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Small sample hypothesis test | Inferential statistics | Probability and Statistics | Khan Academy
 
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Small Sample Hypothesis Test Watch the next lesson: https://www.khanacademy.org/math/probability/statistics-inferential/hypothesis-testing/v/t-statistic-confidence-interval?utm_source=YT&utm_medium=Desc&utm_campaign=ProbabilityandStatistics Missed the previous lesson? https://www.khanacademy.org/math/probability/statistics-inferential/hypothesis-testing/v/z-statistics-vs-t-statistics?utm_source=YT&utm_medium=Desc&utm_campaign=ProbabilityandStatistics 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! About Khan Academy: Khan Academy offers practice exercises, instructional videos, and a personalized learning dashboard that empower learners to study at their own pace in and outside of the classroom. We tackle math, science, computer programming, history, art history, economics, and more. Our math missions guide learners from kindergarten to calculus using state-of-the-art, adaptive technology that identifies strengths and learning gaps. We've also partnered with institutions like NASA, The Museum of Modern Art, The California Academy of Sciences, and MIT to offer specialized content. For free. For everyone. Forever. #YouCanLearnAnything Subscribe to KhanAcademy’s Probability and Statistics channel: https://www.youtube.com/channel/UCRXuOXLW3LcQLWvxbZiIZ0w?sub_confirmation=1 Subscribe to KhanAcademy: https://www.youtube.com/subscription_center?add_user=khanacademy
Views: 426969 Khan Academy
Small sample size confidence intervals | Probability and Statistics | Khan Academy
 
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Constructing small sample size confidence intervals using t-distributions Watch the next lesson: https://www.khanacademy.org/math/probability/statistics-inferential/margin-of-error/v/mean-and-variance-of-bernoulli-distribution-example?utm_source=YT&utm_medium=Desc&utm_campaign=ProbabilityandStatistics Missed the previous lesson? https://www.khanacademy.org/math/probability/statistics-inferential/confidence-intervals/v/confidence-interval-example?utm_source=YT&utm_medium=Desc&utm_campaign=ProbabilityandStatistics 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! About Khan Academy: Khan Academy offers practice exercises, instructional videos, and a personalized learning dashboard that empower learners to study at their own pace in and outside of the classroom. We tackle math, science, computer programming, history, art history, economics, and more. Our math missions guide learners from kindergarten to calculus using state-of-the-art, adaptive technology that identifies strengths and learning gaps. We've also partnered with institutions like NASA, The Museum of Modern Art, The California Academy of Sciences, and MIT to offer specialized content. For free. For everyone. Forever. #YouCanLearnAnything Subscribe to KhanAcademy’s Probability and Statistics channel: https://www.youtube.com/channel/UCRXuOXLW3LcQLWvxbZiIZ0w?sub_confirmation=1 Subscribe to KhanAcademy: https://www.youtube.com/subscription_center?add_user=khanacademy
Views: 303237 Khan Academy
Some statistics tests, t-test, z-test, f-test and chi square test
 
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Keep watching chanakya group of economics.
Choosing which statistical test to use - statistics help.
 
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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: 680050 Dr Nic's Maths and Stats
Hypothesis test for 2 Population Means using Excel’s Data Analysis
 
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*Α brief overview of hypothesis tests for 2 sample means. *Equal variances t-test example.
Views: 27641 Joshua Emmanuel
The Shape of Data: Distributions: Crash Course Statistics #7
 
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When collecting data to make observations about the world it usually just isn't possible to collect ALL THE DATA. So instead of asking every single person about student loan debt for instance we take a sample of the population, and then use the shape of our samples to make inferences about the true underlying distribution our data. It turns out we can learn a lot about how something occurs, even if we don't know the underlying process that causes it. Today, we’ll also introduce the normal (or bell) curve and talk about how we can learn some really useful things from a sample's shape - like if an exam was particularly difficult, how often old faithful erupts, or if there are two types of runners that participate in marathons! Crash Course is on Patreon! You can support us directly by signing up at http://www.patreon.com/crashcourse Thanks to the following Patrons for their generous monthly contributions that help keep Crash Course free for everyone forever: Mark Brouwer, Justin Zingsheim, Nickie Miskell Jr., Jessica Wode, Eric Prestemon, Kathrin Benoit, Tom Trval, Jason Saslow, Nathan Taylor, Divonne Holmes à Court, Brian Thomas Gossett, Khaled El Shalakany, Indika Siriwardena, Robert Kunz, SR Foxley, Sam Ferguson, Yasenia Cruz, Daniel Baulig, Eric Koslow, Caleb Weeks, Tim Curwick, Evren Türkmenoğlu, Alexander Tamas, D.A. Noe, Shawn Arnold, mark austin, Ruth Perez, Malcolm Callis, Ken Penttinen, Advait Shinde, Cody Carpenter, Annamaria Herrera, William McGraw, Bader AlGhamdi, Vaso, Melissa Briski, Joey Quek, Andrei Krishkevich, Rachel Bright, Alex S, Mayumi Maeda, Kathy & Tim Philip, Montather, Jirat, Eric Kitchen, Moritz Schmidt, Ian Dundore, Chris Peters,, Sandra Aft, Steve Marshall -- Want to find Crash Course elsewhere on the internet? Facebook - http://www.facebook.com/YouTubeCrashCourse Twitter - http://www.twitter.com/TheCrashCourse Tumblr - http://thecrashcourse.tumblr.com Support Crash Course on Patreon: http://patreon.com/crashcourse CC Kids: http://www.youtube.com/crashcoursekids
Views: 106985 CrashCourse
Small sample properties of U statistic
 
