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Search results “What type of data analysis to use”

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Seven different statistical tests and a process by which you can decide which to use. The tests are: Test for a mean, test for a proportion, difference of proportions, difference of two means - independent samples, difference of two means - paired, chi-squared test for independence and regression. This video draws together videos about Helen, her brother, Luke and the choconutties. There is a sequel to give more practice choosing and illustrations of the different types of test with hypotheses.
Views: 803136 Dr Nic's Maths and Stats

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In common health care research, some hypothesis tests are more common than others. How do you decide, between the common tests, which one is the right one for your research? Thank you to the Statistical Learning Center for their excellent video on the same topic. https://www.youtube.com/rulIUAN0U3w
Views: 406351 Erich Goldstein

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This video explains the differences between parametric and nonparametric statistical tests. The assumptions for parametric and nonparametric tests are discussed including the Mann-Whitney Test, Kruskal-Wallis Test, Wilcoxon Signed-Rank Test, and Friedman’s ANOVA.
Views: 177805 Dr. Todd Grande

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The kind of graph and analysis we can do with specific data is related to the type of data it is. In this video we explain the different levels of data, with examples. Subtitles in English and Spanish.
Views: 947449 Dr Nic's Maths and Stats

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A step-by-step approach for choosing an appropriate statistcal test for data analysis.

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Data falls into several categories. Each type has some pros and cons, and is best suited for specific needs. Learn more in this short video from our Data Collection DVD available at http://www.velaction.com/data-collection-lean-training-on-dvd/.
Views: 157140 VelactionVideos

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This video part of our online course Become a Data Analyst, it helps to understand types of data source available and which one to select. Our course doesn’t only seek to teach you about data analysis but also helps you learn how to apply it in real-life situations. Apart from detailed programs on learning the basics of Python and the art of data analysis using Python, the course provides you with five projects that are real-life case studies. Check our tutorial on BECOME A DATA ANALYST using Python: http://yodalearning.com/courses/become-a-data-analyst-using-python On top of that… ALSO CHECK SOME OF THE DEMO COURSES WE OFFER http://yodalearning.com/p/advanced-online-courses Do not miss out from any videos and course offers. Follow us now! http://www.facebook.com/yodalearning http://www.twitter.com/yodalearning Check out more of our youtube video: https://www.youtube.com/channel/UCDLslmxC07X9DBun71c62sQ

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A description of the concepts behind Analysis of Variance. There is an interactive visualization here: http://demonstrations.wolfram.com/VisualANOVA/ but I have not tried it, and this: http://rpsychologist.com/d3-one-way-anova has another visualization
Views: 575370 J David Eisenberg

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@ Members ~ This video would let you know about rising importance of Analytics where by we are covering all 4 Branches of Analytics like Financial Analytics , Risk Based Analytics , Cash Flow Analytics and Data Analytics. Video would also let you know about 3 types of Analytics covering Descriptive Analytics , Predictive Analytics and Prescriptive Analytics. You are most welcome to connect with us at 91-9899242978 (Handheld) , Skype ~ Rahul5327 , Twitter @ Rahulmagan8 , [email protected] , [email protected] or visit our website - www.treasuryconsulting.in

