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Search results “Exploring displaying and examining data”
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: 313277 Khan Academy
AP Statistics: Displaying Quantitative Data
 
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This video covers displaying quantitative data with dot plots, stem plots and histograms. It also covers describing data by discussing shape, center, spread, and outlines. 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: 3380 Michael Porinchak
How to Use SPSS: Data Exploration
 
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Using SPSS to examine data for accuracy, completeness, basic descriptives and normality.
Explore and Summarize Categorical (Nominal or Ordinal) Data in SPSS (Ep.5)
 
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We dive deeper into exploring and summarizing categorical data with SPSS. We review levels of measurement so you can determine what kinds of data you have. Both nominal and ordinal are categorical variables because they each have limited number of distinct categories, but that ordinal data also have a meaningful, underlying order. Using the demo.sav data set, we learn how to get summary descriptive statistics, a bar chart, and a frequency table with the Frequencies command. We learn how to compare averages between categories using the Means command. This will give you a set of tools to examine categorical data and teach you how to think about your first-level analyses depending on the level of your data. You can do data cleaning and data exploration with these tools. Each of these examples uses the data set demo.sav, included with SPSS. Opening SPSS and Demo.sav for Mac: https://youtu.be/tLI5tGco4VI Opening SPSS and Demo.sav for PC: https://youtu.be/csWA-gn8qXQ Link to a Google Drive folder with all of the files that I use in the videos including the Bear Handout and StatsClass.sav. As I add new files, they will appear here, as well. https://drive.google.com/drive/folders/1n9aCsq5j4dQ6m_sv62ohDI69aol3rW6Q?usp=sharing
Views: 1421 Research By Design
R - Exploring Data (part 3) - Univariate Summaries
 
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We continue to discuss the used cars data from part 1 and 2 of this Module. Here we learn to calculate some univariate numerical summaries of features/variables as well as some basic graphs like pie charts, bar charts, histograms, and boxplots.
Views: 10228 Jalayer Academy
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: 19383 OpenIntroOrg
AP Statistics: Analyzing Categorical Data
 
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This video goes over displaying categorical data and how to examine a chart for marginal and relative marginal distributions. It also examines the idea of independence between 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: 15032 Michael Porinchak
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: 951 Michael Porinchak
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: 26678 OpenIntroOrg
04 - Producing Data
 
01:24:46
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: 247 Lollynonymous
01 - Looking at Data
 
01:50:26
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: 713 Lollynonymous
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: 18781 Michael Porinchak
How to Explore the Analysis Toolpak in Excel
 
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Follow this tutorial and learn how to work with the options present under Analysis Toolpak such as correlation and moving average. Don't forget to check out our site http://howtech.tv/ for more free how-to videos! http://youtube.com/ithowtovids - our feed http://www.facebook.com/howtechtv - join us on facebook https://plus.google.com/103440382717658277879 - our group in Google+ In this tutorial, we will teach you how to explore the analysis toolpak in Excel. First of all, we will show you how to enable Analysis toolpak. Once the toolpak has been enabled, we will show you how to work with it. Under data analysis, you will find several options. We will show you how to work with two of them in this video; correlation and moving average. We will show you how to apply both of the functions. Step # 1 -- Go to the Backstage View First of all you need to enable the analysis toolpak in Excel. Go to the "file" tab also known as the backstage view and click on "options". Step # 2 -- Enable Analysis Toolpak From the "excel options" window, go to the "add-ins" tab and select the "analysis toolpak" there. Click on the "go" button once you're done. A small window will open up; select the first option which is the analysis toolpak and click on the "ok" button. Step # 3 -- Use the Data Analysis Option Analysis ToolPak has various options but we will work with only two in this tutorial. The first one is how to find correlation. Go to the "data" tab and click on the "data analysis" button on the extreme right. From the small box, select the "correlation" option and click the "ok" button. Step # 4 -- Apply Correlation in Excel Now, the "correlation" box will appear on your screen. In the "input" range, select the cells which have data in them. If you include the first row which contains labels, then make sure the "labels in first row" box is checked. Lastly, select the "output" range. Click inside the box of the "output range" and then click on the cell where you want the output to be displayed. Click on the "ok" button to exit. The correlation result will be displayed to you. Step # 5 -- Apply Moving Average in Excel In the next sheet, we will take out the "moving average". Click on the "data analysis" button and then select "moving average". Click on the "ok" button and the "moving average" box will open up. Put in the range of input and since we are only going to select the "total" column without the labels so the "label" box should be unchecked. Specify the cell where you want the output to be displayed in the output range. Before clicking on the "ok" button, check the box of "chart output". Once done, the "moving average" will be displayed along with the chart.
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: 1029157 mathantics
How To... Plot a Normal Frequency Distribution Histogram in Excel 2010
 
