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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: 116656 CrashCourse
Thinking about shapes of distributions | Data and statistics | 6th grade | Khan Academy
 
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Some distributions are symmetrical, with data evenly distributed about the mean. Other distributions are "skewed," with data tending to the left or right of the mean. We sometimes say that skewed distributions have "tails." Practice this lesson yourself on KhanAcademy.org right now: https://www.khanacademy.org/math/cc-sixth-grade-math/cc-6th-data-statistics/cc-6-shape-of-data/e/shape-of-distributions?utm_source=YT&utm_medium=Desc&utm_campaign=6thgrade Watch the next lesson: https://www.khanacademy.org/math/cc-sixth-grade-math/cc-6th-data-statistics/cc-6-shape-of-data/v/examples-analyzing-clusters-gaps-peaks-and-outliers-for-distributions?utm_source=YT&utm_medium=Desc&utm_campaign=6thgrade Missed the previous lesson? https://www.khanacademy.org/math/cc-sixth-grade-math/cc-6th-data-statistics/cc-7th-compare-data-displays/v/comparing-dot-plots-histograms-and-box-plots?utm_source=YT&utm_medium=Desc&utm_campaign=6thgrade Grade 6th on Khan Academy: By the 6th grade, you're becoming a sophisticated mathemagician. You'll be able to add, subtract, multiply, and divide any non-negative numbers (including decimals and fractions) that any grumpy ogre throws at you. Mind-blowing ideas like exponents (you saw these briefly in the 5th grade), ratios, percents, negative numbers, and variable expressions will start being in your comfort zone. Most importantly, the algebraic side of mathematics is a whole new kind of fun! And if that is not enough, we are going to continue with our understanding of ideas like the coordinate plane (from 5th grade) and area while beginning to derive meaning from data! (Content was selected for this grade level based on a typical curriculum in the United States.) 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 Khan Academy‰Ûªs 6th grade channel: https://www.youtube.com/channel/UCnif494Ay2S-PuYlDVrOwYQ?sub_confirmation=1 Subscribe to Khan Academy: https://www.youtube.com/subscription_center?add_user=khanacademy
Views: 307115 Khan Academy
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: 93737 ProfRobBob
Normal Distribution - Explained Simply (part 1)
 
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I describe the standard normal distribution and its properties with respect to the percentage of observations within each standard deviation. I also make reference to two key statistical demarcation points (i.e., 1.96 and 2.58) and their relationship to the normal distribution. Finally, I mention two tests that can be used to test normal distributions for statistical significance. normal distribution, normal probability distribution, standard normal distribution, normal distribution curve, bell shaped curve
Views: 1000361 how2stats
Examining Distributions
 
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Unit 1, Part 1 Quantitative Data & Categorical Data Descritptive Statistical Methods
Views: 3301 Robert Emrich
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: 93 Vikas Agrawal
Center, spread, and shape of distributions — Harder example | Math | New SAT | Khan Academy
 
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Watch Sal work through a harder Center, spread, and shape of distributions problem. Watch the next lesson: https://www.khanacademy.org/test-prep/new-sat/new-sat-math/new-sat-problem-solving-data-analysis/v/sat-math-q10-easier?utm_source=YT&utm_medium=Desc&utm_campaign=NewSAT Missed the previous lesson? https://www.khanacademy.org/test-prep/new-sat/new-sat-math/new-sat-problem-solving-data-analysis/v/sat-math-q9-easier?utm_source=YT&utm_medium=Desc&utm_campaign=NewSAT New SAT (starting March 2016) on Khan Academy: Practice all of the skills you’ll need for the new SAT. We also have four official practice exams from College Board. The October 2015 PSAT is in the style of the new SAT. 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 New SAT channel: https://www.youtube.com/channel/UCb6Pzsn8oIFv1N8eGem570A?sub_confirmation=1 Subscribe to KhanAcademy: https://www.youtube.com/subscription_center?add_user=khanacademy
Views: 99715 Khan Academy
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: 309 Experfy
Distinguishing Distributions
 
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Statistics: looking at the shape, center, and spread of various distributions.
Views: 583 aconley5
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: 422105 Kent Löfgren
3.2 Examining Distributions
 
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made with ezvid, free download at http://ezvid.com
Views: 106 Alex Brazill
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: 29774 Kent Wiginton
Examining a Distribution
 
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Views: 251 Liz Minton
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: 1953 Numberbender
Exploring Categorical Data
 
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This video was created by OpenIntro (openintro.org) and provides an overview of the content in Section 1.7 of OpenIntro Statistics, which is a free statistics textbook with a $10 paperback option on Amazon. In this section we will be introduced a couple of techniques for exploring and summarizing categorical variables.
Views: 17639 OpenIntroOrg
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: 1785 Lollynonymous
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: 5571 Lee Rusty Waller
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: 251 LummoMy
Examining First Marriages In The US - National Health Statistics Report (Data From 2006-2010)
 
