Simple Linear Regression using Microsoft Excel

Views: 278362
Jalayer Academy

Using Data Analysis Toolpak Correlation analysis and interpreting the results

Views: 26637
Tobin Porterfield

Knowledge Varsity (www.KnowledgeVarsity.com) is sharing this video with the audience.

Views: 134922
KnowledgeVarsity

Overview of the Excel regression analysis output. For more detail, go to: http://www.statisticshowto.com/excel-regression-analysis-output-explained/

Views: 71186
Stephanie Glen

This video uses Anderson 11e Chapter 15 #4 to walk through regression output and explain how to interpret it.

Views: 234979
Jason Delaney

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: 25889
ExcelIsFun

LearnAnalytics demonstrates use of Multiple Linear Regression on Excel 2010. (Data Analysis Toolpak). Data set referenced in video can be downloaded at www.learnanalytics.in/blog/wp-content/uploads/2014/02/car_sales.xlsx

Views: 63686
Learn Analytics

This video shows you how to the test the significance of the coefficients (B) in multiple regression analyses using the Data Analysis Toolpak in Excel 2016.
For an introduction to multiple regression see in Excel for Windows, see: https://www.youtube.com/playlist?list=PLx-uqKoW1C5lqaPwqWPMBoNCjwKqRahES
For an introduction to multiple regression in Excel for Mac, see:
https://www.youtube.com/playlist?list=PLx-uqKoW1C5lK6nhfmd_-mt4mTXMo8OyS
This is part of a lecture series (5 videos) covering several supplemental topics in statistics.
The PowerPoint and data sets can be downloaded at:
https://www.researchgate.net/profile/David_Dunaetz/publication/308996129_PowerPoint_for_Supplemental_Topics_in_Statistics/links/57fd7a5508ae49db47553c1b
If you have not installed the Data Analysis Toolpak (which comes free with Excel), the following video will show you how to do it.
Windows: https://www.youtube.com/watch?v=rq8VynGNAFU
Mac: https://www.youtube.com/watch?v=1R_aJ_Fli2w

Views: 3803
David Dunaetz

How to install Data Analysis Addin in Excel: https://youtu.be/SqpSwxJ9t2k
This video shows how to generate simple linear regression statistics using the Data Analysis Addin in Excel –intercept, slope, regression equation, SSR, SSE, SST, correlation coefficient (r), coefficient of determination (R-square), and standard error of estimate.

Views: 13041
Joshua Emmanuel

Using some made up ice cream sales vs. temperature data, I demonstrate how to calculate and interpret a point prediction and 90% prediction interval using MS Excel 2007

Views: 78840
ProfTDub

This video shows how to preform a simple regression using excel 2010. Scatter plots are also shown.

Views: 3500
Daria Newfeld

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: 28540
Dr. Todd Grande

This video covers a few topics using the data analysis tool.
After this video you should be able to:
a) Find and use data analysis on excel to calculate statistics
b) Calculate the mean, median, mode, standard deviation, range and coefficient variation on a variable set of data in excel.
c) Conduct a confidence interval in excel.
d) Complete a T-test in excel to help complete a hypothesis test.
e) Conduct a linear regression analysis output from excel and create a scatter diagram.

Views: 104279
Me ee

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: 181738
Data Analysis Videos

Multiple Regression in Excel in a nutshell. Focusing on Excel functionality more than presentation of regression theory.

Views: 88856
Jalayer Academy

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: 326912
TheWoundedDoctor

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
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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: 26002
Quantitative Specialists

How to find a regression line equation and analyze residuals using Excel's Data Analysis Tool Pack. Larson 9.2.33

Views: 1709
The Stats Files - Dawn Wright Ph.D.

How to quickly read and understand the important parts of the output of a Regression done in Excel. You be able to immediately recognize and understand the four most important parts of the output of an Excel Regression. Become an Excel Statistical Master !

Views: 231598
excelmasterseries

Learn how to conduct multiple linear regression using Excel data analysis toolpak

Views: 111387
KnowledgeVarsity

This video shows you how run a multivariate linear regression in Excel. It also explains some common mistakes people make that results in Excel being unable to perform the regression. It also shows how to enable the Data Analysis Toolpak, although the screen capture software glitched a bit when I recorded this, so I recommend you watch the video entitled "Enabling the Data Analysis Toolpak". Author: David Switzer

Views: 542213
SCSUEcon

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: 7708
ExcelIsFun

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: 107890
Matt Macarty

Excel isn't brilliant at running statistical tests, but the Data Analysis Toolpak add-in does make it a bit easier. If you don't have this add-in you'll need to add it in first.

Views: 4972
BrunelASK

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: 66333
ExcelIsFun

A brief explanation of the output of regression analysis. For more information visit www.calgarybusinessblog.com

Views: 466646
Matt Kermode

This video is about RegressionAnalysis

Views: 82609
David Orndorff

Experimental data analysis, curve fitting, linear regression, trendline.
Lone Star College CyFair ENGR 2304 Programming for Engineers Example Problems: Excel Problem 16.