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Paper: Statistical Inference II Module: Small sample properties of U statistic Content Writer: Mr Taranga Mukherjee
Views: 264 Vidya-mitra
Qualitative analysis of interview data: A step-by-step guide
 
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The content applies to qualitative data analysis in general. Do not forget to share this Youtube link with your friends. The steps are also described in writing below (Click Show more): STEP 1, reading the transcripts 1.1. Browse through all transcripts, as a whole. 1.2. Make notes about your impressions. 1.3. Read the transcripts again, one by one. 1.4. Read very carefully, line by line. STEP 2, labeling relevant pieces 2.1. Label relevant words, phrases, sentences, or sections. 2.2. Labels can be about actions, activities, concepts, differences, opinions, processes, or whatever you think is relevant. 2.3. You might decide that something is relevant to code because: *it is repeated in several places; *it surprises you; *the interviewee explicitly states that it is important; *you have read about something similar in reports, e.g. scientific articles; *it reminds you of a theory or a concept; *or for some other reason that you think is relevant. You can use preconceived theories and concepts, be open-minded, aim for a description of things that are superficial, or aim for a conceptualization of underlying patterns. It is all up to you. It is your study and your choice of methodology. You are the interpreter and these phenomena are highlighted because you consider them important. Just make sure that you tell your reader about your methodology, under the heading Method. Be unbiased, stay close to the data, i.e. the transcripts, and do not hesitate to code plenty of phenomena. You can have lots of codes, even hundreds. STEP 3, decide which codes are the most important, and create categories by bringing several codes together 3.1. Go through all the codes created in the previous step. Read them, with a pen in your hand. 3.2. You can create new codes by combining two or more codes. 3.3. You do not have to use all the codes that you created in the previous step. 3.4. In fact, many of these initial codes can now be dropped. 3.5. Keep the codes that you think are important and group them together in the way you want. 3.6. Create categories. (You can call them themes if you want.) 3.7. The categories do not have to be of the same type. They can be about objects, processes, differences, or whatever. 3.8. Be unbiased, creative and open-minded. 3.9. Your work now, compared to the previous steps, is on a more general, abstract level. 3.10. You are conceptualizing your data. STEP 4, label categories and decide which are the most relevant and how they are connected to each other 4.1. Label the categories. Here are some examples: Adaptation (Category) Updating rulebook (sub-category) Changing schedule (sub-category) New routines (sub-category) Seeking information (Category) Talking to colleagues (sub-category) Reading journals (sub-category) Attending meetings (sub-category) Problem solving (Category) Locate and fix problems fast (sub-category) Quick alarm systems (sub-category) 4.2. Describe the connections between them. 4.3. The categories and the connections are the main result of your study. It is new knowledge about the world, from the perspective of the participants in your study. STEP 5, some options 5.1. Decide if there is a hierarchy among the categories. 5.2. Decide if one category is more important than the other. 5.3. Draw a figure to summarize your results. STEP 6, write up your results 6.1. Under the heading Results, describe the categories and how they are connected. Use a neutral voice, and do not interpret your results. 6.2. Under the heading Discussion, write out your interpretations and discuss your results. Interpret the results in light of, for example: *results from similar, previous studies published in relevant scientific journals; *theories or concepts from your field; *other relevant aspects. STEP 7 Ending remark This tutorial showed how to focus on segments in the transcripts and how to put codes together and create categories. However, it is important to remember that it is also OK not to divide the data into segments. Narrative analysis of interview transcripts, for example, does not rely on the fragmentation of the interview data. (Narrative analysis is not discussed in this tutorial.) Further, I have assumed that your task is to make sense of a lot of unstructured data, i.e. that you have qualitative data in the form of interview transcripts. However, remember that most of the things I have said in this tutorial are basic, and also apply to qualitative analysis in general. You can use the steps described in this tutorial to analyze: *notes from participatory observations; *documents; *web pages; *or other types of qualitative data. STEP 8 Suggested reading Alan Bryman's book: 'Social Research Methods' published by Oxford University Press. Steinar Kvale's and Svend Brinkmann's book 'InterViews: Learning the Craft of Qualitative Research Interviewing' published by SAGE. Good luck with your study. Text and video (including audio) © Kent Löfgren, Sweden
Views: 667530 Kent Löfgren
How to calculate Sample Size
 
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A video on how to calculate the sample size. Includes discussion on how the standard deviation impacts sample size too. Like us on: http://www.facebook.com/PartyMoreStudyLess Related Video How to calculate Samples Size Proportions http://youtu.be/LGFqxJdk20o
Views: 246937 statisticsfun
Webinar 2: Small Sample Size Clinical Trials
 
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Chris Coffey presents small sample size clinical trial designs as part of the NINDS clinical trials methodology course.
Views: 247 NINDS-Vail2012
Dealing with Unequal Variances and Sample Sizes - SPSS (part 1)
 
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SPSS provides a correction to the t-test in cases where there are unequal variances. However, when one has unequal variances and unequal sample sizes, this correction is no longer accurate. In this video, I demonstrate a simple approach to dealing with the problem of unequal variances and sample sizes.
Views: 46671 how2stats
Beginner - Data Science Project
 
