About 20 results out of 1000000 (0.23 seconds)

**Channel Title :** Khan Academy

**Views :** 573544

**Likes :** 1094

**DisLikes :** 51

**Published Date :** 2010-11-05T21:42:39.000Z

R-Squared or Coefficient of Determination
Watch the next lesson: https://www.khanacademy.org/math/probability/regression/regression-correlation/v/calculating-r-squared?utm_source=YT&utm_medium=Desc&utm_campaign=ProbabilityandStatistics
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Probability and statistics on Khan Academy: We dare you to go through a day in which you never consider or use probability. Did you check the weather forecast? Busted! Did you decide to go through the drive through lane vs walk in? Busted again! We are constantly creating hypotheses, making predictions, testing, and analyzing. Our lives are full of probabilities! Statistics is related to probability because much of the data we use when determining probable outcomes comes from our understanding of statistics. In these tutorials, we will cover a range of topics, some which include: independent events, dependent probability, combinatorics, hypothesis testing, descriptive statistics, random variables, probability distributions, regression, and inferential statistics. So buckle up and hop on for a wild ride. We bet you're going to be challenged AND love it!
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**Channel Title :** MrNystrom

**Views :** 176347

**Likes :** 1992

**DisLikes :** 76

**Published Date :** 2011-10-16T03:40:06.000Z

How should you interpret R squared? what does it really tell us?
this video should help

**Channel Title :** ConnectCubed

**Views :** 41546

**Likes :** 310

**DisLikes :** 8

**Published Date :** 2014-05-19T18:46:04.000Z

**Channel Title :** statisticsfun

**Views :** 325931

**Likes :** 2297

**DisLikes :** 33

**Published Date :** 2012-02-05T18:01:46.000Z

An example on how to calculate R squared typically used in linear regression analysis and least square method.
Like us on: http://www.facebook.com/PartyMoreStudyLess
Link to Playlist on Linear Regression:
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Created by David Longstreet, Professor of the Universe, MyBookSucks
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**Channel Title :** Simple Learning Pro

**Views :** 5680

**Likes :** 73

**DisLikes :** 3

**Published Date :** 2015-11-23T09:30:44.000Z

Learn about regression and r-squared
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**Channel Title :** R SQUARE

**Views :** 234

**Likes :** 41

**DisLikes :** 2

**Published Date :** 2018-11-18T15:47:50.000Z

choreographed by R SQUARE.
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Original credits*

**Channel Title :** StatQuest with Josh Starmer

**Views :** 14522

**Likes :** 260

**DisLikes :** 3

**Published Date :** 2015-02-03T14:48:20.000Z

A StatQuest https://statquest.wordpress.com/ for R-squared.
For a complete index of all the StatQuest videos, check out:
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If you'd like to support StatQuest, please consider a StatQuest t-shirt or sweatshirt...
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**Channel Title :** Jalayer Academy

**Views :** 11303

**Likes :** 50

**DisLikes :** 3

**Published Date :** 2017-03-24T15:38:11.000Z

R square (R2) - Coefficient of Determination in Simple Linear Regression or Coefficient of Multiple Determination in Multiple Regression