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Here is the most in-depth data I could find from a representative sample of Americans aged 15-44. This is data from the NSFG (National Survey of Family Growth). There is data on cohabitation trends, marriage trends, as well as a more in-depth breakdown of age trends and marriage/divorce by race. It also compares 2006-2010 with data from 1982, 1995, and 2002. Hope you guys enjoy some of the interesting correlational data presented in this report. Read it here for more data and numbers if you want: https://www.cdc.gov/nchs/data/nhsr/nhsr049.pdf BIG SHOUTOUT TO NEW Patreon Patrons: MikeTO, Walter, and Symp09 Also, shoutout to other patrons: Liam and Brian My Patreon: https://www.patreon.com/jerryliu/ Please go to my Amazon store and check out the stuff featured in this video: https://www.amazon.com/shop/influencer-bbbbd7c6 If you haven't checked out my Amazon store, here's some of my key video recording equipment Here's my Zoom H4n recorder: http://amzn.to/2BVdMg4 Here's the new webcam I use to record: http://amzn.to/2kbOZfk If you want to check out the camera I use to film: http://amzn.to/2lqGufi Here's the mic I bought that I use to record from my phone: http://amzn.to/2j9Ns8Q Here's the condenser mic I use to record from the screen: http://amzn.to/2hgDjqs Here's the wireless mic I use: http://amzn.to/2lqLKQ2 Shoutout to Patreon sponsors, whether past or present.
Views: 1823 Jerry Liu
GLM in R: Poisson regression with categorical Xs| biostats data |part 1
 
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A biostatistics example. Data: infected cell count (DV); explanatory variables are factors - smoker,sex,age. Keywords: interaction terms, hierarchical structure, deviance, barchart, overdispersion.
Views: 3845 Phil Chan
R - Exploring Data (part 4) - Bivariate Summaries
 
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We continue to discuss the used cars data from part 1,2, and r of this Module. Here we start looking at some relationships among the features in our data. We create a scatterplot and side-by-side boxplot.
Views: 7643 Jalayer Academy
AP Stats: Midterm Review Examining Data
 
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This video gives a quick recap about how to approach examining data by looking at center, spread, shape, and outliers.
Views: 729 Michael Porinchak
Exploratory Analysis of TCGA-BLCA RNA Seq data
 
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00:10 - The memory setting to handle large data sets. 00:40 - Importing samples from GDC data center. 01:30 - Creating a series, and set GDC sample annotations as parameters. 03:50 - Setting a normalization as viewing data distribution patterns. 06:20 - Filtering. 08:20 - PCA, and marking samples in a cluster. 08:50 - Visualizing parameters to help interpreting the result. 13:10 - Examining data distribution patterns of artificial effects. 18:20 - Excluding a part of samples from the analysis. 19:00 - Defining subgroups of tumor samples. 21:10 - Extracting differentially expressed genes between the subgroups. 22:00 - Creating a new series of Normal-Tumor paired samples. 24:30 - Making tumor/normal ratios to cancel individual differences. 27:00 - Examining "tumorization" effect on the expression profile. 27:30 - Defining 2 types of "tumorization" from a result of PCA. 29:10 - Extracting differentially expressed genes between the "tumorization" types. 30:00 - Comparing results for further analysis.
Views: 628 subiosupport
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: 23934 OpenIntroOrg
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: 44604 Dr. Todd Grande
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: 25742 Mine Çetinkaya-Rundel
How to detect outliers in SPSS
 
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I describe and discuss the available procedure in SPSS to detect outliers. The procedure is based on an examination of a boxplot. SPSS can identify two different types of outliers, based on two different inter-quartile range rule multipliers. Neither multiplier (1.5 and 3.0) is ideal, however, with a bit of extra work, you can calculate an outlier based on the 2.2 multiplier. I demonstrate how to do so here: https://www.youtube.com/watch?v=WSflSmcNRFI
Views: 106355 how2stats
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: 41 WayPoint Analytics
Examining Mass Defection From APC With Anthony Kila
 
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For more information log on to http://www.channelstv.com
Views: 1464 Channels Television
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: 581 st8tistics
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: 1696 mrmaisonet
Analyzing Two Variables - Data Analysis with R
 
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This video is part of an online course, Data Analysis with R. Check out the course here: https://www.udacity.com/course/ud651. This course was designed as part of a program to help you and others become a Data Analyst. You can check out the full details of the program here: https://www.udacity.com/course/nd002.
Views: 2921 Udacity
Models for the Future Utility: Examining NY State's Distributed System Vision
 