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Learn how to plot a frequency distribution histogram in Microsoft Excel 2010. This helps you to see if your data are distributed normally. Note - MAC keyboard commands differ from PC.
Views: 771249 Eugene O'Loughlin
Import Data and Analyze with Python
 
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Python programming language allows sophisticated data analysis and visualization. This tutorial is a basic step-by-step introduction on how to import a text file (CSV), perform simple data analysis, export the results as a text file, and generate a trend. See https://youtu.be/pQv6zMlYJ0A for updated video for Python 3.
Views: 207923 APMonitor.com
Normal Distribution - Explained Simply (part 1)
 
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*** IMPROVED VERSION of this video here: https://youtu.be/tDLcBrLzBos 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: 1083050 how2stats
DEC 93: Thyroid data exploration
 
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DEC-CRL 93: data exploration system to provide tools which establish linked cursors between several windows. The system extends AVS, a commercial visualization system, to provide data probing and cursor linking facilities which have proven invaluable for tele-medicine and for statistical data analysis involving image data and histograms. Shown is a serial section from the thyroid of the rat. The tissue has been stained so that blood capillaries appear blueish. In the movie, the data is shown in two windows that are used for tele-collaboration. The cursors of the two windows are cross-linked such that we can independently annotate the data in each window using different colors. The annotations are shown in both windows. Using the built-in networking facilities of X-Windows, we send one of the windows to a communication partner across our computer network. Users can customize the individual views. For example, they can resize the window or recolor it. This technology can be used to share data between any X-based displays around the world. We have established such connections across the United States as well as between the US and Sweden. Our system combines tele-collaboration with extensive data exploration tools. Users can interactively establish linked cursors between many different data sets. For example, we can link images with histograms or scatter plots. For the thyroid image, researchers are interested in developing methods to automatically quantify tissue vascularity by detecting the blue pixels in the image. When the user outlines an area of the histogram, our system highlights all pixels with such colors. Such cross-linking between the image and the histogram is essential to the statistical analysis of image data. Our system uses a visual programming interface with which users interactively set up and modify their data exploration environment. Because of this interactive flexibility, the system is amenable to many forms of data exploration in a tele-collaborative environment. This work was conducted in the Visualization Group of the Cambridge Research Lab of Digital Equipment Corporation (Cambridge, MA). Thyroid data courtesy of Dr. Michael Doyle from UCSF. See also: http://campar.in.tum.de/Chair/KlinkerCRL http://ar.in.tum.de/Chair/PublicationDetail?pub=klinker1993vis
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: 27462 Mine Çetinkaya-Rundel
Shock Waves from the Sun: Space Weather in 3D
 