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The best way to learn data science and showcase your skills is by doing some actual projects – we learn best by doing. So, how do we choose a project to work on? Where do we start? One way to approach it is to first look at some career websites and find a few jobs in data science that you aspire to have in the future. Write down the skills, qualifications, day-to-day expectations, and overall job description from the jobs that interest you. This will give you the project “requirements” that you can work with to formulate a project. http://storybydata.com/data-science-learn-by-doing-global-super-store-project/
Views: 23955 Story by Data
Methods: Survey Analysis in Small Populations (Low)
 
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Topics: Small sampling fraction, finite population correction, sampling with/without replacement
Views: 1449 Dana R Thomson
Hypothesis t-test for One Sample Mean using Excel’s Data Analysis
 
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This video shows how to conduct a one-sample hypothesis t-test for the mean in Microsoft Excel using the built-in Data Analysis (from raw data). How to load Data Analysis in Excel: https://youtu.be/SqpSwxJ9t2k
Views: 75164 Joshua Emmanuel
small samples HP 1
 
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Views: 52 Beskid Cabrera
3. Choosing Between Parametric & Non-Parametric Tests
 
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Basic Statistical Tests Training session with Dr Helen Brown, Senior Statistician, at The Roslin Institute, December 2015. ************************************************ These training sessions were given to staff and research students at the Roslin Institute. The material is also used for the Animal Biosciences MSc course taught at the Institute. ************************************************ *Recommended YouTube playback settings for the best viewing experience: 1080p HD ************************************************ Content: ‘Continuous’ data -Measurements recorded on a scale -Eg : --White blood cell count --Blood pressure --Temperature -2 types of test :- --‘Parametric’ tests: suitable for normally distributed data --‘Non-parametric’ tests: suitable for any continuous data, based on ranks of the data values Analyses for data assumed to have a normal distribution Checking normality -Simple check can be based on histogram -Satisfactory if roughly symmetrical -Ideally data should be normal within each group tested --In practice satisfactory if histogram for full data is symmetrical --If full histogram has several modes, consider histograms for groups separately Checking normality – smaller samples -Histogram may not be smooth even if data are normal -Difficult to determine whether normal -Information on same measurements from previous larger studies may be helpful -Sometimes still clear that data are non-normal, even in small sample Data not normal? -First consider a transformation of the data, particularly if the plots reveal a pattern -If non-normality is due to outliers, can their deletion be justified? -If normality in doubt :
Excel 2013 Statistical Analysis #52: Determine Sample Size for Sample Mean & Sample Proportion
 
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Download files: http://people.highline.edu/mgirvin/excelisfun.htm Topics in this video: 1. (00:12) Overview of formulas for estimating Sample Size. 2. (03:00) Example to estimate sample size for Sample Mean, Xbar. 3. (04:48) Example to estimate sample size for Sample Proportion, Pbar. 4.
Views: 4727 ExcelIsFun
Type I and II Errors, Power, Effect Size, Significance and Power Analysis in Quantitative Research
 
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There is a mistake at 9.22. Alpha is normally set to 0.05 NOT 0.5. Thank you Victoria for bringing this to my attention. This video reviews key terminology relating to type I and II errors along with examples. Then considerations of Power, Effect Size, Significance and Power Analysis in Quantitative Research are briefly reviewed. http://youstudynursing.com/ Research eBook on Amazon: http://amzn.to/1hB2eBd Check out the links below and SUBSCRIBE for more youtube.com/user/NurseKillam Quantitative research is driven by research questions and hypotheses. For every hypothesis there is an unstated null hypothesis. The null hypothesis does not need to be explicitly stated because it is always the opposite of the hypothesis. In order to demonstrate that a hypothesis is likely true researchers need to compare it to the opposite situation. The research hypothesis will be about some kind of relationship between variables. The null hypothesis is the assertion that the variables being tested are not related and the results are the product of random chance events. Remember that null is kind of like no so a null hypothesis means there is no relationship. For example, if a researcher asks the question "Does having class for 12 hours in one day lead to nursing student burnout?" The hypothesis would indicate the researcher's best guess of the results: "A 12 hour day of classes causes nursing students to burn out." Therefore the null hypothesis would be that "12 hours of class in one day has nothing to do with student burnout." The only way of backing up a hypothesis is to refute the null hypothesis. Instead of trying to prove the hypothesis that 12 hours of class causes burnout the researcher must show that the null hypothesis is likely to be wrong. This rule means assuming that there is not relationship until there is evidence to the contrary. In every study there is a chance for error. There are two major types of error in quantitative research -- type 1 and 2. Logically, since they are defined as errors, both types of error focus on mistakes the researcher may make. Sometimes talking about type 1 and type 2 errors can be mentally tricky because it seems like you are talking in double and even triple negatives. It is because both type 1 and 2 errors are defined according to the researcher's decision regarding the null hypothesis, which assumes no relationship among variables. Instead of remembering the entire definition of each type of error just remember which type has to do with rejecting and which one is about accepting the null hypothesis. A type I error occurs when the researcher mistakenly rejects the null hypothesis. If the null hypothesis is rejected it means that the researcher has found a relationship among variables. So a type I error happens when there is no relationship but the researcher finds one. A type II error is the opposite. A type II error occurs when the researcher mistakenly accepts the null hypothesis. If the null hypothesis is accepted it means that the researcher has not found a relationship among variables. So a type II error happens when there is a relationship but the researcher does not find it. To remember the difference between these errors think about a stubborn person. Remember that your first instinct as a researcher may be to reject the null hypothesis because you want your prediction of an existing relationship to be correct. If you decide that your hypothesis is right when you are actually wrong a type I error has occurred. A type II error happens when you decide your prediction is wrong when you are actually right. One way to help you remember the meaning of type 1 and 2 error is to find an example or analogy that helps you remember. As a nurse you may identify most with the idea of thinking about medical tests. A lot of teachers use the analogy of a court room when explaining type 1 and 2 errors. I thought students may appreciate our example study analogy regarding class schedules. It is impossible to know for sure when an error occurs, but researchers can control the likelihood of making an error in statistical decision making. The likelihood of making an error is related to statistical considerations that are used to determine the needed sample size for a study. When determining a sample size researchers need to consider the desired Power, expected Effect Size and the acceptable Significance level. Power is the probability that the researcher will make a correct decision to reject the null hypothesis when it is in reality false, therefore, avoiding a type II error. It refers to the probability that your test will find a statistically significant difference when such a difference actually exists. Another way to think about it is the ability of a test to detect an effect if the effect really exists. The more power a study has the lower the risk of a type II error is. If power is low the risk of a type II error is high. ...
Views: 85900 NurseKillam
Cucudata SPSS Tutorial Lecture 10 Normality Test of Small Sample Size—Shapiro-Wilk Test(1)
 