**Channel Title :** Quantitative Specialists

**Views :** 24391

**Likes :** 74

**DisLikes :** 5

**Published Date :** 2017-02-27T12:00:04.000Z

In this video we take a look at how to calculate and interpret R square in SPSS. R square indicates the amount of variance in the dependent variable that is accounted for or explained by the independent variable.
Video Transcript: In this video I want to show you how to calculate R squared. Now here I have an example with three variables. If I'm trying to find R squared for just two variables, one way we can go about doing that is just running a correlation. Let me show you what I mean here. So if I go to analyze and then correlate and then bivariate let's say we want R square between SAT and college GPA, so I'll move those two over and then I'll click OK. Now this gives me not R squared but it gives me r so the correlation between SAT and college GPA is .65 and that is in fact significant at the .01 level. Now that's r; so if I want R squared what I can do is just simply square that. So .65 and then squared here is point .4225 so R squared here is .42. Now another way to do this is that if I have more than two variables that I'm working with, or if I just don't feel like calculating R squared manually by squaring r, what I can do is I can go to analyze and then regression and select linear. Now I have to decide here what my dependent variable is, or what it is that I'm trying to predict. Now SAT was measured in high school and college GPA, as the variable sounds, is GPA in college during the first year, after one year college. So it makes sense that SAT would predict college GPA. So we'll put the thing we're trying to predict in the dependent box and then we'll put SAT and the independent. OK let's go ahead and click OK. Now if you recall from our earlier analysis, when we squared that correlation we got .4225. So R square was .4225. And here to find R squared we want to go to the Model Summary table and here's r this is the correlation .65, we saw that in our previous analysis. And then R squared is right next to r, notice .422. And that's exactly what we got before within rounding error. So we can run regression to calculate R squared. Now in case you're not familiar with what R squared is, it indicates the amount of variance in the dependent variable that is accounted for or explained by the independent variable. So since we're using SAT here to predict college GPA, What that means is. If we know a person's SAT score, we can account for I can convert this to a percentage, about 42% of the variance in college GPA, which is pretty good. OK so that's what R squared means, it's a measure of how much we explained in one variable using one or more other variables. And one last thing here, if you want to calculate R squared and you have more than two variables at once, then you really need to use this regression approach here to find that. So let's say we want to use both SAT and social support to predict college GPA and we're doing this two try and get our R square. So we're doing this to try and see how well, overall, these two predictors combined how much of college GPA they account for or explain, which you may recall is what R square really means. How much of the variance did we account for in a given variable using one or more other variables. So go ahead and click OK. And then here notice our R squared increased, and it will when we add another predictor in almost all cases. And here our R squared is .511. So using social support and SAT we can account for about 51% of the variance in college. So the GPA in college after their first year. OK that's it. Thanks for watching.

**Channel Title :** ritvikmath

**Views :** 11938

**Likes :** 208

**DisLikes :** 5

**Published Date :** 2015-08-18T02:05:15.000Z

**Channel Title :** Stephanie Glen

**Views :** 55763

**Likes :** 174

**DisLikes :** 9

**Published Date :** 2014-11-20T23:35:15.000Z

A basic overview of adjusted R squared including the adjusted R squared formula and a comparison to R squared.

**Channel Title :** intromediateecon

**Views :** 38300

**Likes :** 100

**DisLikes :** 6

**Published Date :** 2009-09-20T18:46:56.000Z

In this video, I give two formulas for r^2, and give one intuitive interpretation of the value of r^2.

**Channel Title :** Keith Bower

**Views :** 49525

**Likes :** 131

**DisLikes :** 23

**Published Date :** 2009-02-01T20:04:06.000Z

Interpretation and positive/negative aspects of R-squared. More info at my website: http://bit.ly/5fuFsY

**Channel Title :** ilmm pk

**Views :** 2603

**Likes :** 25

**DisLikes :** 1

**Published Date :** 2017-10-11T03:11:06.000Z

044 Adjusted R squared
For More Check Out ilmm.pk

**Channel Title :** Erika Malinoski

**Views :** 54176

**Likes :** 164

**DisLikes :** 13

**Published Date :** 2013-09-17T15:12:38.000Z

**Channel Title :** Ben Lambert

**Views :** 28701

**Likes :** 109

**DisLikes :** 0

**Published Date :** 2013-06-09T20:56:25.000Z

This video introduces the concept of R squared in econometrics, in terms of the explained sum of variance and the total sum of squares. Check out https://ben-lambert.com/econometrics-course-problem-sets-and-data/ for course materials, and information regarding updates on each of the courses. Quite excitingly (for me at least), I am about to publish a whole series of new videos on Bayesian statistics on youtube. See here for information: https://ben-lambert.com/bayesian/ Accompanying this series, there will be a book: https://www.amazon.co.uk/gp/product/1473916364/ref=pe_3140701_247401851_em_1p_0_ti

**Channel Title :** Math With Mark

**Views :** 2052

**Likes :** 5

**DisLikes :** 5

**Published Date :** 2016-11-17T22:08:42.000Z

This video offers a visual explanation of what r-squared represents in a linear regression.

