Basic introduction to correlation - how to interpret correlation coefficient, and how to chose the right type of correlation measure for your situation.
0:00 Introduction to bivariate correlation
2:20 Why does SPSS provide more than one measure for correlation?
3:26 Example 1: Pearson correlation
7:54 Example 2: Spearman (rhp), Kendall's tau-b
15:26 Example 3: correlation matrix
I could make this video real quick and just show you Pearson's correlation coefficient, which is commonly taught in a introductory stats course. However, the Pearson's correlation IS NOT always applicable as it depends on whether your data satisfies certain conditions. So to do correlation analysis, it's better I bring together all the types of measures of correlation given in SPSS in one presentation.
Watch correlation and regression: https://youtu.be/tDxeR6JT6nM
-------------------------
Correlation of 2 rodinal variables, non monotonic
This question has been asked a few times, so I will make a video on it. But to answer your question, monotonic means in one direction. I suggest you plot the 2 variables and you'll see whether or not there is a monotonic relationship there. If there is a little non-monotonic relationship then Spearman is still fine. Remember we are measuring the TENDENCY for the 2 variables to move up-up/down-down/up-down together. If you have strong non-monotonic shape in the plot ie. a curve then you could abandon correlation and do a chi-square test of association - this is the "correlation" for qualitative variables. And since your 2 variables are ordinal, they are qualitative.
Good luck

Views: 524031
Phil Chan

Step-by-step instructions for calculating the correlation coefficient (r) for sample data, to determine in there is a relationship between two variables.

Views: 502765
Eugene O'Loughlin

Purchase the spreadsheet (formulas included!) that's used in this tutorial for $5: https://gum.co/satisfactionsurvey
-----
Soar beyond the dusty shelf report with my free 7-day course: https://depictdatastudio.teachable.com/p/soar-beyond-the-dusty-shelf-report-in-7-days/ Most "professional" reports are too long, dense, and jargony. Transform your reports with my course. You'll never look at reports the same way again.

Views: 392374
Ann K. Emery

In this brief presentation, Kelly Clement shows you what correlation analysis is, and how to use it in your market analysis.

Views: 34065
MetaStock

How to enter and analyze questionnaire (survey) data in SPSS is illustrated in this video. Lots more Questionnaire/Survey & SPSS Videos here: https://www.udemy.com/survey-data/?couponCode=SurveyLikertVideosYT
Check out our next text, 'SPSS Cheat Sheet,' here: http://goo.gl/b8sRHa. Prime and ‘Unlimited’ members, get our text for free. (Only 4.99 otherwise, but likely to increase soon.)
Survey data
Survey data entry
Questionnaire data entry
Channel Description: https://www.youtube.com/user/statisticsinstructor
For step by step help with statistics, with a focus on SPSS. Both descriptive and inferential statistics covered. For descriptive statistics, topics covered include: mean, median, and mode in spss, standard deviation and variance in spss, bar charts in spss, histograms in spss, bivariate scatterplots in spss, stem and leaf plots in spss, frequency distribution tables in spss, creating labels in spss, sorting variables in spss, inserting variables in spss, inserting rows in spss, and modifying default options in spss. For inferential statistics, topics covered include: t tests in spss, anova in spss, correlation in spss, regression in spss, chi square in spss, and MANOVA in spss. New videos regularly posted. Subscribe today!
YouTube Channel: https://www.youtube.com/user/statisticsinstructor
Video Transcript:
In this video we'll take a look at how to enter questionnaire or survey data into SPSS and this is something that a lot of people have questions with so it's important to make sure when you're working with SPSS in particular when you're entering data from a survey that you know how to do. Let's go ahead and take a few moments to look at that. And here you see on the right-hand side of your screen I have a questionnaire, a very short sample questionnaire that I want to enter into SPSS so we're going to create a data file and in this questionnaire here I've made a few modifications. I've underlined some variable names here and I'll talk about that more in a minute and I also put numbers in parentheses to the right of these different names and I'll also explain that as well. Now normally when someone sees this survey we wouldn't have gender underlined for example nor would we have these numbers to the right of male and female. So that's just for us, to help better understand how to enter these data. So let's go ahead and get started here. In SPSS the first thing we need to do is every time we have a possible answer such as male or female we need to create a variable in SPSS that will hold those different answers. So our first variable needs to be gender and that's why that's underlined there just to assist us as we're doing this. So we want to make sure we're in the Variable View tab and then in the first row here under Name we want to type gender and then press ENTER and that creates the variable gender. Now notice here I have two options: male and female. So when people respond or circle or check here that they're male, I need to enter into SPSS some number to indicate that. So we always want to enter numbers whenever possible into SPSS because SPSS for the vast majority of analyses performs statistical analyses on numbers not on words. So I wouldn't want and enter male, female, and so forth. I want to enter one's, two's and so on. So notice here I just arbitrarily decided males get a 1 and females get a 2. It could have been the other way around but since male was the first name listed I went and gave that 1 and then for females I gave a 2. So what we want to do in our data file here is go head and go to Values, this column, click on the None cell, notice these three dots appear they're called an ellipsis, click on that and then our first value notice here 1 is male so Value of 1 and then type Label Male and then click Add. And then our second value of 2 is for females so go ahead and enter 2 for Value and then Female, click Add and then we're done with that you want to see both of them down here and that looks good so click OK. Now those labels are in here and I'll show you how that works when we enter some numbers in a minute. OK next we have ethnicity so I'm going to call this variable ethnicity. So go ahead and type that in press ENTER and then we're going to the same thing we're going to create value labels here so 1 is African-American, 2 is Asian-American, and so on. And I'll just do that very quickly so going to Values column, click on the ellipsis. For 1 we have African American, for 2 Asian American, 3 is Caucasian, and just so you can see that here 3 is Caucasian, 4 is Hispanic, and other is 5, so let's go ahead and finish that. Four is Hispanic, 5 is other, so let's go to do that 5 is other. OK and that's it for that variable. Now we do have it says please state I'll talk about that next that's important when they can enter text we have to handle that differently.