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Cucudata Short Videos: SPSS Data Analysis Tutorial Please Visit:www.cucudata.com For More Videos and Files Download More Data and Information Ask Experts & Get Answers Our Professional Team Provides a Comprehensive Data Analysis & Data Mining Services
Views: 39 spss
Excel 2013 Statistical Analysis #50: t Distribution Confidence Intervals Sigma NOT Known 3 Examples
 
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Download files: http://people.highline.edu/mgirvin/excelisfun.htm Topics in this video: 1. (00:12) What is the t Distribution and when do we have to use it? Create Confidence Intervals when Population Standard Deviation is not known 2. (04:13) Formulas for t distribution to build Confidence Intervals when Population Standard Deviation is not known. Math formulas and Excel functions 3. (05:21) Example 1: Printer Manufacturer Example to create Confidence Interval 4. (06:41) Printer Manufacturer Example: Calculate Sample Mean, Sample Standard Deviation, Sample Size, Degrees of Freedom, Standard Error, Alpha and Alpha/2 5. (08:16) Printer Manufacturer Example: T.INV function to calculate t 6. (09:24)Printer Manufacturer Example: CONFIDENCE.T function to calculate Margin of Error 7. (11:38)Printer Manufacturer Example: Data Analysis Descriptive Statistics, Summary Statistics, Confidence Interval for Mean 8. (14:57) Restaurant Rating Example: All 3 Methods: 1) T.INV function, 2) CONFIDENCE.T function, 3) Data Analysis Descriptive Statistics
Views: 6137 ExcelIsFun
One Sample t-Test
 
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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: 312852 statslectures
HT for small sample sizes t Score
 
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Course: Data Analysis for Astronomy and Physics Summer term 2017 - Universität zu Köln Lecture 6 - Hypothesis Tests for numerical variables How to perform an HT if the sample size is small.
Views: 8 roellig01
Hypothesis testing and p-values | Inferential statistics | Probability and Statistics | Khan Academy
 
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Hypothesis Testing and P-values Practice this yourself on Khan Academy right now: https://www.khanacademy.org/e/hypothesis-testing-with-simulations?utm_source=YTdescription&utm_medium=YTdescription&utm_campaign=YTdescription Watch the next lesson: https://www.khanacademy.org/math/probability/statistics-inferential/hypothesis-testing/v/one-tailed-and-two-tailed-tests?utm_source=YT&utm_medium=Desc&utm_campaign=ProbabilityandStatistics Missed the previous lesson? https://www.khanacademy.org/math/probability/statistics-inferential/margin-of-error/v/margin-of-error-2?utm_source=YT&utm_medium=Desc&utm_campaign=ProbabilityandStatistics 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! About Khan Academy: Khan Academy is a nonprofit with a mission to provide a free, world-class education for anyone, anywhere. We believe learners of all ages should have unlimited access to free educational content they can master at their own pace. We use intelligent software, deep data analytics and intuitive user interfaces to help students and teachers around the world. Our resources cover preschool through early college education, including math, biology, chemistry, physics, economics, finance, history, grammar and more. We offer free personalized SAT test prep in partnership with the test developer, the College Board. Khan Academy has been translated into dozens of languages, and 100 million people use our platform worldwide every year. For more information, visit www.khanacademy.org, join us on Facebook or follow us on Twitter at @khanacademy. And remember, you can learn anything. For free. For everyone. Forever. #YouCanLearnAnything Subscribe to KhanAcademy’s Probability and Statistics channel: https://www.youtube.com/channel/UCRXuOXLW3LcQLWvxbZiIZ0w?sub_confirmation=1 Subscribe to KhanAcademy: https://www.youtube.com/subscription_center?add_user=khanacademy
Views: 2035447 Khan Academy
Small-Sample-Size Statistics in Agriculture: 6) Conrady
 