**Channel Title :** statisticsfun

**Views :** 135340

**Likes :** 228

**DisLikes :** 27

**Published Date :** 2009-09-28T02:52:08.000Z

Tutorial shows how to calculate a linear regression line using excel.
Like MyBooKSucks on: http://www.facebook.com/PartyMoreStudyLess
Playlist on Regression
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Created by David Longstreet, Professor of the Universe, MyBookSucks
http://www.linkedin.com/in/davidlongstreet

**Channel Title :** zedstatistics

**Views :** 570094

**Likes :** 3188

**DisLikes :** 90

**Published Date :** 2011-11-22T08:20:15.000Z

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

**Channel Title :** R SQUARE

**Views :** 331865

**Likes :** 5108

**DisLikes :** 883

**Published Date :** 2018-11-14T11:26:17.000Z

చంద్రబాబు భిక్షమేస్తే #KCR అనుభవిస్తున్నాడు #Hyderbad #Public Fires On #TRS #Governament - #RSQUARE
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**Channel Title :** Yadnya Investment Academy

**Views :** 22186

**Likes :** 429

**DisLikes :** 8

**Published Date :** 2017-10-15T05:00:00.000Z

Alpha and Beta in Hindi
Alpha and beta are both risk ratios that investors use as a tool to calculate, compare and predict returns. You are most likely to see alpha and beta referenced with mutual funds. Both measurements utilize benchmark indexes, such as the BSE Sensex, and compare them against the individual security to highlight a particular performance tendency.
Alpha is a measure of an fund's performance compared to a benchmark. It's a mathematical estimate of the return, based usually on the growth of earnings per share.
Beta, on the other hand, is based on the volatility—extreme ups and downs in prices or trading—of the stock or fund, something not measured by alpha. But beta, too, is compared to a benchmark.
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**Channel Title :** sentdex

**Views :** 67150

**Likes :** 549

**DisLikes :** 7

**Published Date :** 2016-04-21T18:11:14.000Z

Welcome to the 10th part of our of our machine learning regression tutorial within our Machine Learning with Python tutorial series. We've just recently finished creating a working linear regression model, and now we're curious what is next. Right now, we can easily look at the data, and decide how "accurate" the regression line is to some degree. What happens, however, when your linear regression model is applied within 20 hierarchical layers in a neural network? Not only this, but your model works in steps, or windows, of say 100 data points at a time, within a dataset of 5 million datapoints. You're going to need some sort of automated way of discovering how good your best fit line actually is.
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**Channel Title :** Suvro Sudip

**Views :** 23633

**Likes :** 63

**DisLikes :** 3

**Published Date :** 2013-10-15T23:47:35.000Z

Displaying R squared value and equation of the trendline in Excel

**Channel Title :** Phil Chan

**Views :** 14328

**Likes :** 16

**DisLikes :** 2

**Published Date :** 2012-09-13T04:52:11.000Z

So newbies tend to focus too much on getting a model with a high R-squared. What a "high" R-squared number is depends on the field of application. Another thing is that R-squared close to 1 may mean your model is poor for interpreting relationship between IV and DV.
The video could be better for sure. Noise from building works in the building unsettled me.
Visit me at: http://www.statisticsmentor.com

**Channel Title :** Jalayer Academy

**Views :** 5062

**Likes :** 105

**DisLikes :** 2

**Published Date :** 2017-03-24T15:38:11.000Z

R square (R2) - Coefficient of Determination in Simple Linear Regression or Coefficient of Multiple Determination in Multiple Regression

