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Search results “Examining distribution data model”

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Views: 116656 CrashCourse

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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

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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

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Unit 1, Part 1 Quantitative Data & Categorical Data Descritptive Statistical Methods
Views: 3301 Robert Emrich

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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

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Views: 309 Experfy

06:57
Statistics: looking at the shape, center, and spread of various distributions.
Views: 583 aconley5

09:15
Views: 422105 Kent Löfgren

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Views: 106 Alex Brazill

08:10
Views: 9006 Amr Arafat

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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

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Views: 251 Liz Minton

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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

12:44
Views: 1520 Amr Arafat

04:59
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

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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

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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

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Statistics Making Sense of Data Examining Relationships Between Two Categorical Variables
Views: 251 LummoMy

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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

15:02
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

18:42
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.

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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

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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

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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

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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

01:07:32
Views: 321 oversightandreform

03:28:54
Views: 1339 oversightandreform

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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

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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

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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

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Views: 1464 Channels Television

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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

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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

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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

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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

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Tests to check Trigeminal Nerve
Views: 3950 OSCE Data-base

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Introduction to Data Visualization with Python | Module 5 | Examining Relationships in Data with Scatter Plots
Views: 8 24x7 Learning

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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

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Made with Doodlecast Pro from the iTunes App Store. http://www.doodlecastpro.com
Views: 1276 Jeremy Haselhorst

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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

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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

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This video explains the basics of the geometric and binomial models with a few basic examples.
Views: 10593 Michael Porinchak

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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

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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

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