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Views: 370357 Simplilearn

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This video reviews the scales of measurement covered in introductory statistics: nominal, ordinal, interval, and ratio (Part 1 of 2). Scales of Measurement Nominal, Ordinal, Interval, Ratio YouTube Channel: https://www.youtube.com/user/statisticsinstructor Subscribe today! Lifetime access to SPSS videos: http://tinyurl.com/m2532td Video Transcript: In this video we'll take a look at what are known as the scales of measurement. OK first of all measurement can be defined as the process of applying numbers to objects according to a set of rules. So when we measure something we apply numbers or we give numbers to something and this something is just generically an object or objects so we're assigning numbers to some thing or things and when we do that we follow some sort of rules. Now in terms of introductory statistics textbooks there are four scales of measurement nominal, ordinal, interval, and ratio. We'll take a look at each of these in turn and take a look at some examples as well, as the examples really help to differentiate between these four scales. First we'll take a look at nominal. Now in a nominal scale of measurement we assign numbers to objects where the different numbers indicate different objects. The numbers have no real meaning other than differentiating between objects. So as an example a very common variable in statistical analyses is gender where in this example all males get a 1 and all females get a 2. Now the reason why this is nominal is because we could have just as easily assigned females a 1 and males a 2 or we could have assigned females 500 and males 650. It doesn't matter what number we come up with as long as all males get the same number, 1 in this example, and all females get the same number, 2. It doesn't mean that because females have a higher number that they're better than males or males are worse than females or vice versa or anything like that. All it does is it differentiates between our two groups. And that's a classic nominal example. Another one is baseball uniform numbers. Now the number that a player has on their uniform in baseball it provides no insight into the player's position or anything like that it just simply differentiates between players. So if someone has the number 23 on their back and someone has the number 25 it doesn't mean that the person who has 25 is better, has a higher average, hits more home runs, or anything like that it just means they're not the same playeras number 23. So in this example its nominal once again because the number just simply differentiates between objects. Now just as a side note in all sports it's not the same like in football for example different sequences of numbers typically go towards different positions. Like linebackers will have numbers that are different than quarterbacks and so forth but that's not the case in baseball. So in baseball whatever the number is it provides typically no insight into what position he plays. OK next we have ordinal and for ordinal we assign numbers to objects just like nominal but here the numbers also have meaningful order. So for example the place someone finishes in a race first, second, third, and so on. If we know the place that they finished we know how they did relative to others. So for example the first place person did better than second, second did better than third, and so on of course right that's obvious but that number that they're assigned one, two, or three indicates how they finished in a race so it indicates order and same thing with the place finished in an election first, second, third, fourth we know exactly how they did in relation to the others the person who finished in third place did better than someone who finished in fifth let's say if there are that many people, first did better than third and so on. So the number for ordinal once again indicates placement or order so we can rank people with ordinal data. OK next we have interval. In interval numbers have order just like ordinal so you can see here how these scales of measurement build on one another but in addition to ordinal, interval also has equal intervals between adjacent categories and I'll show you what I mean here with an example. So if we take temperature in degrees Fahrenheit the difference between 78 degrees and 79 degrees or that one degree difference is the same as the difference between 45 degrees and 46 degrees. One degree difference once again. So anywhere along that scale up and down the Fahrenheit scale that one degree difference means the same thing all up and down that scale. OK so if we take eight degrees versus nine degrees the difference there is one degree once again. That's a classic interval scale right there with those differences are meaningful and we'll contrast this with ordinal in just a few moments but finally before we do let's take a look at ratio.
Views: 402305 Quantitative Specialists

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statisticslectures.com - where you can find free lectures, videos, and exercises, as well as get your questions answered on our forums!
Views: 433478 statslectures

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Dr. Lisa Moyer at EIU discusses what type of statistical analysis is appropriate to use to answer research questions or test hypotheses.
Views: 261 Lisa Moyer

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( Correction - Pen was assumed name instead of auther) T-test, Z-test, F-yest, Chi square test. For different competitive exams Keep watching chanakya group of economics.

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An explanation of how to compute the chi-squared statistic for independent measures of nominal data. For an explanation of significance testing in general, see http://evc-cit.info/psych018/hyptest/index.html There is also a chi-squared calculator at http://evc-cit.info/psych018/chisquared/index.html
Views: 1024046 J David Eisenberg

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How to perform a simple t-test in Microsoft Excel
Views: 1272485 Jim Grange

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Qualitative research is a strategy for systematic collection, organization, and interpretation of phenomena that are difficult to measure quantitatively. Dr. Leslie Curry leads us through six modules covering essential topics in qualitative research, including what it is qualitative research and how to use the most common methods, in-depth interviews and focus groups. These videos are intended to enhance participants' capacity to conceptualize, design, and conduct qualitative research in the health sciences. Welcome to Module 5. Bradley EH, Curry LA, Devers K. Qualitative data analysis for health services research: Developing taxonomy, themes, and theory. Health Services Research, 2007; 42(4):1758-1772. Learn more about Dr. Leslie Curry http://publichealth.yale.edu/people/leslie_curry.profile Learn more about the Yale Global Health Leadership Institute http://ghli.yale.edu
Views: 175790 YaleUniversity