01:05:22
Like this content? Please consider becoming a patron: https://patreon.com/DeepAstronomy Eruptions from the Sun cause Aurora and are the drivers of space weather throughout the solar system. Creating beautiful night time displays and interesting features in the atmospheres of outer planets like Jupiter, Saturn, Neptune and Uranus, space weather can also disrupt communications on Earth and damage power grid equipment. But understanding space weather is complex and requires a 3 dimensional understanding of the magnetic structure of solar eruptions and how they propagate outwards. How is that done? By combining data from several spacecraft spread around Earth’s orbit and constructing 3D computer models a team of researchers is examining what happens when an ejection occurs and creates a shock wave. In particular, they are examining data from ESA/NASA’s Solar and Heliospheric Observatory (SOHO)and the twin Solar Terrestrial Relations Observatory (STEREO) satellites. Join Tony Darnell and Carol Christian during Afternoon Astronomy Coffee on March 22 at 3PM Eastern time as they discuss with Angelos Vourlidas (Johns Hopkins Applied Physics Lab) and Ryun Young Kwan (George Mason University) how they visualize the Sun’s Coronal Mass Ejections and what significance such events have for solar system weather. Solar Eruptions in 3D Press release: https://www.nasa.gov/feature/goddard/2018/3-nasa-satellites-recreate-solar-eruption-in-3-d Check out our website for more content: https://deepastronomy.space/video/c_RhQcb39KM
Views: 1575 Deep Astronomy
ETC1000 4: Describing Categorical Data
 
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Basic tools of Descriptive Analytics when the data represents categories of some characteristic of interest.
Visual Studio tutorial: Working with databases in Server Explorer | lynda.com
 
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In this tutorial, see how to connect to a database using the Server Explorer in Visual Studio 2012, as well as how to examine data within a database. Watch more at http://www.lynda.com/Visual-Studio-tutorials/Visual-Studio-2012-Essential-Training/118076-2.html?utm_campaign=SrsCz2q2zLo&utm_medium=viral&utm_source=youtube. This tutorial is a single movie from the Visual Studio 2012 Essential Training course presented by lynda.com author Walt Ritscher. The complete course duration is blank 7 hours and 13 minutes and shows how to get comfortable in Visual Studio, the full-featured app development environment from Microsoft, and learn how to create a variety of projects, from websites to rich Internet applications. Introduction 1. Getting Started 2. Exploring the Visual Studio Workspace 3. Understanding the Project Types 4. Exploring the Project Types 5. Code and Text Editors 6. Tools That Enhance Your Coding Sessions 7. Debugging and Troubleshooting Code 8. Using the Designers for UI Development Conclusion
Views: 51916 LinkedIn Learning
Independent Categorical Variables
 
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This video explains how to determine if two categorical variable are independent from a contingency table.
Views: 1997 Statstan
Relationships between numeric variables
 
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We look at scatterplots and ways of viewing and interpreting them. After you’ve watched this video, you should be able to answer these questions •What is the standard way of displaying the relationship between 2 numeric variables? •What sort of variable is plotted against the vertical scale and what against the horizontal scale? •It is often useful to think of patterns in such plots in terms of 3 components. What are they? •What type of question should separate clusters of dots suggest to us?
Views: 1856 Wild About Statistics
How to Sort data in a Pivot Table or Pivot Chart
 
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In this tutorial, you will learn how to sort data in a Pivot Table. Don't forget to check out our site http://howtech.tv/ for more free how-to videos! http://youtube.com/ithowtovids - our feed http://www.facebook.com/howtechtv - join us on facebook https://plus.google.com/103440382717658277879 - our group in Google+ In this tutorial, you will learn how to sort data in a Pivot Table. Sorting your data might be necessary when you are designing or examining a Pivot Table, in order to bring the values that matter first, or just to give it a sense of order. Excel offers you some easy ways to do this. Step 1. On the spreadsheet that we prepared, we put a sort function directly into the Pivot Table. This is standard for any new Pivot Table that is created. From here, we can choose to sort the columns alphabetically, in ascending or descending order. Step 2. Apart from this option, you can select the Data menu on the ribbon and choose "Sort" on any of the fields in the Pivot Table: your data will update according to your selection. Step 3. Click "Sort" from the Data menu on the ribbon and select "More Options", then choose your primary sort key to sort by name of the month, and later on by year. Step 4. Click OK. The data will update according to your selection. Result: Congratulations, you have learned how to sort your data in a Pivot Table.
AppMap: Exploring User Interface Visualizations
 