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“From Statistical Inference to Probabilistic Reasoning; Using Bayesian Networks for Reasoning with Small Samples, Missing Values, and No Data.” Stefan Conrady, Managing Partner, Bayesia USA. PSC Hot Topics 2016 Workshop: Small-Sample-Size Statistics in Agriculture; How to Maximize Business Value, November 3, 2016 Knoxville, TN
Chi-square test in SPSS + interpretation
 
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How to run a chi-square test and interpret the output in SPSS (v20). ASK SPSS Tutorial Series
Views: 815239 BrunelASK
How to Use SPSS: Estimating Appropriate Sample Size
 
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Using SPSS Sample Power 3, G*Power and web-based calculators to estimate appropriate sample size. G*Power Download site: http:--www.psycho.uni-duesseldorf.de-abteilungen-aap-gpower3-download-and-register Web-Based Calculators: http:--danielsoper.com-statcalc3-default.aspx (scroll down to menu labelled -Sample Size-
Small sample Opposition Scouting Analysis Football Performance Analysis with @ER1Csports
 
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Very small sample of an Opposition Analysis scouting report from match DVD. Maidstone United leaders of the Ryman Premier against Vanarama Conference Welling United. This sample looks at left back Tom Mills Reports cover all areas of attack and defence from shots, chances, crosses, set pieces, shape etc, transitions and build up with strength and weaknesses. Contact us on twitter http://www.twitter.com/SportAnalysisUK for Filming, Scouting, Analysis or to find more or get a trial of ER1Csports as used by Barcelona and Zaragoza for analysis.
Views: 356 MrVideoAnalysis
Confidence Interval Estimates of Mean From Small Samples
 
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Statistics_stat-11-12-large-samp26c.mp4
Views: 129 Sabaq. Pk
AP Physics 1: Introduction 13: Statistical Analysis of Small Data Sets
 
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a short tutorial to introduce students to some terms involved in experimental uncertainty
Views: 258 Nicole Carro
An Example of a Paired-Difference t Test and Confidence Interval
 
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An example of a paired-difference t test and confidence interval. The data in this video is from: Penetar et al. (2012). The isoflavone puerarin reduces alcohol intake in heavy drinkers: A pilot study. Drug and Alcohol Dependence, 126:256-261. Values used in this video are simulated values based on the summary statistics found in the paper. (The summary statistics, test statistic, p-value, and overall conclusions are the same.)
Views: 55304 jbstatistics
Cucudata SPSS Tutorial Lecture 11 Normality Test of Small Sample Size—Shapiro-Wilk Test(2)
 
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Cucudata Short Videos: SPSS Data Analysis Tutorial Please Visit:www.cucudata.com For More Videos and Files Download More Data and Information Ask Experts & Get Answers Our Professional Team Provides a Comprehensive Data Analysis & Data Mining Services
Views: 9 spss
10-7 Cohen’s d Effect Size for t Tests
 
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An effect size is a standardized measure of the size of an effect that allows for objective evaluation the size of the effect to determine whether a treatment had any practical usefulness. Cohen’s d is the most commonly used measure of effect size for t tests. Using an example from Rosnow & Rosenthal, we learn how very different p values can result from exactly the same effect size. We lean about Jacob Cohen’s conventions for interpreting d, including practical examples and the overlap of the distributions. This gives us the basis for conducting a power analysis before beginning data collection. I give you four reasons why we should report the effect size of a study (Neill, 2008), because of the APA, when generalization is not important, when sample size is small, and when sample size is large. In short, there is no reason why you should fail to report effect size. RStats Effect Size Calculator for t Tests available at: http://www.MissouriState.edu/RStats/Tables-and-Calculators.htm
Views: 5358 Research By Design
Bootstrap Sample Technique in R software
 
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Bootstrap is very simple technique used for small samples. Major takeaways from video are: What?: Resampling with replacement from sample data. Why?: To find std errors without invoking CLT. How?: K times repetitive sampling of size n (observation). When to use?: Most suitable for small sample sizes. Major Advantage: No need to invoke CLT or normality assumption
Views: 15559 Sarveshwar Inani
Normality test using SPSS: How to check whether data are normally distributed
 