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This webinar provides an overview of basic quantitative analysis, including the types of variables and statistical tests commonly used by Student Affairs professionals. Specifically discussed are the basics of Chi-squared tests, t-tests, and ANOVAs, including how to read an SPSS output for each of these tests.
Views: 22342 CSSLOhioStateU

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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: 528231 Phil Chan

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Excel file: https://dl.dropboxusercontent.com/u/561402/TTEST.xls In this video Paul Andersen explains how to run the student's t-test on a set of data. He starts by explaining conceptually how a t-value can be used to determine the statistical difference between two samples. He then shows you how to use a t-test to test the null hypothesis. He finally gives you a separate data set that can be used to practice running the test. Do you speak another language? Help me translate my videos: http://www.bozemanscience.com/translations/ Music Attribution Intro Title: I4dsong_loop_main.wav Artist: CosmicD Link to sound: http://www.freesound.org/people/CosmicD/sounds/72556/ Creative Commons Atribution License Outro Title: String Theory Artist: Herman Jolly http://sunsetvalley.bandcamp.com/track/string-theory All of the images are licensed under creative commons and public domain licensing: 1.3.6.7.2. Critical Values of the Student’s-t Distribution. (n.d.). Retrieved April 12, 2016, from http://www.itl.nist.gov/div898/handbook/eda/section3/eda3672.htm File:Hordeum-barley.jpg - Wikimedia Commons. (n.d.). Retrieved April 11, 2016, from https://commons.wikimedia.org/wiki/File:Hordeum-barley.jpg Keinänen, S. (2005). English: Guinness for strenght. Retrieved from https://commons.wikimedia.org/wiki/File:Guinness.jpg Kirton, L. (2007). English: Footpath through barley field. A well defined and well used footpath through the fields at Nuthall. Retrieved from https://commons.wikimedia.org/wiki/File:Footpath_through_barley_field_-_geograph.org.uk_-_451384.jpg pl.wikipedia, U. W. on. ([object HTMLTableCellElement]). English: William Sealy Gosset, known as “Student”, British statistician. Picture taken in 1908. Retrieved from https://commons.wikimedia.org/wiki/File:William_Sealy_Gosset.jpg The T-Test. (n.d.). Retrieved April 12, 2016, from http://www.socialresearchmethods.net/kb/stat_t.php
Views: 563047 Bozeman Science

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Views: 436240 ExcelIsFun

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Advice on gathering and analyzing data in organizations, tips on using Likert scales, and a case study on leveraging data to help the bottom line. &lt;br/ &gt;&lt;br/ &gt;Chris McMillan\'s Full Interview&lt;br/ &gt;Full Case Study by Sivar
Views: 18944 ASQ

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Tutorial for using SPSS 16 to run descriptive statistics for categorical and continuous variables, a 2-way contingency table for categorical variables, and chi-squared analysis, and a correlation analysis for 2 continuous variables. These videos are not intended to teach you how to calculate, comprehend, or interpret statistics. These videos are merely a tool to introduce you to some basic SPSS procedures. Download the sample data at the KSU Psych Lab web page: http://www.kennesaw.edu/psychology/videos/lab/sample_data.xlsx Subtitles available: click on the CC button toward the bottom right of the video. Menu available for jumping to chapters in the flash video posted on the KSU Psych Lab website: http://psychology.hss.kennesaw.edu/resources/psychlab/
Views: 151898 Terry Jorgensen

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Views: 784074 Kent Löfgren

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Views: 218303 Simplilearn

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Views: 80598 David Russell

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Use simple data analysis techniques in SPSS to analyze survey questions.
Views: 868414 Claus Ebster