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In traditional graphical user interfaces, the majority of UI elements are hidden to the user in the default view. Application designers and users desire more space for their application data and thus want to minimize the user interface footprint. We explore the benefits of dedicating additional screen space for presenting an alternative visualization of an application's user interface. Some potential benefits are to assist users in examining complex software, understanding the extent of an application's capabilities, and exploring the available features. Thus, we propose user interface visualizations, alternative representations of an application's interface augmented with usage information. We first introduce a design space for UI visualizations and describe some initial prototypes and insights based on this design space. We then present AppMap, our new design, which displays the entire function set of AutoCAD and allows the user to interactively explore the visualization which is augmented with visual overlays displaying analytical data about the functions and their relations. In our initial studies, users welcomed this new presentation of functionality, and the unique information that it presents. We conclude by summarizing some potential benefits of UI visualizations. _______________________________________________ AppMap is a research project from Autodesk Research. Michael Rooke, Tovi Grossman & George Fitzmaurice. (2011). AppMap: Exploring User Interface Visualizations GI 2011 Conference Proceedings: Graphics Interface Conference. http://www.autodeskresearch.com/publications/appmap Autodesk Research http://www.autodeskresearch.com
Views: 312 Autodesk Research
03 - Looking at Data Relationships
 
01:40:03
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: 759 Lollynonymous
Biased Data #43
 
03:51
examining a question that analyzing data collection techniques
Views: 597 shaunteaches
02 - Normal Distribution
 
57:21
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: 1805 Lollynonymous
Overlaying Summaries with Raw Data - 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: 5732 Udacity
What is CULTURAL ANALYTICS? What does CULTURAL ANALYTICS mean? CULTURAL ANALYTICS meaning
 
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What is CULTURAL ANALYTICS? What does CULTURAL ANALYTICS mean? CULTURAL ANALYTICS meaning - CULTURAL ANALYTICS definition - CULTURAL ANALYTICS explanation. Source: Wikipedia.org article, adapted under https://creativecommons.org/licenses/by-sa/3.0/ license. SUBSCRIBE to our Google Earth flights channel - https://www.youtube.com/channel/UC6UuCPh7GrXznZi0Hz2YQnQ Cultural analytics is the exploration and research of massive cultural data sets of visual material – both digitized visual artifacts and contemporary visual and interactive media. Taking on the challenge of how to best explore large collections of rich cultural content, cultural analytics researchers developed new methods and intuitive visual techniques which rely on high-resolution visualization and digital image processing. These methods are used to address both the existing research questions in humanities, to explore new questions, and to develop new theoretical concepts which fit the mega-scale of digital culture in the early 21st century. The term "cultural analytics" was coined by Lev Manovich in 2007. Cultural analytics shares many ideas and approaches with visual analytics ("the science of analytical reasoning facilitated by visual interactive interfaces") and visual data analysis: Visual data analysis blends highly advanced computational methods with sophisticated graphics engines to tap the extraordinary ability of humans to see patterns and structure in even the most complex visual presentations. Currently applied to massive, heterogeneous, and dynamic datasets, such as those generated in studies of astrophysical, fluidic, biological, and other complex processes, the techniques have become sophisticated enough to allow the interactive manipulation of variables in real time. Ultra high-resolution displays allow teams of researchers to zoom in to examine specific aspects of the renderings, or to navigate along interesting visual pathways, following their intuitions and even hunches to see where they may lead. New research is now beginning to apply these sorts of tools to the social sciences and humanities as well, and the techniques offer considerable promise in helping us understand complex social processes like learning, political and organizational change, and the diffusion of knowledge. While increased computing power and technical developments allowing for interaction visualization have made the exploration of large data sets using visual presentations possible, the intellectual drive to understand cultural and social processes and production pre-dates many of these computational advances. Charles Joseph Minard's famous dense graphic showing Napoleon's March on Moscow (1869) offers a 19th-century example. More recently, Pierre Bourdieu's historical survey of the cultural consumption practices of mid-century Parisians, documented in La Distinction, foregrounds the study of culture and aesthetics through the lens of large data sets. Most recently, Franco Moretti's Graphs, maps, trees: abstract models for a literary history. along with many projects in the Digital Humanities reveal the benefit of large scale analysis of cultural material. To date, cultural analytics techniques have been applied to films, animations, video games, comics, magazines, books, and other print publications, artworks, photos, and a variety of other media content. The technology used ranges from open-source programs downloadable on any personal computer to supercomputer processing and large-scale displays such as the HIPerSpace (42,000 x 8000 pixels). The methodologies which fall under the umbrella of cultural analytics includes the data mining of large sets of culturally-relevant data (such as studies of library catalogs, image collections, and social networking databases.) Image processing of still and moving video, with feature recognition as well as image data extraction is used to support research into cultural and historical change. Cultural analytical methodologies are deployed to study and interpret videogames and other software forms, both at the phenomonological level (human-computer interface, feature extraction) or at the object level (the analysis of source code.) Cultural analytics relies heavily on software-based tools, and the field is related to the nascent discipline of software studies. While the objects of a cultural analytical approach are often digitized representations of the work, rather than the work in its original material form, the objects of study need not be digital works in themselves.
Views: 114 The Audiopedia
Webinar: Getting Started with Story Maps - ArcGIS Online from Esri
 
