jtleek/dataanalysis IDEAS: Economics and Finance Research Methods in Biostatistics I Instructor: Brian Caffo Originally Offered: Fall 2006 Offered By: Biostatistics Course Number: Description: Presents fundamental concepts in applied probability, exploratory data analysis, and statistical inference, focusing on probability and analysis of one and two samples. Learning Objectives The goal of this course is to equip biostatistics and quantitative scientists with core applied statistical concepts and methods: 1) The course will refresh the mathematical, computational, statistical and probability background that students will need to take the course. 2) The course will introduce students to the display and communication of statistical data. 3) Students will learn the distinctions between the fundamental paradigms underlying statistical methodology. 4) Students will learn the basics of maximum likelihood. 5) Students will learn the basics of frequentist methods: hypothesis testing, confidence intervals. 7) Students will learn the creation and interpretation of P values.
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Statistics Tutorial - Instructions Application This tutorial is based on a set of data and an experiment taken from your own field of study. The Application or Interpretation section uses this data, and the study that generated it, to allow you to try out each statistical technique on real data. At the first level, you are shown the statistics that have been calculated from the data and tested on your understanding of what they mean. You will be asked to answer questions and be given feedback as you go. The flat line next to the box for the answer shows that you have not yet answered the question. The question mark in a circle is the help button.
Introduction - Handbook of Biological Statistics Variables in Statistics In statistics, a variable has two defining characteristics: A variable is an attribute that describes a person, place, thing, or idea. The value of the variable can "vary" from one entity to another. For example, a person's hair color is a potential variable, which could have the value of "blond" for one person and "brunette" for another. Qualitative vs. Variables can be classified as qualitative (aka, categorical) or quantitative (aka, numeric). Qualitative. In algebraic equations, quantitative variables are represented by symbols (e.g., x, y, or z). Discrete vs. Quantitative variables can be further classified as discrete or continuous. Some examples will clarify the difference between discrete and continouous variables. Suppose the fire department mandates that all fire fighters must weigh between 150 and 250 pounds. Univariate vs. Statistical data are often classified according to the number of variables being studied. Univariate data. Test Your Understanding of This Lesson Problem 1 I.
swirldev/swirl_courses: A collection of interactive courses for the swirl R package.