
Explore core statistics concepts from measurement and variables to regression and analysis of variance, and see how observations, hypotheses, and emergent properties arise through statistics.
Explore the four levels of measurement—nominal, ordinal, interval, and ratio—and why ratio scales offer the most mathematical operations, with real-world examples and scaling theory.
Explore the collection, organization, analysis, interpretation, and presentation of data, differentiate descriptive and inferential statistics, and learn key sample and population symbols and measures, including mean, variance, and standard deviation.
Explore how variables, or data items, compose a data matrix of observations and a dataset, with numeric and categorical scales from nominal to ratio.
Explore how bar graphs, histograms, and pie charts translate numbers into clear visual displays, combining nominal and ratio scales to compare categories and 3-d graphics to enhance statistics.
Explore measures of central tendency by computing the mean, median, and mode, examine normal distribution and skewness, and apply these concepts to real data examples.
Explore how range, variance, and standard deviation quantify data dispersion, describe spread around the mean, and visualize with the bell curve for any sample or population.
Apply the correlation coefficient to assess relationships between two variables. See examples like hours of tv watched vs gpa and temperature vs ice cream sales.
Explore probability concepts from coin flips and dice to infer population trends, and understand how sample means, variance, and the bell curve distribution guide inferential statistics.
Compare two means with a t-test, assess mean differences and variance, and decide in a two-tailed test at 0.05 whether the null hypothesis is rejected.
Identify functions of variables by distinguishing independent and dependent variables and controlled variables, and frame experiments with null and alternative hypotheses to test cause-and-effect relationships, e.g., fertilizer on plant growth.
Learn regression analysis to model how independent variables affect a dependent variable, using simple and multiple linear or non-linear approaches, with slope, intercept, and least-squares fit.
Compare means across one independent variable using one-way anova for two or more groups, then apply the f-test to evaluate the null and alternative hypotheses.
Present a high overview of statistics from statistics lecture 14. Align this overview with the introduction to statistics course.
Explore the course catalog within introduction to statistics, interpreting a sequence of numbers and phrases to understand how course offerings are structured.
This is a course designed to take out the intimidation factor from Statistics. It is designed to show you how to, right away, use statistics for your own projects. Everything is explained clearly and simply, using interesting and informative graphics. The course also shows you the incredible usefulness and power of statistics. The course is a thorough introduction to Statistics, but without getting caught up in a swamp of formulas.
Statistics is the study of the •collection •organization •analysis •interpretation and •presentation of data.
It deals with all aspects of data including the planning of data collection in terms of the design of research projects.
Statistics is alternately described as a mathematical body of science that pertains to the collection, analysis, interpretation or explanation, and presentation of data, or as a branch of mathematics concerned with collecting and interpreting data.
After a brief Introduction we study Measurement and Scales, Statistics, Variables, Graphic Display of Variables, Measures of Central Tendency, Measures of Dispersion, Correlation, Probability, Comparison of Two Means, Types of Variables, Regression Analysis, Analysis of Variance, List of Statistical Formulas
It is a companion course to my online Research Methods and Design course. Statistics play a fundamental role in research and of our understanding of the world. Statistics are everywhere, they manifest themselves in every quantitative facet of human understanding. In this course we emphasize the role of Scaling Theory as a foundation for all statistical analyses.
I teach lecture courses and studios as I wish they would have been taught to me. Much of the graphic material in my lectures is taken or generated first hand directly by me on site. I teach to learn. I teach subjects as I wish they were taught to me. The Mission Statement. Education is a tool for the improvement of successive generations. I hear and I forget. I see and I remember. I do and I understand. Confucius
This course is designed under the premise that humans should be taught in a way that is modeled after the educational patterns of evolution.
The design, development and application of educational systems based on the educational principles of evolution generates a philosophy and methodology of education in synchrony with the evolutionary education system that is firmly and deeply rooted in each of us.
Education for evolution is an educational system designed to help propel humans forward in the natural course of evolution. The purpose of education for evolution is to enhance and strengthen the natural evolutionary process of humans through the mechanism of education. The means to achieve this objective is the design of a curricula based on the same educational techniques and strategies used by natural evolution, enhanced and guided by the application of conscious educational decisions.