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If data need to be approximately normally distributed, this tutorial shows how to use SPSS to verify this. On a side note: my new project: http://howtowritecitations.com. Statistical analyses often have dependent variables and independent variables and many parametric statistical methods require that the dependent variable is approximately normally distributed for each category of the independent variable. Let us assume that we have a dependent variable, exam scores, and an independent variable, gender. In short, we must investigate the following numerical and visual outputs (and the tutorial shows how to do just that): -The Skewness & kurtosis z-values, which should be somewhere in the span -1.96 to +1.96; -The Shapiro-Wilk p-value, which should be above 0.05; -The Histograms, Normal Q-Q plots and Box plots, which should visually indicate that our data are approximately normally distributed. Remember that your data do not have to be perfectly normally distributed. The main thing is that they are approximately normally distributed, and that you check each category of the independent variable. (In our example, both male and female data.) Step 1. In the menu of SPSS, click on Analyze, select Descriptive Statistics and Explore. Step 2. Set exam scores as the dependent variable, and gender as the independent variable. Step 3. Click on Plots, select "Histogram" (you do not need "Stem-and-leaf") and select "Normality plots with tests" and click on Continue, then OK. Step 4. Start with skewness and kurtosis. The skewness and kurtosis measures should be as close to zero as possible, in SPSS. In reality, however, data are often skewed and kurtotic. A small departure from zero is therefore no problem, as long as the measures are not too large compare to their standard errors. As a consequence, you must divide the measure by its standard error, and you need to do this by hand, using a calculator. This will give you the z-value, which, as I said, should be somewhere within -1.96 to +1.96. Let us start with the males in our example. To calculate the skewness z-value, divide the skewness measure by its standard error. All z-values in the tutorial video are within ±1.96. We can conclude that the exam score data are a little skewed and kurtotic, for both males and females, but they do not differ significantly from normality. Step 5. Check the Shapiro-Wilk test statistic. The null hypothesis for this test of normality is that the data are normally distributed. The null hypothesis is rejected if the p-value is below 0.05. In SPSS output, the p-value is labeled "Sig". In our example, the p-values for males and females are above 0.05, so we keep the null hypothesis. The Shapiro-Wilk test thus indicates that our example data are approximately normally distributed. Step 6. Next, let us look at the graphical figures, for both male and female data. Inspect the histograms visually. They should have the approximate shape of a normal curve. Then, look at the normal Q-Q plot. The dots should be approximately distributed along the line. This indicates that the data are approximately normally distributed. Skip the Detrended Q-Q plots. You do not need them. Finally, look at the box plots. They should be approximately symmetrical. The video contains references to books and articles. About writing out the results: I would put it under the sub-heading "Sample characteristics", and the video contains examples of how I would write. In this tutorial, I show you how to check if a dependent variable is approximately normally distributed for each category of an independent variable. I am assuming that you, eventually, want to use a certain parametric statistical methods to explore and investigate your data. If it turns out that your dependent variable is not approximately normally distributed for each category of the independent variable, it is no problem. In such case, you will have to use non-parametric methods, because they make no assumptions about the distributions. Good luck with your research. Text and video (including audio) © Kent Löfgren, Sweden Here are the references that I discuss in the video (thanks Abdul Syafiq Bahrin for typing them our for me): Cramer, D. (1998). Fundamental statistics for social research. London: Routledge. Cramer, D., & Howitt, D. (2004). The SAGE dictionary of statistics. London: SAGE. Doane, D. P., & Seward, L.E. (2011). Measuring Skewness. Journal of Statistics Education, 19(2), 1-18. Razali, N. M., & Wah, Y. B. (2011). Power comparisons of Shapiro-Wilk, Kolmogorov-Smirnov, Liliefors and Anderson-Darling test. Journal of Statistical Modeling and Analytics, 2(1), 21-33. Shapiro, S. S., & Wilk, M. B. (1965). An Analysis of Variance Test for Normality (Complete Samples). Biometrika, 52(3/4), 591-611.
Views: 416387 Kent Löfgren
Hypothesis Small Sample Test t Tests & F Tests
 
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Subject :Economics Course :Undergraduate Keyword : SWAYAMPRABHA
Standard error of the mean | Inferential statistics | Probability and Statistics | Khan Academy
 
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Standard Error of the Mean (a.k.a. the standard deviation of the sampling distribution of the sample mean!) Watch the next lesson: https://www.khanacademy.org/math/probability/statistics-inferential/sampling_distribution/v/sampling-distribution-example-problem?utm_source=YT&utm_medium=Desc&utm_campaign=ProbabilityandStatistics Missed the previous lesson? https://www.khanacademy.org/math/probability/statistics-inferential/sampling_distribution/v/sampling-distribution-of-the-sample-mean-2?utm_source=YT&utm_medium=Desc&utm_campaign=ProbabilityandStatistics 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! About Khan Academy: Khan Academy offers practice exercises, instructional videos, and a personalized learning dashboard that empower learners to study at their own pace in and outside of the classroom. We tackle math, science, computer programming, history, art history, economics, and more. Our math missions guide learners from kindergarten to calculus using state-of-the-art, adaptive technology that identifies strengths and learning gaps. We've also partnered with institutions like NASA, The Museum of Modern Art, The California Academy of Sciences, and MIT to offer specialized content. For free. For everyone. Forever. #YouCanLearnAnything Subscribe to KhanAcademy’s Probability and Statistics channel: https://www.youtube.com/channel/UCRXuOXLW3LcQLWvxbZiIZ0w?sub_confirmation=1 Subscribe to KhanAcademy: https://www.youtube.com/subscription_center?add_user=khanacademy
Views: 813507 Khan Academy
Understanding the p-value - Statistics Help
 
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With Spanish subtitles. This video explains how to use the p-value to draw conclusions from statistical output. It includes the story of Helen, making sure that the choconutties she sells have sufficient peanuts. You might like to read my blog: http://learnandteachstatistics.wordpress.com
Views: 713649 Dr Nic's Maths and Stats
Small-Sample-Size Statistics in Agriculture: 7) van Santen
 
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“Common Mistakes In Small Sample Size Biological Experiments And How To Avoid Them” Professor Edzard van Santen, IFAS Statistical Consulting Unit & Agronomy Department, University of Florida. PSC Hot Topics 2016 Workshop: Small-Sample-Size Statistics in Agriculture; How to Maximize Business Value, November 3, 2016 Knoxville, TN
Cucudata SPSS Tutorial Lecture 12 Normality Test of Small Sample Size — Histogram
 
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Cucudata Short Videos: SPSS Data Analysis Tutorial Please Visit:www.cucudata.com For More Videos and Files Download More Data and Information Ask Experts & Get Answers Our Professional Team Provides a Comprehensive Data Analysis & Data Mining Services
Views: 29 spss
Z-statistics vs. T-statistics | Inferential statistics | Probability and Statistics | Khan Academy
 
