation for the ordinary least sqares method in Java and I'll show how can the regression function be plotted in a graphic with the given dataset. Resources: OLS method: https://en.wikipedia.org/wiki/Ordinary_least_squares Download of the project and libraries: http://j.gs/AVT7
Views: 138 Ivan Capponi
Multiple Linear Regression in R (R Tutorial 5.3)
 
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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: 192322 MarinStatsLectures
Moderated multiple regression in STATA using continuous focal and moderator variables
 
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This video provides an illustration of running a moderated multiple regression in STATA when you have continuous predictors in the model. You can download the data here: https://drive.google.com/open?id=1jyZJ5uqyVRNU2vTU3hQVP-r-iFRdrB0D The commands referenced in the video are in a Word document here: https://drive.google.com/open?id=1oowp0RCIm4BcvZryAJKIUKqrnnJp_Z6q An additional file containing the Excel spreadsheet can be obtained here: https://drive.google.com/open?id=1rdt5G-Taw7--LrMO6DKOL7bHVoC36D7v
Views: 728 Mike Crowson
Excel 2013 Statistical Analysis #62: Calculate Slope and Intercept for Regression Line
 
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Download files: http://people.highline.edu/mgirvin/excelisfun.htm Topics in this video: 1. (00:09) Discussion of Slope and y-intercept formulas 2. (03:30) X Y Scatter Chart / Plot 3. (05:00) Calculate Slope and Intercept 4. (09:55) Use equation to predict 5. (10:48) Example 2 for calculating Slope and Intercept
Views: 6405 ExcelIsFun
Linear Regression with Java Netbeans
 
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Basically is a program that calculated the equation of linear regression with the least squares method and Java Netbeans. Development of statistical and mathematical problems Online. [email protected] download here: https://drive.google.com/file/d/0B626fkHC2AupZ2MwNHpub1pNWUk/view?usp=sharing visit minuvahard10.com
Views: 3320 Miguel Nunez
WHAT IS REGRESSION  ANALYSIS WITH EXAMPLES IN HINDI
 
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WHAT IS REGRESSION ANALYSIS WITH EXAMPLES IN HINDI
Views: 12983 LearnEveryone
6 Multiple Linear Regression - Reading the SPSS Output
 
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The Multiple Linear Regression video series is availablefor 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 navigates through the SPSS output for a multiple linear regression problem.
Views: 2735 Lee Rusty Waller
Bivariate Linear Regression with Plot of Residuals and Predicted
 
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made with ezvid, free download at http://ezvid.com
Views: 221 Joshua Freistadt
Statistics with R (1) - Linear regression
 
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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: 285463 Christoph Scherber
4 Multiple Linear Regression - Writing Research Questions
 
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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: 7379 Lee Rusty Waller
SEM Basics 01 - Path Modeling - Simple Regression
 
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In this video you will learn how to model a simple bivariate regression with OpenMx. Download R: https://www.r-project.org/ Download OpenMx: http://openmx.ssri.psu.edu/installing-openmx Data and Scripts: https://drive.google.com/drive/folders/0B48ddQ1HQJCEekN5WDV3NURNanM?usp=sharing OpenMx runs on Mac OS X, Windows (XP, Vista, 7, 8), and several varieties of Linux. This means the same scripts you write in Windows will run in Mac OS X or Linux. OpenMx is free and open source software for use with R that allows estimation of a wide variety of advanced multivariate statistical models. OpenMx consists of a library of functions and optimizers that allow you to quickly and flexibly define an SEM model and estimate parameters given observed data. OpenMx can be used by those who think in terms of path models or by those who prefer to specify models in terms of matrix algebra. OpenMx is extremely powerful, taking full advantage of the R programming environment. This means that complicated models and data sets can be specified and modified using the R language.
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