Views: 632199
Quantitative Specialists

This video examines how to produce a correlation matrix on three or more variables in SPSS, including how to interpret the results.
Video Transcript: In this video we'll take a look at how to calculate a correlation matrix in SPSS. And a correlation matrix involves calculating all possible pairs of correlations. So, in this example here, notice there's 3 variables. For our correlation matrix, we'll calculate a correlation between SAT and social support, SAT and college GPA, and then finally social support and college GPA. And the way to do that is we want to go to Analyze and then Correlate and then select Bivariate. Now here we want to move our 3 variables over to the Variables box. We'll leave the default option, Pearson, selected, since we want the Pearson r correlation coefficient. We'll leave a two-tailed test selected as our default and then we'll go ahead and let SPSS flag the significant correlations. And we'll go ahead and test these correlations using an alpha of .05. Go ahead and click OK. And then here we get the output of our correlation matrix. And, as you can see here we have all of our variables SAT, social support, I'll go ahead and expand that a little bit, and college GPA in the columns then we have the same variables in the rows again. And where two variables meet or intersect, that's the bivariate correlation. Notice here on this main diagonal, we call it, these are all 1.0, or perfect correlations. Well that really shouldn't be surprising if you think about it, because this is the correlation of a variable with itself. So SAT with SAT, that's always going to be 1.0, social support with social support is 1, and college GPA with college GPA is 1. So these are always 1 on the main diagonal. What we want to look at is the off-diagonal. So either these 3 values right here in this lower triangle or these 3 values up here in the upper triangle. They're the exact same tests. So in other words, notice here we have social support and SAT, a correlation of .177. We have the exact same thing right here, SAT and social support, correlation of .177. So we'll just go and look at this lower triangle here. So notice that, as I said before, the correlation between social support and SAT is .177, college GPA and SAT is .650, and then finally social support and college GPA is .408. Now notice that these two correlations have asterisks next to them, and that this one has two asterisks and this one has one. Well if we look at the p-values here for each of these tests, which are shown under Sig., we can see that in fact the correlation between SAT and college GPA has a p-value of, it's reported as .000. What that really means is that it's less than .0005 and it's been rounded down. So this is a very small p-value, and this p-value is .015. Well notice that this p-value of .000 is less than .01, it's also less than .05. So this has two asterisks because, notice here on this note, 'correlation is significant at the .01 level,' since this p-value is less than .01, that's why we get the two asterisks here. Now for this correlation between social support and college GPA, notice the p-value here is .015. That's not less than .01, since this is .015, but it is less than .05. So it gets one asterisk, because it's less than .05, but not less than .01. Now we were testing these at .05, so we would say that SAT and college GPA, and social support and college GPA are both significant correlations. These correlations are significantly different than 0. And notice they're both positive, so higher scores on one variable are associated with higher scores on the other variable. Now social support and SAT is not significant, it had a p-value .310, and that's definitely greater than .05, so this correlation is not significant. So we cannot conclude that it's significantly different than 0. OK, so in summary, these two correlations are significant, and this one is not. And the correlation matrix calculated all these correlations at a single point in time. Now there's no limit to the number of variables you can calculate, you could have moved 10 variables over and had 45 correlations in that case. So there really is no limit all to the number of bivariate correlations you can calculate in your correlation matrix. Now we can also test if this entire matrix itself is significant. Now here I'm not talking about bivariate correlations, as we've done here, but this whole matrix at once, is it significant. And I'll do that in another video. Thanks for watching.