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Have an interesting historical downtown that you want people to explore? Want to explore a social issue in a spatial context? Have story with a spatial component that would benefit from linking your content to geographic location? If you are looking to share your information with a wide non-specialist audience ArcGIS Online Story Maps are a useful way to explore and display data in a variety of formats. Compare before and after scenarios, mark change over time in a series of maps, give a tour of places/features with shared characteristics, explore a particular problem in depth. This webinar will explore the basics of building storymaps and the different ways to display your information using publicly accessible Story Maps. This webinar brought to you by eXtension [email protected]: http://www.extension.org/geospatial_technology https://twitter.com/exgeospatial https://www.facebook.com/exgeospatial #MapASyst
Views: 8809 Map A Syst
Tap the ShapeTones: Exploring the effects of crossmodal congruence in an audio-visual interface
 
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Tap the ShapeTones: Exploring the effects of crossmodal congruence in an audio-visual interface Oussama Metatla, Nuno N Correia, Fiore Martin, Nick Bryan-Kinns, Tony Stockman Abstract: There is growing interest in the application of crossmodal perception to interface design. However, most research has focused on task performance measures and often ignored user experience and engagement. We present an examination of crossmodal congruence in terms of performance and engagement in the context of a memory task of audio, visual, and audio-visual stimuli. Participants in a first study showed improved performance when using a visual congruent mapping that was cancelled by the addition of audio to the baseline conditions, and a subjective preference for the audio-visual stimulus that was not reflected in the objective data. Based on these findings, we designed an audio-visual memory game to examine the effects of crossmodal congruence on user experience and engagement. Results showed higher engagement levels with congruent displays with some reported preference for potential challenge and enjoyment that an incongruent display may support, particularly for increased task complexity. ACM DL: http://dl.acm.org/citation.cfm?id=2858456 DOI: http://dx.doi.org/10.1145/2858036.2858456 ------ https://chi2016.acm.org/wp/
Views: 640 ACM SIGCHI
SAP HANA Academy - Lumira: Creating Complex Visualizations
 
