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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: 177749 CrashCourse
Describing Distributions in Statistics
 
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The four key points are discussed when describing distributions in statistics...Shape, Center, Spread, and Outliers. Please forgive the misspelling of DESCRIBED in the video. TIP to identify Left & Right Skewness: (Thanks LeBadman:) Left: Mean is less than Median is less than Mode Symmetrical: Mean, Median and Mode are approximately equal Right: Mean is greater than Median is greater than Mode You just take: Mean, Median, Mode If it's left skewed, you will see the inequalities pointing to the left. If it's right skewed, you will see the inequalities pointing to the right. Check out http://www.ProfRobBob.com, there you will find my lessons organized by class/subject and then by topics within each class. Find free review test, useful notes and more at http://www.mathplane.com
Views: 99963 ProfRobBob
Examining Distributions
 
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Unit 1, Part 1 Quantitative Data & Categorical Data Descritptive Statistical Methods
Views: 3376 Robert Emrich
Describing the Shape, Center, and Spread of a Distribution
 
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This statistics lesson shows you how to describe the shape, center, and spread of the distribution by just examining the graph of the data given by a histogram or a dotplot. By inspecting the graph of a distribution, you could identify important statistic and behavior of your data by how the density curve forms it shape.
Views: 4529 Numberbender
Examining a Distribution
 
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Views: 284 Liz Minton
Shape, Center, and Spread
 
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How to Describe Distributions of quantitative data. How to construct a box plot from the 5 number summary.
Views: 35180 Kent Wiginton
Cleaning Data in R Course - Examining Distributions
 
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This session deals with Examining Distributions. When you are looking to optimize your features and variables you would want to examine the distributions of data in r. It is very easy to view and test the distribution for non-categorical data in R. For instance, you can describe the distribution with descdist or you can plot your data against a known distribution. Data Wrangling in R Course on Experfy.com -- R is an extraordinarily powerful language with a vast community of great resources, but where should you start when all you want to do is get your data into a usable format? How do you know your data might be ready? What are the pitfalls you should watch for so that you don’t perform an analysis on bad data? This course will teach you from start to finish how to get your data into R efficiently and polish it up so that it is as good as it can be. This will let you or your team focus after this step on the statistical modeling, visualization, reporting, sharing, or any other post-processing task you wish to perform. Confidence, reliability, and reproducibility in your data acquisition and preparation are the kingpins to being able to maximize your data’s value. Follow us on: https://www.facebook.com/experfy https://twitter.com/experfy https://experfy.com
Views: 410 Experfy
Key concepts in modelling the spatial distribution of fish
 
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A presentation by Benjamin Planque, Institute of marine research in Tromsö, on the PhD course: Modeling to study the Baltic Sea ecosystem - possibilities and challenges Askö Laboratory in March 2013 To BEAMs homepage: http://www.smf.su.se/beam
Views: 642 SUBalticSeaCentre
Distinguishing Distributions
 
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Statistics: looking at the shape, center, and spread of various distributions.
Views: 606 aconley5
3.2 Examining Distributions
 
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made with ezvid, free download at http://ezvid.com
Views: 110 Alex Brazill
Examining A Dot Plot
 
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This video shows how to find the median, mean, IQR and the number of observations of a dot plot.
Views: 4531 mrmaisonet
Bernoulli Distribution
 
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In this tutorial we are going to discuss another type of probability distributions- the Bernoulli distribution. Bernoulli events are the simplest we can have, since they consist of a single trial and only 2 possible outcomes. Examining them will provide us with the fundamental properties necessary to build more complex scenarios as we dive deeper into the field of probability. LINK TO OUR DISTRIBUTIONS PLAYLIST: https://www.youtube.com/playlist?list=PLaFfQroTgZnzbfK-Rie19FdV6diehETQy LINK TO OUR ‘INTRODUCTION TO DISCRETE UNIFORM DISTRIBUTION’ VIDEO: https://www.youtube.com/watch?v=3C9mpj-NYgo&t Follow us on YouTube: https://www.youtube.com/c/365DataScience Connect with us on our social media platforms: Website: https://bit.ly/2TrLiXb Facebook: https://www.facebook.com/365datascience Instagram: https://www.instagram.com/365datascience Twitter: https://twitter.com/365datascience LinkedIn: https://www.linkedin.com/company/365datascience Prepare yourself for a career in data science with our comprehensive program: https://bit.ly/2HnysSC Get in touch about the training at: [email protected] Comment, like, share, and subscribe! We will be happy to hear from you and will get back to you!
Views: 374 365 Data Science
Describing the shape of a graph
 