Views: 42752
Quantitative Specialists

In this video you will learn how to measure the strength of relation between variables by calculating correlation and interpreting it.
For Training & Study packs on Analytics/Data Science/Big Data, Contact us at [email protected]
Find all free videos & study packs available with us here:
http://analyticsuniversityblog.blogspot.in/
SUBSCRIBE TO THIS CHANNEL for free tutorials on Analytics/Data Science/Big Data/SAS/R/Hadoop

Views: 2918
Analytics University

After watching this video you would have learnt how to compute correlation between a categorical and a continuous variable and between two categorical variables
For Training & Study packs on Analytics/Data Science/Big Data, Contact us at [email protected]
Find all free videos & study packs available with us here:
http://analyticuniversity.com/
SUBSCRIBE TO THIS CHANNEL for free tutorials on Analytics/Data Science/Big Data/SAS/R/Hadoop

Views: 6637
Analytics University

Learn how to carry out tests for correlations in data using Microsoft Excel, including the Spearman’s rank correlation, and Pearson’s product moment correlation.
https://global.oup.com/academic/product/research-methods-for-the-biosciences-9780198728498
This video relates to section 9.5 in the book Research Methods for the Biosciences third edition by Debbie Holmes, Peter Moody, Diana Dine, and Laurence Trueman. The video is narrated by Laurence Trueman.
© Oxford University Press

Views: 1725
Oxford Academic (Oxford University Press)

/learn how to interpret a correlation matrix. http://youstudynursing.com/
Research eBook: http://amzn.to/1hB2eBd
Related Videos: http://www.youtube.com/playlist?list=PLs4oKIDq23Ac8cOayzxVDVGRl0q7QTjox
A correlation matrix displays the correlation coefficients among numerous variables in a research study. This type of matrix will appear in hypothesis testing or exploratory quantitative research studies, which are designed to test the relationships among variables. In order to interpret this matrix you need to understand how correlations are measured. Correlation coefficients always range from -1 to +1. The positive or negative sign tells you the direction of the relationship and the number tells you the strength of the relationship. The most common way to quantify this relationship is the Pearson product moment correlation coefficient (Munro, 2005). Mathematically it is possible to calculate correlations with any level of data. However, the method of calculating these correlations will differ based on the level of the data. Although Pearson's r is the most commonly used correlation coefficient, Person's r is only appropriate for correlations between two interval or ratio level variables. When examining the formula for Person's r it is evident that part of the calculation relies on knowing the difference between individual cases and the mean. Since the distance between values is not known for ordinal data and a mean cannot be calculated, Pearson's r cannot be used. Therefore another method must be used. ... Recall that correlations measure both the direction and strength of a linear relationship among variables. The direction of the relationship is indicated by the positive or negative sign before the number.
If the correlation is positive it means that as one variable increases so does the other one. People who tend to score high for one variable will also tend to score high for another varriable.
Therefore if there is a positive correlation between hours spent watching course videos and exam marks it means that people who spend more time watching the videos tend to get higher marks on the exam.
Remember that a positive correlation is like a positive relationship, both people are moving in the same direction through life together.
If the correlation is negative it means that as one variable increases the other decreases. People who tend to score high for one variable will tend to score low for another.
Therefore if there is a negative correlation between unmanaged stress and exam marks it means that people who have more unmanaged stress get lower marks on their exam.
Remember that A negative correlation is like a negative relationship, the people in the relationship are moving in opposite directions. Remember that The sign (positive or negative) tells you the direction of the relationship and the number beside it tells you how strong that relationship is. To judge the strength of the relationship consider the actual value of the correlation coefficient. Numerous sources provide similar ranges for the interpretation of the relationships that approximate the ranges on the screen. These ranges provide guidelines for interpretation. If you need to memorize these criteria for a course check the table your teacher wants you to learn. Of course, the higher the number is the stronger the relationship is. In practice, researchers are happy with correlations of 0.5 or higher.
Also note that when drawing conclusions from correlations the size of the sample as well as the statistical significance is considered.
Remember that the direction of the relationship does not affect the strength of the relationship. One of the biggest mistakes people make is assuming that a negative number is weaker than a positive number. In fact, a correlation of -- 0.80 is just as high or just as strong as a correlation of +0.80. When comparing the values on the screen a correlation of -0.75 is actually stronger than a correlation of +0.56.
... Notice that there are correlations of 1 on a diagonal line across the table. That is because each variable should correlate perfectly with itself. Sometimes dashes are used instead of 1s.
In a correlation matrix, typically only one half of the triangle is filled out. That is because the other half would simply be a mirror image of it. Examine this correlation matrix and see if you can identify and interpret the correlations. A great question for an exam would be to give you a correlation matrix and ask you to find and interpret correlations.
What is the correlation between completed readings and unmanaged stress? What does it mean? Which coefficient gives you the most precise prediction? Which correlations are small enough that they would not be of much interest to the researcher? Which two correlations have the same strength? From looking at these correlations, what could a student do to get a higher mark on an exam?
Comment below to start a conversation.