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This video describes the procedure of tabulating and analyzing the likert scale survey data using Microsoft Excel. This video also explains how to prepare graph from the tabulated data. Photo courtesy: http://littlevisuals.co/
Views: 135839 Edifo

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Use Student's t-test to compare the means of two samples. However, the formulas that you use depends on whether the samples are paired or unpaired. If unpaired you also have to check for equality of variance. This video maps out a path to each of the three possible t-test formulas.
Views: 35006 Eugene O'Loughlin

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Views: 659423 Quantitative Specialists

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How to define variables and enter data into SPSS (v20) ASK SPSS Tutorial Series

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Every day plenty of data is generated worldwide and stored by public administration and private companies, around 2.5 trillion bytes globally to be precise. Besides, cities are full of sensors collecting all kinds of feed regarding weather, telephony, traffic. Big Data Analytics is a concept that clusters all those technologies and mathematical developments dedicated to store, analyze and cross-reference all that information to try and find behavioural patterns. Let´s dive into Big Data Analytics and this year´s trends. More information: https://www.imnovation-hub.com/digital-transformation/big-data-analytics-the-datafication-of-society/ Subscribe: https://www.youtube.com/user/interacciona1?sub_confirmation=1 Website: http://www.acciona.com/ --Social Media-- Twitter: https://twitter.com/acciona_en Facebook: https://www.facebook.com/ACCIONA.English/ LinkedIn: http://acciona.sa/YsSOM Google +: http://acciona.sa/YsT6T Instagram: https://www.instagram.com/acciona/
Views: 78994 ACCIONA

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This statistical analysis overview explains descriptive and inferential statistics. Watch more at http://www.lynda.com/Excel-2007-tutorials/business-statistics/71213-2.html?utm_medium=viral&utm_source=youtube&utm_campaign=videoupload-71213-0101 This specific tutorial is just a single movie from chapter one of the Excel 2007: Business Statistics course presented by lynda.com author Curt Frye. The complete Excel 2007: Business Statistics course has a total duration of 4 hours and 19 minutes and covers formulas and functions for calculating averages and standard deviations, charts and graphs for summarizing data, and the Analysis ToolPak add-in for even greater insights into data Excel 2007: Business Statistics table of contents: Introduction 1. Introducing Statistics 2. Learning Useful Excel Techniques 3. Summarizing Data Using Tables and Graphics 4. Describing Data Using Numerical Methods 5. Using Probability Distributions 6. Sampling Values from a Population 7. Testing Hypotheses 8. Using Linear and Multiple Regression Conclusion

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Get the full course at: http://www.MathTutorDVD.com The student will learn the big picture of what a hypothesis test is in statistics. We will discuss terms such as the null hypothesis, the alternate hypothesis, statistical significance of a hypothesis test, and more. In this step-by-step statistics tutorial, the student will learn how to perform hypothesis testing in statistics by working examples and solved problems.
Views: 1515790 mathtutordvd

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Views: 174298 Joma Tech

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Views: 10201 Ranywayz Random

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Views: 194786 Quantitative Specialists

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Likert Scale: http://en.wikipedia.org/wiki/Likert_scale R: http://www.r-project.org/
Views: 224847 Alan Cann

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Views: 343157 NurseKillam

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It’s easy to get lost in a lot of text-based data. NVivo is qualitative data analysis software that provides structure to text, helping you quickly unlock insights and make something beautiful to share. http://www.qsrinternational.com
Views: 152430 NVivo by QSR

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This video explains the purpose of t-tests, how they work, and how to interpret the results. For a simple explanation of Chi-Squares, visit: https://www.youtube.com/watch?v=ZjdBM7NO7bY
Views: 736828 StatsCast

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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: 251763 Jason Delaney

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How to run a correlation analysis using Excel and write up the findings for a report
Views: 317031 Chris Olson

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Updated video 2018: SPSS for Beginners - Introduction https://youtu.be/_zFBUfZEBWQ This video provides an introduction to SPSS/PASW. It shows how to navigate between Data View and Variable View, and shows how to modify properties of variables.
Views: 1598916 Research By Design