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The discussion is a featured event of Solar One and NYC ACRE’s cleantech panel discussion series, Clean Energy Connections, in partnership with Greentech Media. Follow our ongoing online discussion at http://www.cleanecnyc.org/next-event/ Join Clean Energy Connections for our first event of 2015 – Models for the Future Utility: Examining New York State’s Distributed System Platform Provider Vision The New York Public Service Commission’s Reforming Energy Vision (REV) is among the most forward-looking electric utility transformation initiatives in the North America, if not globally. In short, the plan has the potential to fundamentally shift utility regulation to meet the needs of a more distributed, consumer-focused energy system that the future is quickly ushering in with the advent of distributed generation, storage, electric vehicles, smart homes and a plethora of next-generation energy technologies. The bedrock of the initiative is the definition and formation of Distributed Service Platform Providers (DSPPs). This session will explore the definition of a DSPP and how it differs from today’s utility model, explain the values and incentives for all parties involved, outline new products and services that can be supported, put forth the challenges surrounding DER penetration, and investigate how DSPPs interact with other energy markets.
Views: 1775 Greentech Media
Trigeminal Nerve (Cranial Nerve V) Examination
 
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Tests to check Trigeminal Nerve
Views: 3950 OSCE Data-base
Data Visualization with Python | Module 5 | Examining Relationships in Data with Scatter Plots
 
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Introduction to Data Visualization with Python | Module 5 | Examining Relationships in Data with Scatter Plots
Views: 8 24x7 Learning
Skewed Distributions and Mean, Median, and Mode (Measures of Central Tendency)
 
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Asymmetrical (Skewed) Distributions and Mean, Median, and Mode (Measures of Central Tendency). Discover the Relationship between the Mean, Median, and Mode for Skewed Distributions. skewed distributions and mean, median, and mode asymmetrical skewed central tendency and skew Lifetime access to SPSS videos: http://tinyurl.com/kuejrzz YouTube Channel: https://www.youtube.com/user/statisticsinstructor Video Transcript: Here let's take a look at positive and negatively skewed distributions and we'll also examine the relationship between the three measures of central tendency in each of those types of distributions. So our first distribution here is going to look something like this bear with the slight inaccuracies here due to the pin tablet. We have right here is one measure of central tendency, here's a second, and here's a third. They're about evenly spaced they could vary in practice but the key here is the ordering. OK this first one is notice how it's the highest point in the distribution here, right? So that is the mode. The mode is always the highest point in the distribution. OK then the next one. Actually let's skip over this one. If you think about the measures of central tendency, which one is most influenced by the outliers or the extreme scores over here? Which measure of central tendency in other words is pulled towards the tail of the distribution? Well, that is the mean. The mean is the one that is pulled towards the tail. So it's going to be the furthest to the right. And then that only leaves us with one more left, right? That would be the median. So the median is in the middle here. OK and this type of distribution, if we have a number line here this is the positive end, this is the negative end. So remember the skew is determined by where the tail goes. So the tail here goes to the positive end so this distribution is known as positively skewed or it has positive skew. Let's look at the other side here. Here we have the opposite type of distribution. Here's the negative end on a number line, here's the positive end. The tail here points to the negative end, so this is a negatively skewed distribution. OK the highest point is somewhere around here. So the highest point's there. So what's this one? That is the mode, that's I would say the easiest of the three to figure out. And then we have two more lines here; one here give or take, and then one here. Look at this one it's the closest to the tail and that means it's influenced by these extreme scores. So that would be the mean. And that leaves us with the one that's in the middle. That's a clue there. The middle one is the median. The median is the middle score. And in these two types of distributions it's going to be the middle measure of central tendency. Let's say that on our number line here, this was 10, this was 20, and this was 30. I'm just making these up but this point here is 10, this point here is 20, and this point here is 30. And then the same thing here: 10, 20, 30. OK so the 10 points to the mean, the 20 points to the median, and the 30 here points to the mode. OK so in a positively skewed distribution notice how the mean is larger than the median, which is larger than the mode. So you could say something like this: the mean is greater than median, which is greater than the mode. In a negatively skewed distribution it's the opposite: notice that the mode is the biggest at 30, followed by the median at 20, and then the mean 10. So here we have mode greater than median which is greater than the mean. OK, that's it. For positively skewed and negatively skewed distributions, this shows the relationship between the three measures of central tendency. Channel Description: For step by step help with statistics, with a focus on SPSS. Both descriptive and inferential statistics covered. For descriptive statistics, topics covered include: mean, median, and mode in spss, standard deviation and variance in spss, bar charts in spss, histograms in spss, bivariate scatterplots in spss, stem and leaf plots in spss, frequency distribution tables in spss, creating labels in spss, sorting variables in spss, inserting variables in spss, inserting rows in spss, and modifying default options in spss. For inferential statistics, topics covered include: t tests in spss, anova in spss, correlation in spss, regression in spss, chi square in spss, and MANOVA in spss. New videos regularly posted. Videos series coming soon include: multiple regression in spss, factor analysis in spss, nonparametric tests in spss, multiple comparisons in spss, linear contrasts in spss, and many more. Subscribe today! YouTube Channel: https://www.youtube.com/user/statisticsinstructor Lifetime access to SPSS videos: http://tinyurl.com/m2532td
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: 18509 Michael Porinchak
Stats 3.1a Examining Relationships
 