Views: 57161
NurseKillam

We demonstrate installing Data Analysis ToolPak excel addin and how to build a Karl Pearson Correlation Matrix easily. The data set used can be downloaded from http://www.learnanalytics.in/blog/?p=150 .
Please subscribe to our channel to receive updates and also join our Linkedin Group for latest training videos and articles @http://www.linkedin.com/groups/Learn-Analytics-step-time-4240061

Views: 121196
Learn Analytics

This video discusses numerical and graphical methods for exploring relationships between two categorical variables, using contingency tables, segmented bar plots, and mosaic plots.

Views: 28756
Mine Çetinkaya-Rundel

Spreadsheet used in this video: https://docs.google.com/spreadsheets/d/13j77H4k4q_dJqJEUu2dNaJP_Aat9fbvU4X6cKUNfTMs/copy
Created with TechSmith Snagit for Google Chrome™
http://goo.gl/ySDBPJ

Views: 2734
Josh Borzick

Alan Jackson, President and CEO of The Jackson Group, talks about the benefits of correlating data from different surveys. Surveying, while important, is only the first step. Knowing what to do with the data and how it impacts and meshes with data from other areas is key.

Views: 963
JacksonGroupInc

How to do Bivariate Analysis when one variable is Categorical and the other is Numerical
Analysis of Variance
ANOVA test
My website: http://people.brunel.ac.uk/~csstnns

Views: 9260
Noureddin Sadawi

Trading clues from the markets engine . http://www.financial-spread-betting.com/course/technical-analysis.html PLEASE LIKE, SUBSCRIBE AND SHARE THIS VIDEO SO WE CAN DO MORE! How to trade market correlations... Let's first see why market correlation matters. We often have a market that is being moved by the power of another market. I call that the market's engine. It doesn't always apply and often it is very broad - for instance trade war with China could be one of the engines, the price of OIl could be another, the price of Yen is another engine..etc These things often happen in cycles throughout the years depending on the global macro events at the time. Irrespective of this I'm going to look at trading clues from the markets' engine when it happens to be very correlated on a 1 day or 2 day basis.
Examples of Market Correlations
Inverse proportion of USD strengthening to Gold. This is the assumption but you also have to see what happens in real-time. Do we trade the USD strength or the secondary plays? In my experience the secondary plays is where the edge is. So instead of buying the dollar you could say, short Gold.
Oil and explorers.
Gold falling and miners falling. If gold is down heavily, miners will make even less money. When I used to trade miners on the London Stock Exchange, the big tier miners would all move in one direction and the smaller miners would rise but lag behind.
Related Videos
What The Market Open Tells You! Part 1 🔍
https://www.youtube.com/watch?v=du0521KDJqk
Useful Trading Clues from the Market Open Part 2 🍳👌
https://www.youtube.com/watch?v=idDRo-L_OvM
What The Market Close Tells You! Part 1 🔍
https://www.youtube.com/watch?v=vzX50IJhwag
Useful Trading Tips From the Market Close Part 2 🔍
https://www.youtube.com/watch?v=QIbMKnaYBSY
Trading Clues From a Large Daily Range 👌
https://www.youtube.com/watch?v=nOKe5wG4AC4

Views: 1282
UKspreadbetting

Currell: Scientific Data Analysis. Minitab analysis for Figs 9.6 and 9.7 http://ukcatalogue.oup.com/product/9780198712541.do
© Oxford University Press

Views: 300922
Oxford Academic (Oxford University Press)

Click this link for more information on correlation coefficients plus more FREE Excel videos and tips:
http://www.statisticshowto.com/what-is-the-pearson-correlation-coefficient/

Views: 49396
Stephanie Glen

In a perfect world, we could calculate ROI from individual activities such as SEO, Social Media, website design, brochures, cooperative marketing efforts, etc. But consumer purchase decisions often take in to account multiple “touch points” before a buying decision is made. Also, the buying cycle may be 3-12 months; our client’s do sell milk, eggs, or bread.
How do you connect today’s marketing activities with a purchase 6 months from now?
Calculating ROI should be a long-term effort. If clients shared monthly sales data we could correlate sales with various metrics such as (1) Website visits, (2) social media metrics, (3) leads from website forms, and so on.

Views: 46
Matterhorn Business Solutions Inc.

Learn how to use the CORREL function and to manually calculate the correlation coefficient (r) in Excel 2010. This allows you to examine is there is a statistical correlation between two variables.
Please note: Correlation is NOT causation!