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In this series of videos, Tahir Hussain Babar examines using SAP Lumira Desktop and SAP Lumira Cloud. In this video, we walk through creating basic data visualizations. First, Bob recaps the bar, line and pie charts he created in the prior lesson. Bob will use the same data set to create more complex visualizations in SAP Lumira. Bob starts by dragging the sales revenue measure onto the canvas of a bar chart to set it as the Y axis measure and then selects the top level of the geographic hierarchy, default (Geography_City), as the legend color attribute. Next, Bob drills into the lower level, region, of the geographic hierarchy by right clicking the sales revenue measure on the canvas and then choosing the drill down option that pops up. To drill even further down a user can right click on the subregion (in this example it's the US State of Texas) and choose the drill down option. Users also have the options of filtering and excluding any attribute they right click on in the canvas. Bob moves onto to examine geographical charts. Bob stresses the importance of making sure that the cities in the dataset match SAP Lumira's definition of those cities. This can be handled by clicking on the edit reconciliation option of the hierarchy to make sure that all of the options have been found and are not ambiguous. With a Geo Bubble Chart selected Bob adds the sales revenue measure to the canvas and then adds his hierarchy as the geographic dimension. As a brief aside, Bob explains how to change the formatting of a measure by choosing the display formatting option. Next, Bob drills down from the world map to show the bubble representations of the sales revenue for the various US States. On the right hand side of the canvas the maximum, minimum, and average bubble size is depicted. Next, Bob examines the same data in a Geo Choropleth Chart, which visualizes the data by coloring the different states in various shades of green with the darkest green representing the highest revenue and the lightest green representing the lowest revenue. The Geo Choropleth Chart offers the option of using a Trellis, which Bob displays by quickly creating three side-by-side charts for the sales revenue per state by year. Bob goes on to showcase the Geo Pie Chart, which is very useful when a user wants to look at how different slices of data affect a specific region in a geographic hierarchy. Bob divides the pie charts that are laid over the various US States by the line attribute. Users can then quickly drill down by filtering out specific elements of the visualized attribute. Next, Bob examines scatter plots and mentions how great they for identifying trends and correlations between multiple measures. Bob sets sales revenue and quantity sold as his two Y axes measures to see if a correlation exists. First, Bob drills down to plot the sales revenue and quantity sold by each US State by dragging the State attribute to the legend color. Bob notes the obvious correlation between the measures due to the diagonal line that can be drawn from the bottom left hand corner to the top right hand corner that intersects a majority of the points. Bob drills into the scatter plot even further by selecting the category attribute as the legend shape. Now, the scatter plot is littered with different colored shapes each representing a unique combination of a category and a State. This feature is great for identifying outliers. Bob next profiles the Scatter Matrix Chart, which lets multiple measures be displayed side by side. The ability for a user to display as many metrics as they desire in the multiple charts of Scatter Matrix Chart allows for a quick analysis of the data's correlations. Continuing the tutorial, Bob showcases how to create a Heat Map, which compares one measure by two different attributes. Bob visualizes sales revenue by State and the Heat Map depicts the data via the density of the color. When Bob adds an additional attribute for line he creates a matrix that quickly shows where the best line and state combinations exist. Finally, Bob details how to build a Tree Map, which compares two measures to one attribute. Adding measures affects the weight and color of the various blocks that compose the visualization's large rectangle.
Views: 12881 SAP HANA Academy
Lowrance Elite Ti Switching Sonar Modes
 
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Watch as Jacob Scott, Lowrance Product Expert, shows how to switch between CHIRP, 2D Sonar and StructureScan HD on the Lowrance Elite Ti graph. Learn more: https://www.lowrance.com/lowrance/series/elite-ti/?utm_source=youtube.com&utm_medium=referral
Views: 14316 Lowrance
2017 xCoAx
 
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Begleitvideo zur Konferenzpublikation: Müller, M, Keck, M, Gründer, T. Hube, N. & Groh, R.: "A Zoomable Product Browser for Elastic Displays", xCoAx 2017. http://2017.xcoax.org/ Abstract: In this paper, we present an interaction and visualization concept for elastic displays. The interaction concept was inspired by the search process of a rummage table to explore a large set of product data. The basic approach uses a similarity-based search pattern — based on a small set of items, the user refines the search result by examining similar items and exchanging them with items from the current result. A physically-based approach is used to interact with the data by deforming the surface of the elastic display. The presented visualization concept uses glyphs to directly compare items at a glance. Zoomable UI techniques controlled by the deformation of the elastic surface allow to display different levels of detail for each item.
Views: 131 MediaDesignTUD
Descriptive Statistics in Excel with Data Analysis Toolpak
 