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Shape center spread and outliers are used to describe the shape of a dot plot
Views: 11067 Andrew Farrell
SPSS: Analyzing Subsets and Groups
 
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Instructional video on how to analyze subsets and groups of data using SPSS, statistical analysis and data management software. For more information, visit SSDS at https://ssds.stanford.edu.
Examining and Screening Data for Multivariate Data Analysis with Unrouped Data - Part III
 
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We discuss how to deal with Data Accuracy, Missing Values, Outliers, Normality, Linearity and Homoscedasticity while performing Multiple Regression.
Views: 146 Vikas Agrawal
02 - Normal Distribution
 
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Materials Looking at Data - Distributions Slides: Looking at Data Lecture Normal Distributions Lecture Looking at Data - Relationships Slides: Looking at Data - Relationships Lecture Producing Data Slides: Producing Data Lecture Objectives Examine distributions. Summarize and describe the distribution of a categorical variable in context. Generate and interpret several different graphical displays of the distribution of a quantitative variable (histogram, stemplot, boxplot). Summarize and describe the distribution of a quantitative variable in context: a) describe the overall pattern, b) describe striking deviations from the pattern. Relate measures of center and spread to the shape of the distribution, and choose the appropriate measures in different contexts. Apply the standard deviation rule to the special case of distributions having the "normal" shape. Explore relationships between variables using graphical and numerical measures. Classify a data analysis situation (involving two variables) according to the "role-type classification," and state the appropriate display and/or numerical measures that should be used in order to summarize the data. Compare and contrast distributions (of quantitative data) from two or more groups, and produce a brief summary, interpreting your findings in context. Graphically display the relationship between two quantitative variables and describe: a) the overall pattern, and b) striking deviations from the pattern. Interpret the value of the correlation coefficient, and be aware of its limitations as a numerical measure of the association between two quantitative variables. In the special case of linear relationship, use the least squares regression line as a summary of the overall pattern, and use it to make predictions. Recognize the distinction between association and causation, and identify potential lurking variables for explaining an observed relationship. Recognize and explain the phenomenon of Simpson's Paradox as it relates to interpreting the relationship between two variables. Sampling. Examine methods of drawing samples from populations Identify the sampling method used in a study and discuss its implications and potential limitations. Designing Studies. Distinguish between multiple studies, and learn details about each study design. Identify the design of a study (controlled experiment vs. observational study) and other features of the study design (randomized, blind etc.). Explain how the study design impacts the types of conclusions that can be drawn.
Views: 1815 Lollynonymous
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: 811655 Dr Nic's Maths and Stats
Lesson 9 - Stem And Leaf Plots (Statistics Tutor)
 
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This is just a few minutes of a complete course. Get full lessons & more subjects at: http://www.MathTutorDVD.com. You will learn how to read stem and leaf plots in statistics during this statistics tutorial online lesson. We will work several stem and leaf examples and solve statistics problems step by step.
Views: 5216 MathAndScience[.]com
Checking that data is normally distributed using R
 
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Learn how to check whether your data have a normal distribution, using the chi-squared goodness-of-fit test using R. https://global.oup.com/academic/product/research-methods-for-the-biosciences-9780198728498 This video relates to section 8.4 in the book Research Methods for the Biosciences third edition by Debbie Holmes, Peter Moody, Diana Dine, and Laurence Trueman. The video is narrated by Laurence Trueman. © Oxford University Press
Examining the Bell Curve: A Data Science Perspective
 
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Table of Contents: 00:15 - Topics 00:32 - Quick Facts 01:08 - Why Study TBC? 01:16 - 01:35 - 01:58 - 02:11 - Comment 02:16 - 02:35 - 03:25 - Principal Theses 03:28 - 04:04 - 04:35 - 04:53 - 05:18 - 05:42 - 05:54 - 06:19 - 07:25 - My Approach 07:27 - 07:33 - 07:59 - 08:38 - STEPS
Views: 227 Alfred Essa
03 - Looking at Data Relationships
 