Views: 149228
Eugene O'Loughlin

Create a correlation matrix between any two stocks using Google Docs. This is a much more flexible platform than excel as data and dates are updated for you.

Views: 3659
Al On Options

Do re-assigned incidents correlate with a higher percentage of SLA breaches?

Views: 270
Northcraft Analytics

Learn how to use NVivo's text analysis features to help you identify themes and explore the use of language in your project.
For more information about NVivo visit: http://bit.ly/sQbS3m

Views: 110131
NVivo by QSR

This video shows how to perform correlation analysis between two or more than two variables. Correlation analysis shows the degree of association between two variables

Views: 995
Excel Tutorials with Wajid Raza

For numerical data with two or more variables, a correlation table
examines the correlations between all pairs of variables.
Windows Excel can construct a correlation table using the Data
Analysis Toolpak, which you should already have enabled as an add-in for
Excel.
First, start Excel and open your data set. I will assume that your
data is arranged with the variables as columns and that your data also
includes column headings.
Next, click the Data tab and choose Data Analysis. If you do not see the
Data Analysis option here, then you have not enabled the Data Analysis
Toolpak correctly.
In the next box, select Correlation and click OK. You will
then be presented with a dialog box requiring a couple of inputs.
For the Input Range, select the rectangular array of cells around the
numerical columns that you would like to analyze. Also, click that
the labels are in the first row. Other options may be left at their
defaults. When ready, click OK.
After a moment, Excel will produce a new tab with the desired
correlation table.

Views: 72136
Sam Burer

Views: 58
Clay Rowell

Instructional video on how to analyze subsets and groups of data using SPSS, statistical analysis and data management software.
For more information, visit SSDS at https://ssds.stanford.edu.

Views: 15667
Stanford University Libraries

This tutorial discusses using the timestamp template correlation wizard and correlating up to three log files.

Views: 801
TechLiveUser

In this video I show you how to create a new categorical variable from a continuous variable (e.g., high and low age). This is also known as a 'median split' approach.

Views: 59385
James Gaskin

http://alphabench.com/data/excel-correlation-matrix-tutorial.html
This tutorial demonstrates how to create a correlation matrix in Excel. The example used in the video is for stock price changes over a one year period.
The spreadsheet in the is example can be downloaded by visiting:
http://www.alphabench.com/resources.html

Views: 65460
Matt Macarty

This screen cast demonstrates the use of Microsoft Excel to organize information for qualitative research.

Views: 44484
tamuwritingcenter

Video tutorial on how to calculate a weighted mean (weighted average)
Like us on: http://www.facebook.com/PartyMoreStudyLess

Views: 99072
statisticsfun

A common task for scientists and engineers is to analyze data from an external source. By importing the data into Python, data analysis such as statistics, trending, or calculations can be made to synthesize the information into relevant and actionable information. See http://apmonitor.com/che263/index.php/Main/PythonDataAnalysis

Views: 184434
APMonitor.com

In this video, Dr Greg Martin provides an introduction to research methods, methedology and study design. Specifically he takes a look at qualitative and quantitative research methods including case control studies, cohort studies, observational research etc.
Global health (and public health) is truly multidisciplinary and leans on epidemiology, health economics, health policy, statistics, ethics, demography.... the list goes on and on. This YouTube channel is here to provide you with some teaching and information on these topics. I've also posted some videos on how to find work in the global health space and how to raise money or get a grant for your projects.
Please feel free to leave comments and questions - I'll respond to all of them (we'll, I'll try to at least). Feel free to make suggestions as to future content for the channel.
SUPPORT:
—————-
This channel has a crowd-funding campaign (please support if you find these videos useful).
Here is the link: http://bit.ly/GH_support
OTHER USEFUL LINKS:
————————
Channel page: http://bit.ly/GH_channel
Subscribe: http://bit.ly/GH_subscribe
Google+: http://bit.ly/GH_Google
Twitter: @drgregmartin
Facebook: http://bit.ly/GH_facebook
HERE ARE SOME PLAYLISTS
——————————————-
Finding work in Global Health: http://bit.ly/GH_working
Epidemiology: http://bit.ly/GH_epi
Global Health Ethics: http://bit.ly/GH_ethics
Global Health Facts: http://bit.ly/GH_facts
WANT CAREER ADVICE?
————————————
You can book time with Dr Greg Martin via Google Helpouts to get advice about finding work in the global health space.
Here is the link: http://bit.ly/GH_career
-~-~~-~~~-~~-~-
Please watch: "Know how interpret an epidemic curve?"
https://www.youtube.com/watch?v=7SM4PN7Yg1s
-~-~~-~~~-~~-~-