**Channel Title :** Ben Lambert

**Views :** 41186

**Likes :** 152

**DisLikes :** 7

**Published Date :** 2013-06-13T23:39:31.000Z

This video explains how an adjustment can be made to R squared so that it is a more useful statistic for choosing between different models in econometrics. Check out https://ben-lambert.com/econometrics-course-problem-sets-and-data/ for course materials, and information regarding updates on each of the courses. Quite excitingly (for me at least), I am about to publish a whole series of new videos on Bayesian statistics on youtube. See here for information: https://ben-lambert.com/bayesian/ Accompanying this series, there will be a book: https://www.amazon.co.uk/gp/product/1473916364/ref=pe_3140701_247401851_em_1p_0_ti

**Channel Title :** Bryan Forman

**Views :** 1987

**Likes :** 5

**DisLikes :** 0

**Published Date :** 2017-02-16T04:47:22.000Z

Displaying Best Fit line and R-Squared value in Google Sheets

**Channel Title :** zedstatistics

**Views :** 143117

**Likes :** 1340

**DisLikes :** 11

**Published Date :** 2013-08-11T08:54:38.000Z

All my videos here: http://www.zstatistics.com/
Here is the second regression video, taking a more advanced look at R-squared and dealing with the troublesome concept of degrees of freedom.
Intro 0:00
SSR + SSE = SST 0:54
R-squared 2:06
Degrees of Freedom 3:56
Adjusted R-squared 9:43
Example 11:34

**Channel Title :** Quantitative Specialists

**Views :** 17073

**Likes :** 36

**DisLikes :** 2

**Published Date :** 2014-10-07T15:00:01.000Z

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 in Excel
Regression Analysis
Statistical Analysis in Excel
Video Transcript: In this video we'll take a look at how to run a multiple regression analysis in Microsoft Excel. Now in this example, notice that we have four variables: college GPA, and that's the GPA after the first year in college, and then we have SAT score, which was taken in high school, and we have social support, and this was a measure of how supported people feel, to what degree they can turn to others for support, and this was also assessed in college, and then we have gender, where we have 1s and 2s, where 1s are males and 2s are females. So in this example, we have a total of four variables and we have 30 rows of data here. And in regression, each row corresponds, most typically, to a different person. So, for example, the first person had a GPA of 3.45 after their first year in college, they had a 1200 on the SAT, a 62 on social support, and they were male, they had a 1 on gender. Now in multiple regression, we have two different kinds of variables, we have the criterion variable, which is also known as the dependent variable, and in this example the criterion variable is college GPA. And then we have predictor variables, and those are also known as independent variables. Here we have 3: SAT score, social support, and gender. And, in multiple regression, we are always going to have at least two predictor variables, or independent variables, and only one criterion variable, or dependent variable. So it's important to get used to this terminology when you're using multiple regression as it can get a little confusing otherwise. So, once again, college GPA is our criterion variable, or our dependent variable, and these three variables are our predictors, or our independent variables. And what we're trying to do in multiple regression is we're trying to use these predictors, SAT score, social support, and gender, to predict our criterion variable, college GPA. And that's at the end of the first year in college once again. And there's one other thing I do want to note here, I have a dichotomous variable here, gender. Of course for gender, there's two values males and females. And that's fine, if I have a dichotomous variable, I can go ahead and enter that into regression as normal. But if I have a categorical variable that has more than two categories, like say ethnicity, let's say we had four categories in ethnicity, I can't just go ahead and enter that as a normal predictor, but instead I have to recode that where I have to have as many predictors for ethnicity as are equal to the number categories minus one. So if I have, for example, four categories of ethnicity, I would need to create three predictors for ethnicity alone so they would be ethnicity 1, ethnicity 2, ethnicity 3, just for that variable. And how to do that is beyond the scope of this video, but it is important to be aware of. So if you do have a categorical variable that has more than two categories you don't want to just go ahead and enter it into the normal multiple regression commands as we're going to do here in Excel. It needs to be re-expressed. But we're good to go with gender, because there's just two categories, or in other words it's dichotomous. OK so let's go ahead and get started. To run the multiple regression in Microsoft Excel, we want to go to Data and then select Data Analysis. And then the Data Analysis window opens. We want to go ahead and scroll down to find Regression. Select that and then click OK. Now here for Input Y range, Y corresponds to our criterion variable, and X corresponds to our predictors. So let's go ahead and start with our Y, since the cursor's flashing in that box. Go ahead and select college GPA and scroll all the way down to select all the values. Then we see B1 through B31 in that box. That looks good. Go ahead and make sure now that you click on the Input X range box, so that it's active. And then now we'll select our three variables and all the values for SAT score, social support, and gender. OK that looks good. So now I have C1 through E31, that's perfect. Next, notice that I did select my labels which I wanted to do. I have the variable names there. So I'm going to go ahead and click on Labels. OK and everything else looks good, so go ahead and click OK. And then here we get our output and it's a little bit compressed so let's go ahead and modify this. First, let's go and change the font to 13 point. And then I'm going to go ahead and expand this by double- clicking on these columns here; that looks good.
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**Channel Title :** FactorPad