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Materials Looking at Data - Distributions Slides: Looking at Data Lecture Normal Distributions Lecture Looking at Data - Relationships Slides: Looking at Data - Relationships Lecture Producing Data Slides: Producing Data Lecture Objectives Examine distributions. Summarize and describe the distribution of a categorical variable in context. Generate and interpret several different graphical displays of the distribution of a quantitative variable (histogram, stemplot, boxplot). Summarize and describe the distribution of a quantitative variable in context: a) describe the overall pattern, b) describe striking deviations from the pattern. Relate measures of center and spread to the shape of the distribution, and choose the appropriate measures in different contexts. Apply the standard deviation rule to the special case of distributions having the "normal" shape. Explore relationships between variables using graphical and numerical measures. Classify a data analysis situation (involving two variables) according to the "role-type classification," and state the appropriate display and/or numerical measures that should be used in order to summarize the data. Compare and contrast distributions (of quantitative data) from two or more groups, and produce a brief summary, interpreting your findings in context. Graphically display the relationship between two quantitative variables and describe: a) the overall pattern, and b) striking deviations from the pattern. Interpret the value of the correlation coefficient, and be aware of its limitations as a numerical measure of the association between two quantitative variables. In the special case of linear relationship, use the least squares regression line as a summary of the overall pattern, and use it to make predictions. Recognize the distinction between association and causation, and identify potential lurking variables for explaining an observed relationship. Recognize and explain the phenomenon of Simpson's Paradox as it relates to interpreting the relationship between two variables. Sampling. Examine methods of drawing samples from populations Identify the sampling method used in a study and discuss its implications and potential limitations. Designing Studies. Distinguish between multiple studies, and learn details about each study design. Identify the design of a study (controlled experiment vs. observational study) and other features of the study design (randomized, blind etc.). Explain how the study design impacts the types of conclusions that can be drawn.
Views: 763 Lollynonymous
Central limit theorem | Inferential statistics | Probability and Statistics | Khan Academy
 
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Introduction to the central limit theorem and the sampling distribution of the mean Watch the next lesson: https://www.khanacademy.org/math/probability/statistics-inferential/sampling_distribution/v/sampling-distribution-of-the-sample-mean?utm_source=YT&utm_medium=Desc&utm_campaign=ProbabilityandStatistics Missed the previous lesson? https://www.khanacademy.org/math/probability/statistics-inferential/normal_distribution/v/ck12-org-more-empirical-rule-and-z-score-practice?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: 1355070 Khan Academy
Examining the Future of Field and Inside Sales in Distribution
 
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Distribution as a whole is rapidly changing with the rise of digital platforms. In general, we are moving toward more efficient shopping and buying mechanisms. In the report, this efficiency manifests as a move toward digital and inside sales and away from field sales. To learn more, visit http://www.WayPointAnalytics.com
Views: 43 WayPoint Analytics
2 Non-Parametric - Examining Skewness of Data
 
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The Non-Parametric Analyses video series is available for FREE as an iTune book for download on the iPad. The ISBN number is 978-1-62407-809-5. The title is "Non-Parametric Analyses." Waller and Lumadue are the authors. The iTune text provides accompanying narrative and the SPSS readouts used in the video series. The textbook can be obtained from: https://itunes.apple.com/us/book/non-parametric-analyses/id657196105?ls=1 This video guides the viewer through the process of using SPSS to examine the skewness and kurtosis of a data set. Time is also spent examining the SPSS readout to provide insight into the interpretation of the results.
Views: 5778 Lee Rusty Waller
Analysing residuals (Minitab)
 
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Currell: Scientific Data Analysis. Analysis for Fig 5.14 data. See also 6.4. http://ukcatalogue.oup.com/product/9780198712541.do © Oxford University Press
Math Antics - Mean, Median and Mode
 
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Learn More at mathantics.com Visit http://www.mathantics.com for more Free math videos and additional subscription based content!
Views: 1305176 mathantics
Exploring relationships between categorical variables
 
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This video discusses numerical and graphical methods for exploring relationships between two categorical variables, using contingency tables, segmented bar plots, and mosaic plots.
Views: 29697 Mine Çetinkaya-Rundel
The Soft Drink Excitation: minitab 5, analysing data 3
 