Views: 335008
Global Health with Greg Martin

Learn the difference between Nominal, ordinal, interval and ratio data. http://youstudynursing.com/
Research eBook on Amazon: http://amzn.to/1hB2eBd
Check out the links below and SUBSCRIBE for more youtube.com/user/NurseKillam
For help with Research - Get my eBook "Research terminology simplified: Paradigms, axiology, ontology, epistemology and methodology" here: http://www.amazon.com/dp/B00GLH8R9C
Related Videos: http://www.youtube.com/playlist?list=PLs4oKIDq23AdTCF0xKCiARJaBaSrwP5P2
Connect with me on
Facebook Page: https://www.facebook.com/NursesDeservePraise
Twitter: @NurseKillam https://twitter.com/NurseKillam
Facebook: https://www.facebook.com/laura.killam
LinkedIn: http://ca.linkedin.com/in/laurakillam
Quantitative researchers measure variables to answer their research question.
The level of measurement that is used to measure a variable has a significant impact on the type of tests researchers can do with their data and therefore the conclusions they can come to. The higher the level of measurement the more statistical tests that can be run with the data. That is why it is best to use the highest level of measurement possible when collecting information.
In this video nominal, ordinal, interval and ratio levels of data will be described in order from the lowest level to the highest level of measurement. By the end of this video you should be able to identify the level of measurement being used in a study. You will also be familiar with types of tests that can be done with each level.
To remember these levels of measurement in order use the acronym NOIR or noir.
The nominal level of measurement is the lowest level. Variables in a study are placed into mutually exclusive categories. Each category has a criteria that a variable either has or does not have. There is no natural order to these categories.
The categories may be assigned numbers but the numbers have no meaning because they are simply labels. For example, if we categorize people by hair color people with brown hair do not have more or less of this characteristic than those with blonde hair.
Nominal sounds like name so it is easy to remember that at a nominal level you are simply naming categories.
Sometimes researchers refer to nominal data as categorical or qualitative because it is not numerical.
Ordinal data is also considered categorical. The difference between nominal and ordinal data is that the categories have a natural order to them. You can remember that because ordinal sounds like order.
While there is an order, it is also unknown how much distance is between each category.
Values in an ordinal scale simply express an order.
All nominal level tests can be run on ordinal data.
Since there is an order to the categories the numbers assigned to each category can be compared in limited ways beyond nominal level tests. It is possible to say that members of one category have more of something than the members of a lower ranked category. However, you do not know how much more of that thing they have because the difference cannot be measured.
To determine central tendency the categories can be placed in order and a median can now be calculated in addition to the mode.
Since the distance between each category cannot be measured the types of statistical tests that can be used on this data are still quite limited. For example, the mean or average of ordinal data cannot be calculated because the difference between values on the scale is not known.
Interval level data is ordered like ordinal data but the intervals between each value are known and equal. The zero point is arbitrary. Zero simply represents an additional point of measurement.
For example, tests in school are interval level measurements of student knowledge. If you scored a zero on a math test it does not mean you have no knowledge. Yet, the difference between a 79 and 80 on the test is measurable and equal to the difference between an 80 and an 81.
If you know that the word interval means space in between it makes remembering what makes this level of measurement different easy.
Ratio measurement is the highest level possible for data. Like interval data, Ratio data is ordered, with known and measurable intervals between each value. What differentiates it from interval level data is that the zero is absolute. The zero occurs naturally and signifies the absence of the characteristic being measured. Remember that Ratio ends in an o therefore there is a zero.
Typically this level of measurement is only possible with physical measurements like height, weight and length.
Any statistical tests can be used with ratio level data as long as it fits with the study question and design.

Views: 339787
NurseKillam

Views: 29739
PUB708 Team

PANEL 2: BIG DATA
The Internet, social media, and data mining have changed language and our ability to analyze usage, and increased sensitivities to the power of the words we use. This panel will explore how these new forms of discourse and analysis expand our understanding of the interplay of gender, personal narrative, and language, as well as data scraping that enables a statistical study of language usage by demographics.
Ben Hookway (7:43), Chief Executive Officer, Relative Insight
Lyle Ungar (20:53), Professor and Graduate Group Chair, Computer and Information Science, University of Pennsylvania
Alice E. Marwick (36:19), Assistant Professor, Department of Communication and Media Studies, and Director, McGannon Center for Communication Research, Fordham University
Moderator: Rebecca Lemov, Associate Professor of the History of Science, Harvard University
Q&A (52:02)

Views: 1125
Harvard University

Several viewers asked me to demonstrate some other ways to -Group a Field- in a Pivot Table. In this tutorial, I show you how to create a Frequency Report by Age Bracket.
Before I start the Pivot Table Demonstration, I show you how to calculate someone's age using the -undocumented- =DATEDIF() Function in Excel.
In the Pivot Table, I change the result from -Summarize Values By- to the -Show Vales As- setting. Finally, I apply Conditional Formatting to the Pivot Table.
I invite you learn more about my Pivot Table Video Tutorials. Visit my secure, online, shopping website - http://shop.thecompanyrocks.com
Danny Rocks
The Company Rocks