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https://alphabench.com/data/excel-descriptive-statistics.html Generate a table of descriptive statistics in Excel for Windows and Excel 2016 for the MAC with the Data Analysis Toolpak Addin. Descriptive statistics or simply descriptives are used to characterize data sets with summary measurements that act as a simple way to summarize data. Descriptives along with a visualization of a data set are the first steps in any data analysis.
Views: 34976 Matt Macarty
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: 117932 how2stats
Supporting reproducibility in Jupyter through dataflow notebooks
 
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Supporting reproducibility in Jupyter through dataflow notebooks David Koop (University of Massachusetts Dartmouth) Jupyter notebooks are an important tool for exploratory data analysis, but their ability to combine code, outputs, and text has also enabled their use to document and reproduce results. Notebooks can further play an important role in enabling reuse and extensions of those results; users can not only examine and reexecute past results but also tweak inputs and test new ideas. However, the ability to modify and execute any cell, at any place in the notebook, can sometimes conflict with the desire to present steps in an easy-to-follow, top-down ordering. David Koop offers an overview of the Dataflow kernel, which combines the ability to tinker with the ability to define precise dependencies between cells. David shows how it can be used to robustly link cells as a notebook is developed and demonstrates how that notebook can be reused and extended without impacting its reproducibility. In addition, he explains the importance of intermediate outputs and how to structure cells to better define the boundaries between them and explores other extensions enabled by the dataflow structure and relationships to the JupyterLab environment. A dataflow notebook promotes cells to be building blocks that can be linked together based on references to each other. More technically, a dataflow encodes dependencies between computations by defining how the outputs of one computation are inputs of another computation. For example, cell A might calculate a value, cell B might read an image, and cell C might use cell A’s output to blur cell B’s output to create a new image. If we change cell B to read a different image, it seems appropriate to also update cell C’s output. With robust dependencies, we can automatically execute such updates. This structure also allows users to examine how a change to one cell might impact results in other cells of the notebook. An interactive visualization of the dependency graph can help users navigate complex notebooks. In a dataflow notebook, outputs (as defined by the last line of a cell) should reflect what was computed in a cell. Jupyter provides extensive capabilities for displaying and customizing outputs, but a cell with multiple outputs is often encapsulated as a tuple or other collection that limits these rich displays. To address this limitation, David shares extensions to the Jupyter notebook to render each output individually. For example, a tuple (df, im) would generate two outputs, the DataFrame df as an HTML table and the image im as an image. Combined with an identification scheme to reference the individual outputs, users can more clearly examine and link specific outputs to be used in other cells. Subscribe to O'Reilly on YouTube: http://goo.gl/n3QSYi Follow O'Reilly on: Twitter: http://twitter.com/oreillymedia Facebook: http://facebook.com/OReilly Instagram: https://www.instagram.com/oreillymedia LinkedIn: https://www.linkedin.com/company-beta/8459/
Views: 172 O'Reilly
FaceDisplay: Towards Asymmetric Multi-User Interaction for Nomadic Virtual Reality
 