Views: 2482
Yiheng Wang

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: 82072
Codible

Use Excel to generate a Simple Regression Equation and to add a line of best fit to compare two sets of data. Excel also gives you an R-squared value to show how strong the correlation is between the two sets of data.

Views: 192421
Eugene O'Loughlin

Learn about managing data in Excel. These are the Video supplements for Workbook of Quantitative Tools and Techniques in Marketing, 2nd Ed. Part of a full MOOC.

Views: 4184
Tim J Smith PhD

In order to get a Regression Equation, many times you can complete that job with only the Scatter Plot, but performing a Single Regression Analysis will give you more significant information. This video will explain how to interpret results of Simple Regression Analysis using Excel Data Analysis Tools. You will learn about the 'Coefficient of Determination', 'Correlation Coefficient', ‘Adjusted R Square’ and the differences among them and how to interpret them for your business. The P-Value represents the degree of relationship between the Explanatory Variable X and the Objective Variable Y, which is important for Multiple Regression Analysis, so this video will explain the P-Value in detail in this video.
(Process Improvement Methodology for Service Operations, PMP, Project Management, Lean Six Sigma, Japan, Six Sigma, Excel, VBA: Episode 106)
＜＜Read this video's transcript in my blog.＞＞
http://econoshift.com/en/simple-regression-analysis-Interpretation-2/
＜＜ Related Videos ＞＞
Simple Regression Analysis by Scatter Plot in Excel
https://youtu.be/jSbEQHlkURY
How to use the T-test and F-test in a real world【Excel Function】
https://youtu.be/kP8Ieb0Sm9I
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Mike Negami's Blog Site: http://econoshift.com/en/
Free Excel Template Downloads: http://econoshift.com/ja/free-downloads/
Facebook Page: https://www.facebook.com/Econoshift/

Views: 78
econoshift.com

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: 14917
ExcelIsFun

In this video you will learn how to perform Simple Linear Regression in Excel.
For all our videos & Study packs visit: http://analyticuniversity.com/

Views: 4781
Analytics University

How to do Multiple Regression in Excel 2016 for Windows (Job Performance Example)
If you have not installed the Data Analysis Toolpak (which comes free with Excel), this video shows you how to do it: https://www.youtube.com/watch?v=rq8VynGNAFU

Views: 18166
David Dunaetz

Microsoft Excel is a great tool for manipulating data. One under used tool is regression analysis, which helps determine the relationship between a series of independent variables and a series of dependent variables. In this video, Professor Yunker shows how to install the Data Analysis Toolpak and then he uses regression analysis to estimate housing prices based on the number of bedrooms, bathrooms and other numeric factors.

Views: 1621
professoryunker

Overview of adding a trend line to a chart along with regression analysis in Excel

Views: 192133
Larry Corman

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: 241832
Jason Delaney

The Data Analysis add-in gives you a lot more information than using =LINREG() by hand, and does not require using an array formula, but does not update the results if the data changes.
To see how to add in the Data Analysis pack, see
http://www.youtube.com/watch?v=sknFUKvR2Fs
This video used to be titled "Multivariate Regression...", but technically that refers to having more than one "Y" or output variable, while "Multiple Regression" refers to having more than one "X" or input or predictor variable. But many people confuse the two terms.

Views: 25244
aross1633

I take the ice cream sales vs. temp data, run a regression, and produce residual (and fitted values and standardized residuals output) and two residuals plots - to check the assumptions of independence and constant variance. Since the data is a time-series, one of the plots produced is residuals vs. time to check if independence over time is satisfied.

Views: 83328
ProfTDub

Using Excel to create a scatter plot, calculate and graph a trendline.

Views: 905804
lbcccampbem

This tutorial covers the basic concepts of Multiple Regression. Before watching this tutorial please make sure you are familiar with the basic concepts of simple linear regression, if you need a review, go to https://youtu.be/BLRjywb0mes. In this tutorial we discuss introduce the multiple regression model and discuss what its parameters mean and how to test the significance of the slopes of each independent variable using a t test. This tutorial also shows how to conduct regression analysis using Excel, and the output for multiple regression hypothesis tests for individual slopes is examined using the t tests statistic and the p value approach. The significance of r and the adjusted r squared values is also discussed. The ANOVA table and F test are not covered in this tutorial.

Views: 45432
Learn Something

Scatterplots, Bivariate Data, and Regression

Views: 6733
Jalayer Academy

How to perfrom multiple linear regression. Three ways. First - linest() - using excel formula, second - using "regression" in data analysis toolpack , third ( 03:06 ) - using linear matrix algebra. I hope this will give some insight on how to deal with matrices in excel.
Hyperlink mentioned in video - http://www.mathsisfun.com/algebra/matrix-inverse.html

Views: 8023
Didzis Lauva

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.
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BI Excel

Creating a scatterplot, linear model, and determining the correlation between two variables in excel (Mac).

Views: 2602
Evan McGee

A three parameter (a,b,c) model y = a + b/x + c ln(x) is fit to a set of data with the Excel solver add-in. This tutorial walks through the process of installing the solver, setting up the objective (normalized sum of squared errors), and adjusting the parameter values to minimize the SSE.

Views: 139728
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