**Views :** 1394

**Likes :** 5

**DisLikes :** 1

**Published Date :** 2016-06-30T14:49:28.000Z

The definition, visualization and demonstration of a calculation of R-Squared in Excel. Including =AVERAGE function and =SUM function. For investment and financial modeling of stocks and portfolios. Of course you can use the =COVARIANCE.P function or =CORREL or =RSQ but to truly learn investment modeling knowing this calculation is vital.
https://factorpad.com/fin/glossary/r-squared.html
Topics covered in our investment glossary: Excel tutorial, Python examples, portfolio theory, portfolio return, portfolio risk, correlation, regression, linear algebra, alpha signal, risk models, performance attribution.
Glossary:
https://factorpad.com/fin/glossary/index.html
Innovators:
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https://factorpad.com

**Channel Title :** Quantitative Specialists

**Views :** 56543

**Likes :** 163

**DisLikes :** 4

**Published Date :** 2014-10-15T14:00:04.000Z

This video illustrates how to perform and interpret a multiple regression statistical analysis in SPSS.
Multiple Regression
Regression
R-Squared
ANOVA table
Regression Weight
Beta Weight
Predicted Value
YouTube Channel (Quantitative Specialists): https://www.youtube.com/user/statisticsinstructor
Subscribe today!
Inferential course: https://www.udemy.com/inferential-statistics-spss
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Video Transcript: In this video, we'll take a look at how to run a multiple regression in SPSS. And on your screen as an example we have four variables SAT score, social support, gender, and college GPA. And in this example we're using the first three variables SAT score, social support, and gender, to predict first year college GPA. And here SAT score was taken in high school, social support is a measure of how much support a student felt that they received from others, where higher scores indicate greater support, and that was taken in the first year in college, and then gender, our dichotomous variable, where 1 is male and 2 is female, and the variable, college GPA, was the GPA after the first year in college. And in regression what we're trying to predict in this case, college GPA, is known as our criterion variable. It's also known as the dependent variable (DV). And then the variables that we're using to predict the criterion variable, SAT score, social support, and gender, those are known as are predictors or predictor variables, and we also refer to those as independent variables (IV). And those once again are SAT score, social support, and gender. Now in multiple regression you always have one criterion or dependent variable, and for it to be multiple regression you have to have two or more predictors or independent variables. if you just had one predictor or independent variable, such as SAT score, then that would be simple regression. But since we have two or more, in this case we have three once again, we're doing multiple regression. OK so to run multiple regression SPSS we want to go to Analyze, and then Regression and then go ahead and select Linear. And here we want to move college GPA to our Dependent box and then we want to select all the predictors and move those to our Independent(s) box. And then go ahead and click OK. And our output opens here and the first table, Variables Entered/Removed, this confirms that we had the variables gender, SAT score, and social support as our predictors, and then our dependent variable, or criterion variable, was college GPA, so that looks good. OK our next two tables, Model Summary and ANOVA, these two tables, they're looking at whether are predictors, once again, SAT score, social support, and gender, when those are taken together as a set or as a group, do they predict college GPA. And the Model Summary and ANOVA table are getting that slightly different things, but they're very closely related. So let's go ahead and start with Model Summary and take a look at that. So for Model Summary in this video we're going to focus on R square and then in another video we'll talk about these measures in more detail. But for this general overview the most commonly reported value in the Model Summary table is the R square value. And R squared, if I round this to two decimal places and then convert it to a percentage, so this would round two .50 or 50%, I could interpret R squared as follows. R squared once again is equal to .50 and then taken as a set the predictors SAT score, social support, and gender, account for 50% of the variance in college GPA. OK so R squared is a measure of the amount of variance in the dependent variable that the independent variables or predictors account for when taken as a group. And that's very important, it doesn't measure how much a given individual predictor accounts for, but only when we take them all as a group, this Model Summary table says overall, the regression model, which is what is referred to sometimes as a model, these three predictors predicting college GPA, that overall model accounts for 50% of the variance. Which is pretty good in practice. OK next we have our ANOVA table

