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Search results “Small sample data analysis”

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Keep watching chanakya group of economics.

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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

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*Α brief overview of hypothesis tests for 2 sample means. *Equal variances t-test example.
Views: 27641 Joshua Emmanuel

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Views: 106985 CrashCourse

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Paper: Statistical Inference II Module: Small sample properties of U statistic Content Writer: Mr Taranga Mukherjee
Views: 264 Vidya-mitra

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Views: 667530 Kent Löfgren

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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

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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

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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

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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

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Topics: Small sampling fraction, finite population correction, sampling with/without replacement
Views: 1449 Dana R Thomson

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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

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Views: 52 Beskid Cabrera

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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 :

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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

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Views: 85900 NurseKillam

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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

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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

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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

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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

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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

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Views: 19 Jacquelyn Gorobets

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How to run a chi-square test and interpret the output in SPSS (v20). ASK SPSS Tutorial Series

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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-

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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

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Statistics_stat-11-12-large-samp26c.mp4
Views: 129 Sabaq. Pk

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a short tutorial to introduce students to some terms involved in experimental uncertainty
Views: 258 Nicole Carro

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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

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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

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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

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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

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Views: 416387 Kent Löfgren

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Subject :Economics Course :Undergraduate Keyword : SWAYAMPRABHA

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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

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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

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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

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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

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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

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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

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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

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I demonstrate how to perform and interpret a Pearson correlation in SPSS.
Views: 622765 how2stats

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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.