Views: 180003
Danny Rocks

NIH hosted a public workshop on the NIH campus in Bethesda, Maryland, April 28-29, 2015, to consider visionary biomedical questions that could be addressed by the proposed national research cohort of one million or more volunteer participants. The workshop will result in a series of use cases describing the distinctive science that the cohort could enable in the near term and longer term.
This workshop is one of four that is being convened by the Precision Medicine Initiative Working Group of the Advisory Committee to the (NIH) Director to help inform the vision for building the PMI national participant group that they have been tasked to develop. For more information on the workshop and PMI, visit http://www.nih.gov/precisionmedicine
Agenda and time codes:
Welcome - Bray Patrick Lake - 00:01
Near-Term Use Cases - Dr. Kathy Hudson - 02:54
Longer-Term Use Cases - Dr. Sachin Kheterpal - 1:35:45
Recap and Next Steps - Dr. Rick Lifton - 2:51:25

Views: 909
All of Us Research Program

Navigation on video fragments: http://www.rotmistrov.com/faen (after entering, use the navigation area on the page's right side)
In joint factor models, the components’ influence is mixed. In contrast, pure factor models are always one-component ones. That is why there is pure component’s influence in a pure factor model. In my example, I examed whether the pure models are correlated. I found out that the genetic correlation was weak. Thus I rejected the hypothesis that the initial model’s components are genetically correlated.

Views: 38
Алексей Ротмистров

Data Science for Human Well-being
Abstract:
The popularity of wearable and mobile devices, including smartphones and smartwatches, has generated an explosion of detailed behavioral data. These massive digital traces provides us with an unparalleled opportunity to realize new types of scientific approaches that provide novel insights about our lives, health, and happiness. However, gaining valuable insights from these data requires new computational approaches that turn observational, scientifically "weak" data into strong scientific results and can computationally test domain theories at scale. In this talk, I will describe novel computational methods that leverage digital activity traces at the scale of billions of actions taken by millions of people. These methods combine insights from data mining, social network analysis, and natural language processing to generate actionable insights about our physical and mental well-being. Specifically, I will describe how massive digital activity traces reveal unknown health inequality around the world, and how personalized predictive models can target personalized interventions to combat this inequality. I will demonstrate that modelling how fast we are using search engines enables new types of insights into sleep and cognitive performance. Further, I will describe how natural language processing methods can help improve counseling services for millions of people in crisis. I will conclude the talk by sketching interesting future directions for computational approaches that leverage digital activity traces to better understand and improve human well-being.
Bio:
Tim Althoff is a Ph.D. candidate in Computer Science in the Infolab at Stanford University, advised by Jure Leskovec. His research advances computational methods to improve human well-being, combining techniques from Data Mining, Social Network Analysis, and Natural Language Processing. Prior to his PhD, Tim obtained M.S. and B.S. degrees from Stanford University and University of Kaiserslautern, Germany. He has received several fellowships and awards including the SAP Stanford Graduate Fellowship, Fulbright scholarship, German Academic Exchange Service scholarship, the German National Merit Foundation scholarship, and a Best Paper Award by the International Medical Informatics Association. Tim's research has been covered internationally by news outlets including BBC, CNN, The Economist, The Wall Street Journal, and The New York Times.
April 17, 2018
This video is CC.

Views: 873
Paul G. Allen School

Through genome sequencing, in combination with other omic information such as microbiome, methylome, metabolome, etc., data can be used to genetically predict disease risk. This information, combined with data collected through technology such as wearables, can help people manage disease and maintain healthy lives. Join Dr. Michael Snyder and Dr. Barry Starr as they explore the advances in genomic sequencing and how it can be used to predict, diagnose, and treat disease.
You will learn:
How genomics can be used to predict disease
The poser of longitudinal profiling
What data is collected from wearables and how they’re valuable to monitoring health
How genome sequencing and big data can impact your health
About the Speaker
Michael Snyder is the Stanford Ascherman Professor and Chair of Genetics and the Director of the Center of Genomics and Personalized Medicine. Dr. Snyder received his Ph.D. training at the California Institute of Technology and carried out postdoctoral training at Stanford University. He is a leader in the field of functional genomics and proteomics, and one of the major participants of the ENCODE project. His laboratory study was the first to perform a large-scale functional genomics project in any organism, and has launched many technologies in genomics and proteomics that have been used for characterizing genomes, proteomes and regulatory networks.