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Z-statistics vs. T-statistics Watch the next lesson: https://www.khanacademy.org/math/probability/statistics-inferential/hypothesis-testing/v/small-sample-hypothesis-test?utm_source=YT&utm_medium=Desc&utm_campaign=ProbabilityandStatistics Missed the previous lesson? https://www.khanacademy.org/math/probability/statistics-inferential/hypothesis-testing/v/type-1-errors?utm_source=YT&utm_medium=Desc&utm_campaign=ProbabilityandStatistics 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! About Khan Academy: Khan Academy offers practice exercises, instructional videos, and a personalized learning dashboard that empower learners to study at their own pace in and outside of the classroom. We tackle math, science, computer programming, history, art history, economics, and more. Our math missions guide learners from kindergarten to calculus using state-of-the-art, adaptive technology that identifies strengths and learning gaps. We've also partnered with institutions like NASA, The Museum of Modern Art, The California Academy of Sciences, and MIT to offer specialized content. For free. For everyone. Forever. #YouCanLearnAnything Subscribe to KhanAcademy’s Probability and Statistics channel: https://www.youtube.com/channel/UCRXuOXLW3LcQLWvxbZiIZ0w?sub_confirmation=1 Subscribe to KhanAcademy: https://www.youtube.com/subscription_center?add_user=khanacademy
Views: 1188234 Khan Academy
Standard Deviation - Explained and Visualized
 
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Video transcript: "Have we discovered a new particle in physics? Is a manufacturing process out of control? What percentage of men are taller than Lebron James? How about taller than Yao Ming? All of these questions can be answered using the concept of standard deviation. For any set of data, the mean and standard deviation can be calculated. For example, five people may have the following amounts of money in their wallets: 21, 50, 62, 85, and 90. The mean is $61.60 and the standard deviation is $28.01. How much does the data vary from the average? Standard deviation is a measure of spread, that is, how spread out a set of data is. A low standard deviation tells us that the data is closely clustered around the mean (or average), while a high standard deviation indicates that the data is dispersed over a wider range of values. It is used when the distribution of data is approximately normal, resembling a bell curve. Standard deviation is commonly used to understand whether a specific data point is “standard” and expected or unusual and unexpected. Standard deviation is represented by the lowercase greek letter sigma. A data point’s distance from the mean can be measured by the number of standard deviations that it is above or below the mean. A data point that is beyond a certain number of standard deviations from the mean represents an outcome that is significantly above or below the average. This can be used to determine whether a result is statistically significant or part of expected variation, such as whether a bottle with an extra ounce of soda is to be expected or warrants further investigation into the production line. The 68-95-99.7 rule tells us that about 68% of the data fall within one standard deviation of the mean. About 95% of data fall within two standard deviations of the mean. And about 99.7% of data fall within 3 standard deviations of the mean. The average height of an American adult male is 5’10, with a standard deviation of 3 inches. Using the 68-95-99.7 rule, this means that 68% of American men are 5’10 plus or minus 3 inches, 95% of American men are 5’10 plus or minus 6 inches, and 99.7% of American men are 5’10 plus or minus 9 inches. So, this means only about .3% of American men deviate more than 9 inches from the average, with .15% taller than 6’7 and .15% shorter than 5’1. This reasoning suggests that Lebron James is 1 in 2500 and Yao Ming is 1 in 450 million. In particle physics, scientists have what are called 5-sigma results, results that are five standard deviations above or below the mean. A result that varies this much can signify a discovery as it has only a 1 in 3.5 million chance that it is due to random fluctuation. In summary, standard deviation is a measure of spread. Along with the mean, the standard deviation allows us to determine whether a value is statistically significant or part of expected variation."
Views: 772671 Jeremy Jones
MS Excel: Normal Distributions and Bell Curves
 