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See the overview of the project and analysis (Part 1) here: http://youtu.be/KzQI_hcfBFo This goes with Part 2 here: http://youtu.be/G06UrSnxZRc which has some visualisations of how anova works, why equal variances and normal residuals matter, and problems often associated with residuals like skewness (left or right) and kurtosis (platykutic, mesokurtic, leptokurtic). This video looks at examining residuals in more detail, particularly skewness and kurtosis. If you are only doing a basic introductory course in data analysis this may be more advanced than your course requires. Please leave me a question if you don't understand something. Formulas used are: "Values of 2 standard errors of skewness (ses) or more (regardless of sign) are probably skewed to a significant degree. The ses can be estimated roughly using the following formula (after Tabachnick & Fidell, 1996): The square root of 6 over N." "Values of 2 standard errors of kurtosis (sek) or more (regardless of sign) probably differ from mesokurtic to a significant degree. The sek can be estimated roughly using the following formula (after Tabachnick & Fidell, 1996): the square root of 24 over N" at http://jalt.org/test/bro_1.htm or look up the text referenced. You don't necessarily need a formula to work out if a distribution is skewed or kurtotic - it is usually pretty self evident from a histogram.
Views: 587 st8tistics
SCGIS: Whale Watch - Developing Models to Predict Blue Whale Distribution in Near R
 
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Presented by: Helen Bailey (Univeristy of Maryland, Center for Environmental Science) and Briana Abrahms (NOAA's Southwest Fisher Science Center Fellow) Blue whales (Balaenoptera musculus) are listed as Endangered under the U.S. Endangered Species Act due to population depletion from commercial whaling. In the eastern North Pacific, ship strikes remain the largest threat to the recovery of this protected species. Static management approaches along the U.S. West Coast are being implemented to direct traffic into designated shipping lanes, yet whale distributions are dynamic and may shift in response to changing environmental conditions, necessitating integration of dynamic management approaches. We developed a dynamic, near real-time blue whale distribution model with the aim to mitigate ship strike risk in a project called WhaleWatch. This model is now being further refined by examining potential changes in predictive skill by developing distribution models using a) daily surface and subsurface variables from a data-assimilative regional ocean model compared to monthly remotely-sensed environmental data, and b) an ensemble modeling approach with multiple datasets (satellite tags and ship surveys) and methods (Generalized Additive Mixed Models and Boosted Regression Trees) compared to a single-model approach. Dynamic, high-resolution species distribution models with strong predictive performance are a valuable tool for targeting management needs in near real-time. This general approach is readily transferable to other species and spatial management needs.
Views: 66 databasin
Statistics Making Sense of Data Examining Relationships Between Two Categorical Variables
 
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Statistics Making Sense of Data Examining Relationships Between Two Categorical Variables
Views: 284 LummoMy
Examining and Screening Data for Univariate Data Analysis with Grouped Data - Part I
 
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We discuss how to deal with Data Accuracy, Missing Values, Outliers, Normality, Linearity and Homoscedasticity while performing ANOVA. Correction: Levene's test should be performed using "rincom4" variable and not "rincom3" variable as shown in the video. However , we still get the same results.
Views: 260 Vikas Agrawal
Testing the Normality of Residuals in a Regression using SPSS
 
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This video demonstrates how test the normality of residuals in SPSS. The residuals are the values of the dependent variable minus the predicted values.
Views: 55707 Dr. Todd Grande
AP Statistics: Exploring Data (ED) Video 1 - Data
 
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This video goes over data, variables, statistics, and the who, what, where, when, why, and how of data.
Views: 19101 Michael Porinchak
Felix Chan. Modelling Body Mass Index Distribution using Maximum Entropy Density
 
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Presenter: Felix Chan, Curtin University Title:Modelling Body Mass Index Distribution using Maximum Entropy Density Abstract: : The objective of this paper is to model the conditional distribution of Body Mass Index (BMI) by examining the relations between a set of covariates and the moments of the BMI distribution. While BMI is often seen as a leading indicators of health, most studies on the distribution of BMI did not model beyond the second order moments. This makes it difficult to examine the determinants of obesity as the mean and variance do not contain sufficient information about the tail of the distribution. This paper applies the Maximum Entropy Density framework to examine the relations between a set of covariates and the higher order moments of the BMI distribution. The aim is to provide a more accurate description on the relations between a set of determinant and the shape of the BMI distribution. Theoretically, the paper derives the asymptotic properties of the maximum likelihood estimator of the proposed density, including consistency and asymptotic normality. Empirically, this paper applies the proposed framework to an Australian dataset. The results demonstrate how different covariates affect different moments of the BMI distribution.
Views: 64 AARES/ARE-UWA
Pretest and Posttest Analysis Using SPSS
 
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This video demonstrates a few ways to analyze pretest/posttest data using SPSS.
Views: 118445 Dr. Todd Grande
Summarizing and Graphing Numerical Data
 