**Channel Title :** Allen Mursau

**Views :** 185363

**Likes :** 929

**DisLikes :** 68

**Published Date :** 2014-04-02T02:28:08.000Z

Multiple Linear Regression Analysis, Evaluating Estimated Linear Regression Function (Looking at a single Independent Variable), basic approach to test relationships, (1) 𝐑^𝟐 Correlation between X (Independent Variable) & Y (Dependent Variable), F-Test, (2) Regression Analysis: If there is a significant relationship between X (Independent Variable) & Y (Dependent Variable), T-Test, (3) Explaining how to calculate the Degrees Of Freedom for the F-Test & T-Test, detailed discussion comparing two different regression equations to see which best predicts the dependent variable by Allen Mursau

**Channel Title :** MachineLearning with Python

**Views :** 361

**Likes :** 12

**DisLikes :** 0

**Published Date :** 2018-02-04T20:31:54.000Z

'''
RSquare and Adjusted RSquare
'''
import pandas as pd
df1 = pd.DataFrame()
X1 = [10,20,30,40,50]
Y1 = [3,4,2,5,6]
df1['X'] = X1
df1['Y'] = Y1
Ymean = df1['Y'].mean()
df1['Y-Ymean'] = df1['Y'] - Ymean
df1['(Y-Ymean)Square'] = (df1['Y'] - Ymean)**2
df1['Ybar'] = 1.9 + 0.07 * df1['X']
df1['Ybar - Ymean'] = df1['Ybar'] - Ymean
df1['(Ybar - Ymean)Square'] = (df1['Ybar'] - Ymean)**2
R_Square = df1['(Ybar - Ymean)Square'].sum() / df1['(Y-Ymean)Square'].sum()
'''
Adjusted_R_Square = 1 - (1 - R_Square)**2 * (N -1 ) / (N - K -1)
N = No of points in the data
K = no of independent variables
'''
N = 5
K = 1
Adjusted_R_Square = 1 - (1 - R_Square)**2 * (N -1 ) / (N - K -1)

**Channel Title :** Bionic Turtle

**Views :** 182674

**Likes :** 258

**DisLikes :** 31

**Published Date :** 2008-02-04T18:16:52.000Z

In a linear regression, you often see the R-squared quoted. To explain the R-squared (coefficient of determination), I compare it to the standard error of estimate (a measure of the line's accuracy) and the correlation (the square root of the coefficient of determination). All three are measures of the line's fit to the data. For more financial risk videos, visit our website! http://www.bionicturtle.com

**Channel Title :** James Gaskin

**Views :** 36888

**Likes :** 70

**DisLikes :** 1

**Published Date :** 2013-10-29T20:26:47.000Z

In this video I show how to display and find standardized regression weights and the R-square, or squared multiple correlations.