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Document Link: https://drive.google.com/open?id=0B_lD7FHorWGzYWpJSU1jTEZYZzg Main downloads: https://arcologydesigns.com/ under "Downloads" What are bin numbers: http://en.allexperts.com/q/Excel-1059/2009/7/bin-numbers.htm Check out the NEW WEBSITE: https://growyourcareer.com UPDATED BLOG: https://arcologydesigns.blogspot.com Bin Formulas: C2=$B$2-3*$B4 C3=C2+$B$4 Then click and drag on the fill handle of cell C3 and drag until you have 8 bin numbers. "Bin numbers represent the intervals that you want the Histogram tool to use for measuring the input data in data analysis. When you use the Histogram tool, Excel counts the number of data points in each data bin. A data point is included in a particular bin if the number is greater than the lowest bound and equal to or less than the greatest bound for the data bin. If you omit the bin range, Excel creates a set of evenly distributed bins between the minimum and maximum values of the input data." Other Formulas: =AVERAGE(A2:A19) =STDEV(A2:A19) This video provides a walk-through of how to create a bell curve in Microsoft Excel. It is meant for educational purposes only. Given a set of data, we normalize it by using Excel's Random Number Generator to create several thousand random values within the parameters of the original data set. A good sample consists of several hundred values - that is when the Law of Large Numbers takes effect. However, that's not always possible. Here, we have a small sample size and need see the bell curve of the data in order to perform statistical analysis for normally distributed data. The problem lies in the fact that the original data does not appear to be normally distributed. The small sample size misrepresents the true behavior of the population data, which should be normally distributed. To correct for this, we generate 2000 random values and then compare the histograms of both the original data and the randomly generated numbers within the parameters established by or original data set. We first calculate the average and the standard deviation of our data. Then, we go to File, Options, Add-Ins, and add the Analysis Toolkit, which we use for the random generator and our histograms. Next, we calculate the bin values: our upper and lower limits on our data that Excel will use in the histogram. After generating the random numbers and creating our histograms, we create our bell curves by inserting a scatter plot with smooth lines and compare our results between the original and random data sets. Send us a message if you have any questions, we're happy to help! On-line data analysis and research tools: http://youtu.be/j6K9rk_5A74 ________________________________________________________________________ ArcologyDesigns: http://www.arcologydesigns.com BCB Energy, LLC: http://www.bcb-energy.com For free IT sample files, go to: www.bcb-energy.com and click on "IT Training Initiative," then go to the "download sample files" page. ________________________________________________________________________ 100% ALL original content - photos, music, lyrics, art and more! BCB Energy, LLC and its subsidiary ArcologyDesigns are the sole creators and owners to all artwork, photographs, illustrations, graphics, logos, lyrics, texts, materials, sound recordings and musical compositions and all features of the content and materials. This includes but is not limited to the design, assortment, arrangement, atmosphere and presentation and any associated copyrights or trademarks of such content and materials.
Views: 156736 Grow Your Career
Small-sample inference for mixed-effects models in Stata
 
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Until now, Stata provided only large-sample inference based on normal and χ² distributions for linear mixed-effects models. In small samples, the sampling distributions of test statistics are known to be t and F in simple cases, and those distributions can be good approximations in other cases. Stata 14 provides five methods for small-sample inference, including Satterthwaite and Kenward-Roger. In this short video, we show you how to specify a small-sample adjustment method and new adjustments for postestimation hypothesis testing. For more information about Stata's new small-sample inference capabilities, you can also check out http://stata.com/stata14/small-sample-mixed-models/ Copyright 2011-2017 StataCorp LLC. All rights reserved.
Views: 2970 StataCorp LLC
Biomarker Discovery, Quantitation, and Analysis with Multiplex Immunoassays
 
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Scientists studying biomarkers associated with human diseases and drug responses commonly use targeted immunoassays for marker detection and quantitation. Challenges associated with conventional detection methods such as Western blotting and ELISAs include limitations imposed by small sample volume due to use of rodent models and the need for greater sensitivity than conventional test formats. The introduction of multiplex xMAP® technology (Luminex) that combines advanced fluidics, optics, and digital signal processing with microsphere technology has enabled multiplexed assay capabilities and enables acquisition of more data in less time than other bioassay formats, with comparable results to ELISA. Featuring a flexible, open-architecture design, xMAP can be configured to perform a wide variety of bioassays quickly, cost-effectively, and accurately. Recently, for example, scientists have applied xMAP technology to detection and expression of cytokine expression and secretion in lymphocyte samples from mouse and human sources, detecting over 20 different cytokines in samples smaller than 50 uL. This technology has also allowed assessment of cytokine expression in human diseases, before and after immunization and in the healthy general population, as well as the study of various mouse models of autoimmune diseases. This multiplexed cytokine assay allows generation of large amounts of data and therefore new analyses, which result in new insights into the roles of cytokines in health and disease. In this webinar, investigators will describe how Thermo Fisher Scientific’s Cytokine Human 30-Plex Panel for the Luminex platform, was used to demonstrate pharmacological inhibition of G protein-coupled estrogen receptor (GPER) as measured by a decrease in pro-inflammatory cytokines. Scientists will also describe how ongoing multiplex assay development for cancer-specific cytokine and autoantibody immunoassays may enable differentiation of lung cancer subtypes in patients.
Views: 3454 GENNews
Small-Sample-Size Statistics in Agriculture: 5) Wasserstein
 
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“Doctor, It Hurts When I p”, Ronald L. Wasserstein, Executive Director, American Statistical Association PSC Hot Topics 2016 Workshop: Small-Sample-Size Statistics in Agriculture; How to Maximize Business Value, November 3, 2016 Knoxville, TN
Pearson Correlation - SPSS
 
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I demonstrate how to perform and interpret a Pearson correlation in SPSS.
Views: 622765 how2stats
NIPS 2011 Domain Adaptation Workshop: Overfitting and Small Sample Statistics
 
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Domain Adaptation Workshop: Theory and Application at NIPS 2011 Invited Talk: Overfitting and Small Sample Statistics by Ruslan Salakhutdinov Abstract: We study the prevalent problem when a test distribution differs from the training distribution. We consider a setting where our training set consists of a small number of sample domains, but where we have many samples in each domain. Our goal is to generalize to a new domain. For example, we may want to learn a similarity function using only certain classes of objects, but we desire that this similarity function be applicable to object classes not present in our training sample (e.g. we might seek to learn that "dogs are similar to dogs" even though images of dogs were absent from our training set). Our theoretical analysis shows that we can select many more features than domains while avoiding overfitting by utilizing data-dependent variance properties. We present a greedy feature selection algorithm based on using T-statistics. Our experiments validate this theory showing that our T-statistic based greedy feature selection is more robust at avoiding overfitting than the classical greedy procedure.
Views: 409 GoogleTechTalks

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