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This video was created by OpenIntro (openintro.org) and provides an overview of the content in Section 1.6 of OpenIntro Statistics, which is a free statistics textbook with a $10 paperback option on Amazon. In this section we will be introduced to techniques for exploring and summarizing numerical variables. Recall that outcomes of numerical variables are numbers on which it is reasonable to perform basic arithmetic operations. For example, the pop2010 variable, which represents the populations of counties in 2010, is numerical since we can sensibly discuss the difference or ratio of the populations in two counties. On the other hand, area codes and zip codes are not numerical, but rather they are categorical variables.
Views: 28567 OpenIntroOrg
Your Survey Closed, Now What? Quantitative Analysis Basics
 
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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: 22786 CSSLOhioStateU
Re Defining Core and Access: A New, Two Tier Network Model
 
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In the past, when we designed, built, and operated networks as a collection of devices (routers, switches, and firewalls) we defined our network architecture in terms of physical layers. The three-tiered Core, Aggregation/Distribution, and Access model is familiar to every network engineer. Server virtualization and new application frameworks have forced us to reconsider this model. Instead of a multi-tier hierarchical design, we have found folded-Clos (spine-leaf) networks much more efficient at moving large quantities of packets from anywhere to anywhere. In order to keep up with the speed of virtualized compute and storage, we’ve adopted virtualized networks that run as an overlay (with the physical Clos network becoming an underlay). Visualizing the network in this way gives us a new 2-tier model. Instead of trying to conceptualize the physical network into an outdated hierarchy, we can now look at the entire logical network platform as a two tier system. The (spine-leaf) underlay is the Core layer switch and the overlay is the Access layer router. This is super helpful when we want to decide where network functions should live. The Core is still there to move packets, fast, and the Access is there to handle routing and policy as well as to provide additional features and functions.
Views: 656 TeamNANOG
Simple Linear Regression | Statistics for Applied Epidemiology | Tutorial 1
 
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Simple Linear Regression Explained ▶︎▶︎More Statistics and R Programming Tutorials: (https://bit.ly/2Fhu9XU) This tutorial reviews simple linear regression and data exploration. Interpreting regression model output, examining errors, model assumptions and checking model assumptions. ►► Watch More: ► Intro to Statistics Course: https://bit.ly/2SQOxDH ►R Tutorials for Data Science https://bit.ly/1A1Pixc ►Getting Started with R (Series 1): https://bit.ly/2PkTneg ►Graphs and Descriptive Statistics in R (Series 2): https://bit.ly/2PkTneg ►Probability distributions in R (Series 3): https://bit.ly/2AT3wpI ►Bivariate analysis in R (Series 4): https://bit.ly/2SXvcRi ►Linear Regression in R (Series 5): https://bit.ly/1iytAtm ►ANOVA Concept and with R https://bit.ly/2zBwjgL ►Linear Regression Concept and with R https://bit.ly/2z8fXg1 ►Puppet Master of Statistics: https://bit.ly/2RDAAv4 ►SPPH 400 Tutorials: https://bit.ly/2Ff3gE0 Follow MarinStatsLectures Subscribe: https://goo.gl/4vDQzT website: https://statslectures.com Facebook:https://goo.gl/qYQavS Twitter:https://goo.gl/393AQG Instagram: https://goo.gl/fdPiDn This statistics tutorial is prepared to support SPPH 500: Analytic Methods in Applied Epidemiology course offered in the School of Population and Public Health at the University of British Columbia (UBC). These videos are created as part of #marinstatslectures video tutorial series to support some courses at UBC (#IntroductoryStatistics and #RVideoTutorials for Health Science Research), although we make all videos available to the everyone everywhere for free. Thanks for watching! Have fun and remember that statistics is almost as beautiful as a unicorn!
Analyzing the Cloudera Hortonworks Merger
 
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Breaking Big Data News last was about the Cloudera Hortonworks merger. What does that mean for the Hadoop Ecosystem? In this episode of the Big Data Beard YouTube show Brett Roberts and Thomas Henson will analyze the merger of the two premier Hadoop Ecosystem distributors. Find out our predictions for the future of Cloudera-Hortonworks and the Hadoop Community as a whole. Be sure to leave comments on your prediction from the Cloudera Hortonworks merger. ► GROW YOUR BIG DATA BEARD - Site devoted to "Exploring all aspects of Big Data & Analytics" ◄ https://bigdatabeard.com/ ► BIG DATA BEARD PODCAST - Subscribe to learn what's going on in the Big Data Community ◄ https://bigdatabeard.com/subscribe-to-podcast/ ► CONNECT ON TWITTER ◄ https://twitter.com/bigdatabeard
Views: 950 Big Data Beard
Sample variance | Descriptive statistics | Probability and Statistics | Khan Academy
 