**Channel Title :** Sayed Hossain

**Views :** 14419

**Likes :** 45

**DisLikes :** 5

**Published Date :** 2011-12-31T21:52:16.000Z

=============================
Welcome to Hossain Academy
Homepage:https://www.sayedhossain.com
YouTube: https://www.youtube.com/user/sayedhossain23
Facebook:https://www.facebook.com/pages/Hossain-Academy/393927400736679
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**Channel Title :** StatQuest with Josh Starmer

**Views :** 7371

**Likes :** 179

**DisLikes :** 0

**Published Date :** 2018-06-18T18:38:47.000Z

This video follows from where we left off in Part 2 in this series on the details of Logistic Regression. Last time we saw how to fit a squiggly line to the data. This time we'll learn how to evaluate if that squiggly line is worth anything. In short, we'll calculate the R-squared value and it's associated p-value.
NOTE: This StatQuest assumes that you are already familiar with Part 1 in this series, Logistic Regression Details Pt1: Coefficients:
https://youtu.be/vN5cNN2-HWE
For a complete index of all the StatQuest videos, check out:
https://statquest.org/video-index/
If you'd like to support StatQuest, please consider a StatQuest t-shirt or sweatshirt...
https://teespring.com/stores/statquest
...or buying one or two of my songs (or go large and get a whole album!)
https://joshuastarmer.bandcamp.com/

**Channel Title :** Quantitative Specialists

**Views :** 21367

**Likes :** 103

**DisLikes :** 6

**Published Date :** 2014-10-07T14:00:02.000Z

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

**Channel Title :** atmananda1

**Views :** 62307

**Likes :** 100

**DisLikes :** 3

**Published Date :** 2010-11-07T22:56:50.000Z

excel scatter plot with r-squared value

**Channel Title :** Spreadsheet Solving

**Views :** 7605

**Likes :** 47

**DisLikes :** 1

**Published Date :** 2013-03-05T22:36:23.000Z

Discover statistical functions: correlation (r) and r-squared. Visit www.spreadsheetsolving.com for more videos and tutorials!

**Channel Title :** Phil Chan

**Views :** 18216

**Likes :** 26

**DisLikes :** 21

**Published Date :** 2012-09-06T10:41:58.000Z

Why is the regular R-squared not reported in logistic regression?
A look at the "Model Summary" and at the "Omnibus Test"
Visit me at: http://www.statisticsmentor.com

**Channel Title :** Taylor Smith

**Views :** 6713

**Likes :** 25

**DisLikes :** 5

**Published Date :** 2016-04-22T19:15:50.000Z

**Channel Title :** Allen Mursau

**Views :** 30002

**Likes :** 85

**DisLikes :** 2

**Published Date :** 2014-03-28T15:34:40.000Z

Multiple Regression Analysis, Multi-collinearity Model Testing, When Two or More Independent Variables Measure Same Thing, (Standard Errors are Large), Is Linear Regression Model Better When X's (Independent Variables) Are Combined Versus Used Separately ??, (1) Calculate Correlation Matrix (Multicollinearity), Need High Degree Correlation Between Y-Dependent & X's Independent Variables & (2) Need Low Degree Correlation Between X's Independent Variables, then Each X's Contribute To Regression Model, 𝐑^𝟐 always increases when Variables are added, but How does it affect 𝐑_𝐀𝐃𝐉 ??, Check If R Increases Or Decreases Upon Adding a Variable, detailed example by Allen Mursau

**Channel Title :** Nancy Miorelli

**Views :** 9404

**Likes :** 25

**DisLikes :** 3

**Published Date :** 2014-03-27T23:55:38.000Z

Excel File to Follow Along: https://drive.google.com/file/d/0BxXGvoyFS1KpRnNHZlRqRnkySmc/edit?usp=sharing

**Channel Title :** rishu rap rock

**Views :** 1359

**Likes :** 90

**DisLikes :** 12

**Published Date :** 2018-01-11T07:50:02.000Z

Suno mera gaana

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