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Search results “Exploring displaying and examining data”

14:44
Using SPSS to examine data for accuracy, completeness, basic descriptives and normality.

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: 18109 OpenIntroOrg

17:10
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
Views: 809 Research By Design

16:04
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.
Views: 14869 Michael Porinchak

22:47
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.
Views: 3259 Michael Porinchak

05:07

09:26
Introduction to Information Management System and Oracle SQL For Full Course Experience Please Go To http://mentorsnet.org/course_preview?course_id=4 Full Course Experience Includes 1. Access to course videos and exercises 2. View & manage your progress/pace 3. In-class projects and code reviews 4. Personal guidance from your Mentors
Views: 2167 Oresoft LWC

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: 707 Lollynonymous

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

12:14
Views: 795 Kimberly Brehm

12:10
This video gives a quick recap about how to approach examining data by looking at center, spread, shape, and outliers.
Views: 866 Michael Porinchak

04:37
This video discusses numerical and graphical methods for exploring relationships between two categorical variables, using contingency tables, segmented bar plots, and mosaic plots.
Views: 26254 Mine Çetinkaya-Rundel

07:04
This video goes over data, variables, statistics, and the who, what, where, when, why, and how of data.
Views: 18638 Michael Porinchak

08:16
Use of Excel's Pivot Table to examine a dataset for "insights".
Views: 314 Stephen Peplow

03:51
examining a question that analyzing data collection techniques
Views: 594 shaunteaches

13:52
This video explains how to determine if two categorical variable are independent from a contingency table.
Views: 1960 Statstan

16:45
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.

05:04
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: 1025094 how2stats

06:17
Exploring Data - Lesson 3 - Graphing Categorical Data
Views: 201 Jerry Linch

03:53
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

40:47
Views: 129 O'Reilly

03:48
How to use the 'Split File' tool in SPSS to split your data file by a categorical variable. In this example, I split my file by gender so that I can analyse data for males and females separately. ASK SPSS Tutorial Series

11:04
Views: 862308 mathantics

01:22

01:39
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.

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: 752 Lollynonymous

13:46
Simple summaries for categorical and numerical variables. R walkthroughs available here: https://github.com/jgscott/learnR Notes available at: https://jgscott.github.io/STA371H_Spring2017/files/STA371H_coursepacket_part1.pdf
Views: 486 James Scott

38:24
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: 8753 Map A Syst

04:08
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

00:31
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: 605 ACM SIGCHI

05:27
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?

18:11
Basic tools of Descriptive Analytics when the data represents categories of some characteristic of interest.

00:54
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.

01:38
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 -~-~~-~~~-~~-~-

19:36
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.

00:31
Pocket Transfers: Interaction Techniques for Transferring Content from Situated Displays to Mobile Devices Ville Mäkelä, Mohamed Khamis, Lukas Mecke, Jobin James, Markku Turunen, Florian Alt CHI '18: ACM CHI Conference on Human Factors in Computing Systems Session: Cross device interaction Abstract We present Pocket Transfers: interaction techniques that allow users to transfer content from situated displays to a personal mobile device while keeping the device in a pocket or bag. Existing content transfer solutions require direct manipulation of the mobile device, making inter-action slower and less flexible. Our introduced tech-niques employ touch, mid-air gestures, gaze, and a mul-timodal combination of gaze and mid-air gestures. We evaluated the techniques in a novel user study (N=20), where we considered dynamic scenarios where the user approaches the display, completes the task, and leaves. We show that all pocket transfer techniques are fast and seen as highly convenient. Mid-air gestures are the most efficient touchless method for transferring a single item, while the multimodal method is the fastest touchless method when multiple items are transferred. We provide guidelines to help researchers and practitioners choose the most suitable content transfer techniques for their systems. DOI: https://doi.org/10.1145/3173574.3173709 WEB: https://chi2018.acm.org/
Views: 462 ACM SIGCHI

07:52
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: 110045 how2stats

11:18
Views: 3397 Amr Arafat

05:04
Views: 94 The Audiopedia

06:41
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: 753163 Eugene O'Loughlin

05:57
SPSS - Summarizing Two Categorical Variables: Cross-tabulation table and clustered bar charts with either counts or relative frequencies (and 3 ways to get them)
Views: 179696 DWR447

02:21
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: 769 MemSQL

00:35
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: 502 obxlabs

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: 1785 Lollynonymous

06:56
[0:14] What are event frames? [1:34] Walkthrough of how to discover related events in PI Coresight [2:20] Explanation on how the events list in PI Coresight is populated [3:58] Examine an event and apply event time range to PI Coresight display [5:51] Add an event data item to PI Coresight display Video content is copyright of OSIsoft, LLC © 2014. All rights reserved. Any redistribution or reproduction of part or all of the contents in any form is prohibited other than for your personal and non-commercial use.
Views: 4231 OSIsoft Learning

04:02
Embark on a voyage through time, infinitely scalable from the Big Bang to today, exploring this master timeline of the cosmos, Earth, life, and human experience. By unifying a wide variety of data and historical perspectives, ChronoZoom provides a framework for examining historical events, trends, and themes, enabling researchers, educators, and students to synthesize knowledge from different studies of history, specialized timelines, and media resources, courtesy of the cloud. This platform for research and learning helps users develop a broad understanding of how the past has unfolded and discover unexpected relationships and historical convergences that help explain the sweep of Big History—and the relationship between the humanities and the sciences. Become your own time traveler and check out the beta today.
Views: 8089 Microsoft Research

32:13
Most flow cytometers are capable of providing kinetics data by recording a time parameter in the FCS file. Kinetics experiments are particularly useful for examining changes in calcium flux, DNA content, and antibody binding. By adding a fluorescent marker to a sample and monitoring influx, efflux, or binding of the dye over time, unique information can be gathered. FCS Express offers the capability to display and analyze kinetics data in an easy and user friendly manner while updating your plots as your analysis changes. This webinar will get you started using your calcium flux data with the new Kinetics plots in FCS Express.
Views: 1190 DeNovoSoftware

03:46
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: 308 Autodesk Research

02:49
This video presents several techniques to interactively explore representations of 2D vector fields. Through a set of simple hand postures used on large, touch-sensitive displays, our approach allows individuals to custom-design glyphs (arrows, lines, etc.) that best reveal patterns of the underlying dataset. Interactive exploration of vector fields is facilitated through freedom of glyph placement, glyph density control, and animation. The custom glyphs can be applied individually to probe specific areas of the data but can also be applied in groups to explore larger regions of a vector field. Re-positionable sources from which glyphs---animated according to the local vector field---continue to emerge are used to examine the vector field dynamically. The combination of these techniques results in an engaging visualization with which the user can rapidly explore and analyze varying types of 2D vector fields, using a virtually infinite number of custom-designed glyphs (for more information see http://tobias.isenberg.cc/VideosAndDemos/Isenberg2008IEV and http://tobias.isenberg.cc/VideosAndDemos/Grubert2008ISN).
Views: 912 Tobias Isenberg

18:22
Views: 51 Linda Quinn