Views: 4328
stanfordonline

Source:
https://www.spreaker.com/user/dabcc/machine-learning-analytics-software-plat
In episode 233, Douglas Brown interviews Jerry Melnick, Chief Operating Officer at SIOS Technology Corp. Jerry and Douglas discuss the new SIOS iQ machine learning analytics software platform. Jerry does a great job diving deep in to SIOS iQ, what it is, how it works, why we should care and much more! Truly a much listen to episode!
About SIOS iQ
SIOS iQ is a machine learning analytics software platform designed to be your primary resource for IT operations information and issue resolution. SIOS iQ optimizes VMware environments to ensure business critical application environments are optimized for performance, efficiency, reliability, and capacity.
Major features of the standard edition of SIOS iQ include:
Performance Root Cause Analysis learns the relationships of objects and their normal patterns of behavior in a VMware infrastructure (hosts, VMs, application, network, storage, etc.); proactively identifies anomalies in behavior and the root causes of performance problems in any application; and recommends specific changes to resolve those problems.
SIOS PERC Dashboard™ enables IT managers to quickly and easily ensure their VMware environment is optimized along four key quality of service dimensions: performance, efficiency, reliability and capacity (PERC). Provides mobile application ease of-use. The standard edition of SIOS iQ includes a variety of user enhancements, including the ability to expand charts to drill deeply into specific PERC areas, color-coded status indicators showing the criticality of issues - critical, warning and informational, and the inclusion of performance impact analysis showing all applications, VMs, hosts and data stores associated with a detected performance problem.
Specialized Analytics for SQL Server provides advanced insight into performance issues associated with SQL Server deployments in VMware. SIOS iQ standard edition correlates interactions between SQL and infrastructure resources in the VMware environment to identify the deep root cause of performance issues.
Enhanced Host Based Caching feature helps IT staff to easily determine how to improve storage performance for applications by using server side storage and host based caching (HBC). It analyzes the environment, including all blocks written to disk, and identifies the read ratio and the load profile to identify the VMs (and their disks) that will benefit most from HBC. SIOS iQ makes specific configuration recommendations such as how much cache to add and what cache block size to configure. It predicts the added performance that will be achieved if recommendations are implemented and shows the results in a single, easy-to-read chart.
SIOS iQ Resource Optimization features. New standard edition of SIOS iQ provides an enhanced user interface for optimizing VMware resources by identifying and eliminating idle VMs and snapshot sprawl. SIOS iQ identifies under-used virtual machines and unnecessary snapshots and predicts the potential monthly savings that can be realized by eliminating them.
Download a free version and/or trial
Follow on Twitter account @SIOSTECH
email: [email protected]

Views: 60
IT News

Can Twitter predict heart disease? Day in and day out, we use social media, making it the center of our social lives, work lives, and private lives. Lyle Ungar reveals how our behavior on social media actually reflects aspects about our health and happiness.
Lyle Ungar is a professor of Computer and Information Science and Psychology at the University of Pennsylvania and has analyzed 148 million tweets from more than 1,300 counties that represent 88 percent of the U.S. population. His published research has been focused around the area of text mining. He has published over 200 articles and holds eleven patents. His current research deals with statistical natural language processing, spectral methods, and the use of social media to understand the psychology of individuals and communities.
This talk was given at a TEDx event using the TED conference format but independently organized by a local community. Learn more at http://ted.com/tedx

Views: 4822
TEDx Talks

https://www.census.gov/about/cac/sac/meetings/2019-03-meeting.html

Views: 633
uscensusbureau

Correlations that lack statistical control. Multiple linear regression models have statistical control--with an example.

Views: 133
Dr. Douglas Dean

October 15, 2009. The skin creates a barrier between the body and the environment. Using animal models, Dr. Julie Segre's laboratory focuses on the genetic pathways involved in building and repairing this skin barrier. The Segre laboratory estimates that approximately one million bacteria reside on each square centimeter of skin and many common skin conditions are associated with both impaired skin barrier function and increased microbial colonization. Dr. Segre moderated the discussion, answered questions and addressed comments. In addition, the webinar discussed details of the Human Microbiome Project. More: http://www.genome.gov/27535715

Views: 8282
National Human Genome Research Institute

Check out my Blog:
http://exceltraining101.blogspot.com
This video covers how to convert or change text to numbers using several methods. Also how to convert or change numbers to text using some different methods.
#exceltips
#exceltipsandtricks
#exceltutorial
#doughexcel
---------------------
Excel Training:
https://www.exceltraining101.com/p/training.html
Excel Books:
https://www.amazon.com/shop/dough

Views: 433800
Doug H

At the 2015 APS Annual Convention, APS James McKeen Cattell Fellow Ian J. Deary discussed using the Scottish Mental Surveys, how intelligence test scores relate to aspects of people’s lives and stories of participants from the studies.

Views: 1972
PsychologicalScience