PPT-Using Tests for Resemblance to Teach Topics in Categorical Data

Analysis Amy G Froelich and Dan Nettleton Iowa State University JSE Webinar November 2013 Does My Baby Really Look Like Me Background Your baby looks just like you Background. ca Abstract Categorical datafrequency data and discrete dataare most of ten presented in tables and analyses using loglinear models and logistic regression are most often presented in terms of parame ter estimates Over the past decade I and others ha Stevan. J. Arnold. Department of Integrative Biology. Oregon State University. Thesis. Most traits are affected by . many genes. We can model the inheritance of such traits with . a statistical approach . TVCG 2013. Sungkil. Lee, Mike Sips, and Hans-Peter Seidel. Introduction. Class Visibility. Optimization . Example. Conclusion. Outline. Principles of effective color palettes (Trumbo, 1981) . Order: colors chosen to present an ordered statistical variables should be perceived as preserving that order. . Review from last time. Metasemantics. Theories of Meaning. The Conformal Theory. The Idea Theory. The Resemblance Theory. Berkeley. General terms/. Abstract ideas. Locke on General Terms. “. It is not enough for the perfection of language, that sounds can be made signs of ideas, unless those signs can be so made use of as to comprehend several particular . EDA. Quantitative Univariate EDA. Slide #. 2. Exploratory Data Analysis. Univariate EDA – . Describe the distribution. Distribution. is concerned with what values a variable takes and how often it takes each value. Daniel Bush, DPI School Financial Services. WASBO Accounting Conference. March 16, 2016. Targeted purpose. Outside revenue limit. Offsets shared cost. Usually “sum-certain,” often prorated. Three broad types. Homework #2. Chapter 3 Problem 57. Car Auction. Each row of data indicates the make of vehicles sold at auction in the US in 2010. The data table describes 1,884 vehicles.. Problem 57(a). a. Using software, tabulate the frequencies of the makes of cars.. Distinguish between:. - A statistic and a parameter. - A categorical and a quantitative variable. - A response and an explanatory . variable. Identify:. - When a categorical variable is ordinal. - When a quantitative variable is continuous. Professor Kari Lock Morgan. kari@stat.duke.edu. . Course Website:. http://stat.duke.edu/courses/Fall12/sta101.002/. Sakai:. https://sakai.duke.edu/portal/site/STAT101_Fall12. Course Website. Syllabus. WASBO Accounting Conference. March 16, 2016. Targeted purpose. Outside revenue limit. Offsets shared cost. Usually “sum-certain,” often prorated. Three broad types. Reimbursement. Formula. Grant. Objectives. Students will be able to:. Graph categorical data. Model athletic . PERFORMANCE. Use technology to simulate athletic . PERFORMANCE. Graphing Calculator. https://www.youtube.com/watch?v=ilL9FoTUQqw. Deontological (Duty-Based Approaches). Actions are inherently good or bad.. Help the less fortunate. . Steal.. Kant’s Categorical Imperative. Act always on the principle that ensures that all individuals will be treated as ends in themselves and never as merely a means to an end.. Binary, Ordinal and . Contingency Table Data. Ronan Fitzpatrick. Lead Statistician. nQuery. Webinar. Host. Agenda. Categorical Data Overview. Binary Data Methods & Sample Size. Ordinal Data Methods & Sample Size. 1. STAT 101Exploratory Data Analysis I1/25/12 One Categorical Variable Two Categorical Variables One Quantitative Variable – CenterSection 2.1, 2.2Professor Kari Lock MorganDuke University2. AnnouncementsTextbooks are here!My office hours: (Old...