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FaceDisplay: Towards Asymmetric Multi-User Interaction for Nomadic Virtual Reality Jan Gugenheimer, Evgeny Stemasov, Harpreet Sareen, Enrico Rukzio CHI '18: ACM CHI Conference on Human Factors in Computing Systems Session: Virtual Reality 3 Abstract Mobile VR HMDs enable scenarios where they are being used in public, excluding all the people in the surrounding (Non-HMD Users) and reducing them to be sole bystanders. We present FaceDisplay, a modified VR HMD consisting of three touch sensitive displays and a depth camera attached to its back. People in the surrounding can perceive the virtual world through the displays and interact with the HMD user via touch or gestures. To further explore the design space of FaceDisplay, we implemented three applications (FruitSlicer, SpaceFace and Conductor) each presenting different sets of aspects of the asymmetric co-located interaction (e.g. gestures vs touch). We conducted an exploratory user study (n=16), observing pairs of people experiencing two of the applications and showing a high level of enjoyment and social interaction with and without an HMD. Based on the findings we derive design considerations for asymmetric co-located VR applications and argue that VR HMDs are currently designed having only the HMD user in mind but should also include Non-HMD Users. DOI: https://doi.org/10.1145/3173574.3173628 WEB: https://chi2018.acm.org/
Views: 507 ACM SIGCHI
Line and Dot Plots- Middle School Math
 
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In this video you will learn the basics of line and dot plots Transcript Welcome to MooMoMath where we upload a new Math video everyday. In this video I would like to look and line and dot plots. Line PLOTS use x's above the numbers to show the amount for each number, whereas Dot PLOTS use dots to show the amounts above each number on a number line. Let’s look at an example. Hayden surveyed 20 students about the number of calls they made in a 24­hour period. His results are as follows: 4, 3, 4, 5, 0, 2, 4, 4, 5, 9, 4, 5, 4, 1, 0, 5, 3, 3, 5, 6 1) Find the minimum (least) and the maximum (greatest) values. MINIMUM = 0 MAXIMUM = 9 2) Draw a number line from the minimum to the maximum. 3) Draw a DOT or X above the number line to show each observation (answer) in Hayden's list. Cross off the number as you plot them. 4) LABEL the number line to show what was being counted. 5) Give the dot plot (or line plot) a TITLE. -~-~~-~~~-~~-~- Please watch: "Study Skills Teacher's Secret Guide to your Best Grades" https://www.youtube.com/watch?v=f3bsg8gaSbw -~-~~-~~~-~~-~-
TaxiStats Showcase Application
 
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TaxiStats is a new real-time dashboard application with Zoomdata. Simulated pickup and drop-off data from taxis is streamed into MemSQL as rides complete. The Zoomdata business intelligence dashboard displays that data as it is collected while exploratory analytics run simultaneously on the dataset. The dashboard includes: - Real-time data for pickups by ZIP code on the map, total volume of rides, and rides by time of day; - A map and graph that can be filtered to explore and drill down; - A live stream that can be paused or rewound to examine a specific time period.
Views: 781 MemSQL
Create aggregations: SAP Analytics Cloud (2018.12.1)
 
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In this video tutorial, you will create an aggregation calculation to display in a chart.
Views: 1027 SAPAnalyticsTraining
SPSS - Parallel Boxplots, Histograms & Descriptive Statistics for Grouped Data
 
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This video shows how to quickly construct Parallel Boxplots, Histograms & generate Descriptive Statistics for Data grouped by the levels of a categorical variable.
Views: 1837 Joshua Emmanuel
Create difference from calculations: SAP Analytics Cloud (2018.12.1)
 
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In this video, you will use the Difference From calculation type to display the difference in values for a measure between two different time periods.
Views: 1204 SAPAnalyticsTraining
Cityspeak (Demo)
 
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Cityspeak is ephemeral graffiti, an exploration into the use of private modes of communication to drive transient public displays of commentary about a particular location. Participants use their SMS- and web-enabled cellphones or wireless PDAs to send text to a common server. The text is combined with real-time data from the location and processed using the NextText text visualization software. The resulting stream of text is layered back onto the locations in the form of large-scale projections. Participants can use the display to leave commentary, tell stories, conduct conversations or simply to play with the visual characteristics of text. Cityspeak is an example p2P (private-to-public) communication which allows participants to use communication technologies we tend to think of as private--cell phones and personal digital assistants--to create public displays. p2P projects examine how culture of the street can interact with the commercial culture of media saturation.
Views: 503 obxlabs