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Thinking about how we can estimate the variance of a population by looking at the data in a sample. Practice this lesson yourself on KhanAcademy.org right now: https://www.khanacademy.org/math/probability/descriptive-statistics/variance_std_deviation/e/variance?utm_source=YT&utm_medium=Desc&utm_campaign=ProbabilityandStatistics Watch the next lesson: https://www.khanacademy.org/math/probability/descriptive-statistics/variance_std_deviation/v/review-and-intuition-why-we-divide-by-n-1-for-the-unbiased-sample-variance?utm_source=YT&utm_medium=Desc&utm_campaign=ProbabilityandStatistics Missed the previous lesson? https://www.khanacademy.org/math/probability/descriptive-statistics/variance_std_deviation/v/variance-of-a-population?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: 330260 Khan Academy
Hierarchical Network Design
 
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A brief introduction to the hierarchical network design, Enterprise Architecture model with enterprise campus and enterprise edge. Also solving tending IT challenges with the Borderless Network Architecture, Collaboration Architecture and Data Center/Virtualization Architecture.,
Views: 25450 ciscoKim
AP Statistics: Scatterplots, Association, Correlation - Part 1
 
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This video covers the basis of examining the relationship between two quantitative variables. If you are interested in practice AP questions to help prepare you for the AP test in May please utilize Barron’s AP Statistics Question Bank. Access via the web or by downloading the app in iTunes or the Google Play Store. Links are below: Web: https://www.examiam.com/ap iTunes: https://itunes.apple.com/us/app/barrons-ap-statistics/id1438469502?mt=8 Google Play Store: https://play.google.com/store/apps/details?id=com.examiam.apstatistics
Views: 34407 Michael Porinchak
R Statistics tutorial: Creating bar charts for categorical variables | lynda.com
 
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This tutorial walks you through the process, step-by-step, for creating bar charts for a single categorical variable with R Statistics. Watch more at http://www.lynda.com/R-tutorials/R-Statistics-Essential-Training/142447-2.html?utm_campaign=ka3mDnOMR3k&utm_medium=viral&utm_source=youtube. This tutorial is a single movie from the R Statistics Essential Training course presented by lynda.com author Barton Poulson. The complete course is 5 hours and 59 minutes and shows how to model statistical relationships using graphs, calculations, tests, and other analysis tools in R Statistics. Introduction 1. Getting Started 2. Charts for One Variable 3. Statistics for One Variable 4. Modifying Data 5. Working with the Data File 6. Charts for Associations 7. Statistics for Associations 8. Charts for Three or More Variables 9. Statistics for Three or More Variables Conclusion
Views: 17205 LinkedIn Learning
Statistics | 1.3 Examining Graphs
 
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This video will help you understand how to analyze graphs in statistics, Key Concepts: Graph, Center, Unusual Values, Spread, Shape, Uniform, Symmetric, Skewed Thank you for watching our educational video.
Views: 579 Club Academia
Rep. Farethold Chairs Hearing on "Examining Data Security at the United States Postal Service"
 
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This hearing examined the collection and distribution of mail cover information by the Postal Service, as well as concerns related to the agency’s recent data breach.
Views: 213 BlakeFarenthold
Examining the Size Field
 
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In this video we demonstrate how to use the new Size Field examine metric in Pointwise V18. The size field is a visual representation of the target cell edge lengths in an unstructured block. The best part is that the size field of an empty unstructured block can be examined. Yes, empty! This allows you to get a pretty good sense for what the distribution of cell sizes will be in the final volume mesh even before you initialize your block. Download Pointwise Version 18 at http://www.pointwise.com/support/ If you liked this video, then be sure to let us know and subscribe for more videos like this. If you have any comments or questions, then drop us line below or connect with us on Twitter. Twitter: https://twitter.com/pointwise
Views: 236 Pointwise
Analysing and commenting on graphical output using OSEM
 
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This video teaches how to comment on graphs and other statistical output by using the acronym OSEM. It is especially useful for students in NCEA statistics classes in New Zealand, but many people everywhere can find OSEM awesome!