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Made with Doodlecast Pro from the iTunes App Store. http://www.doodlecastpro.com
Views: 1276 Jeremy Haselhorst
1 Non-Parametric - An Introduction
 
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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 provides an introduction to nonparametric analysis.
Views: 59516 Lee Rusty Waller
Analysing residuals (SPSS)
 
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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
AP Statistics: Binomial and Geometric Models
 
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This video explains the basics of the geometric and binomial models with a few basic examples.
Views: 10593 Michael Porinchak
R tutorial: Histograms and outliers
 
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Learn more about credit risk modeling in R: https://www.datacamp.com/courses/introduction-to-credit-risk-modeling-in-r Now let's look at some continuous variables using histograms and plots. For a basic histogram you call function hist() with the variable of interest, in this case interest rate. You can use the arguments main and xlab for nicer labels. The frequencies for the variable of interest are shown on the y-axis. Here, you can see that all loans had an interest rate of over 5 percent and very few loans had an interest rate higher than 20%. Let's have a look at the histogram of annual income. We notice that we get a strange result here, with seemingly just one big bar. Storing the histogram in hist_income and using dollar sign breaks, we get information on the location of the histogram breaks. In order to get a clear idea on the data structure, you can change the number of breaks using the breaks argument, such that you get a more intuitive plot. This can be done by choosing a number that seems more appropriate, or use a rule of thumb, such as the square root of the number of observations in the data set. This results in a much longer vector of breaks. However, the result still doesn't look very nice here, with a lot of blank space. The x-axis of the histogram automatically ranges from the smallest observed value to the largest one. Let us look at a scatterplot to see what is going on. In this plot, the annual income is shown on the y-axis and the observation's index number is shown on the x-axis. We see that there is one huge salary of 6 million dollars where none of the others is bigger than around 2 million dollars, We consider this an outlier. In statistics, an outlier is an observation that is abnormally distant from other values. But when is a distance abnormal? In general, data scientists will use their expert judgement, rules of thumb or a combination of both. Expert judgement could be used if the data scientist is considered an expert in the domain of credit risk modeling. He can then judge that an annual wage above 3 million dollars must be an error and should be deleted from the data set. If a data scientist wants to rely on a rule of thumb, he could delete all values that are bigger or smaller than 1.5 times the interquartile-range, which is the range between the first and the third quartiles of the variable's distribution. As outliers in the negative range did not occur here, we only delete ones in the positive range. After deleting outliers, you get the following results. These histograms are more informative than the initial ones including the outliers, especially the histogram that was constructed using the rule of thumb. Note that quite some observations were deleted using this rule of thumb. Even if you don't plan to actually leave out these outliers in your analysis, it might still be useful to delete them temporarily when visualizing the data. Let's conclude by looking at a bivariate plot. When you include a second variable in the plot() function, the first argument will be plotted on the x-axis and the second argument on the y-axis. A bivariate plot for employment length and annual income is shown here. Having a look at bivariate plots can be interesting to track bivariate outliers, which are outliers on two dimensions of the data. For the combination plotted here, we only see an outlier on the scale of annual income and not for employment length. Now let's try to make some plots and histograms ourselves.
Views: 3382 DataCamp
Relationship between categorical variables in a 2 way table
 
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I show you how to analyze catagorical data in a 2 way table. We will find marginal distributions, conditional probabilities, and bar graphs. Find free review test, useful notes and more at http://www.mathplane.com
Views: 7521 ProfRobBob
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: 817813 mathantics
Median Polish - Exploratory Data Analysis
 
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[NOTE: Good CC/Subtitles Added] Median Polish is an Exploratory Data Analysis technique for analyzing two-way tables. This video shows a step-by-step example of working the Median Polish on a simple 3x3 two-way table: -15 4 1 6 16 30 -5 4 -12 Here is a simple R program that will create 3x3 two-way tables for you to practice with, and the median polish results generated by R: tbl = matrix(data=as.integer(runif(9) * 10), nrow=3, ncol=3) tbl medpolish(tbl)
Views: 4510 Timothy Chen Allen

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