This course gives a mathematical introduction to neural coding and dynamics. Topics include convolution, correlation, linear systems, game theory, signal detection theory, probability theory, information theory, and reinforcement learning. Applications to neural coding, focusing on the visual system are covered, as well as, Hodgkin-Huxley and other related models of neural excitability, stochastic models of ion channels, cable theory, and models of synaptic transmission. Visit the Seung Lab Web site.

Introduction to Sociology 2e adheres to the scope and sequence of a typical, one-semester introductory sociology course. It offers comprehensive coverage of core concepts, foundational scholars, and emerging theories, which are supported by a wealth of engaging learning materials. The textbook presents detailed section reviews with rich questions, discussions that help students apply their knowledge, and features that draw learners into the discipline in meaningful ways. The second edition retains the book’s conceptual organization, aligning to most courses, and has been significantly updated to reflect the latest research and provide examples most relevant to today’s students. In order to help instructors transition to the revised version, the 2e changes are described within the preface.

Differentiate between four kinds of research methods: surveys, field research, experiments, and secondary data analysis
Understand why different topics are better suited to different research approaches

Textbook for a one-semester, undergraduate statistics course. It was used for Math 156 at Colorado State University–Pueblo in the spring semester of 2017.

Table of Contents
Chapter 1. One-Variable Statistics: Basics
Chapter 2. Bi-variate Statistics: Basics
Chapter 3. Linear Regression
Chapter 4. Probability Theory
Chapter 5. Bringing Home the Data
Chapter 6. Basic Inferences

This course provides an introduction to critical thinking, informal logic, and a small amount of formal logic; its purpose is to provide students with the basic tools of analytical reasoning. Upon successful completion of this course, students will be able to: Understand what critical thinking is and why it is valuable; Distinguish between good and bad definitions, Recognize the differences between explicit and implicit meaning, and remove ambiguities of meaning from unclearly worded statements; Recognize arguments in writing, pick out good and bad arguments by their form, and construct sound arguments of their own; Diagnose the most common reasoning errors and fallacies, as well as identify ways of improving them; Understand the basics of sentential and predicate logic and gain practice manipulating meaning symbolically; Understand the rudiments of scientific methodology and reasoning; Evaluate arguments that rely on specific types of visual representation; Understand the basics of strategic reasoning and problem solving; Understand the particular challenges involved in reasoning about values and morality; Diagnose fallacies and evaluate arguments about values and morality. (Philosophy 102)

You are probably asking yourself the question, "When and where will I use statistics?". If you read any newspaper or watch television, or use the Internet, you will see statistical information. There are statistics about crime, sports, education, politics, and real estate. Typically, when you read a newspaper article or watch a news program on television, you are given sample information. With this information, you may make a decision about the correctness of a statement, claim, or "fact." Statistical methods can help you make the "best educated guess."

Psychology is designed to meet scope and sequence requirements for the single-semester introduction to psychology course. The book offers a comprehensive treatment of core concepts, grounded in both classic studies and current and emerging research. The text also includes coverage of the DSM-5 in examinations of psychological disorders. Psychology incorporates discussions that reflect the diversity within the discipline, as well as the diversity of cultures and communities across the globe.Senior Contributing AuthorsRose M. Spielman, Formerly of Quinnipiac UniversityContributing AuthorsKathryn Dumper, Bainbridge State CollegeWilliam Jenkins, Mercer UniversityArlene Lacombe, Saint Joseph's UniversityMarilyn Lovett, Livingstone CollegeMarion Perlmutter, University of Michigan

By the end of this section, you will be able to:

Explain what a correlation coefficient tells us about the relationship between variables
Recognize that correlation does not indicate a cause-and-effect relationship between variables
Discuss our tendency to look for relationships between variables that do not really exist
Explain random sampling and assignment of participants into experimental and control groups
Discuss how experimenter or participant bias could affect the results of an experiment
Identify independent and dependent variables

This course introduces statistical tools and techniques that are routinely used by modern statisticians for a wide variety of applications. Upon successful completion of this course, the student will be able to: apply statistical hypothesis testing for one population; conduct statistical hypothesis testing and estimation for two populations; apply multiple regression analysis to analyze a multivariate problem; analyze the outputs for a multiple regression model and interpret the regression results; conduct test hypotheses about the significance of a multiple regression model and test the significance of the independent variables in the model; select appropriate multiple regression models using automatic model selection, forward selection, backward elimination, and stepwise selection; recognize and address issues when using multiple regression analysis; identify situations when nonparametric tests are appropriate; conduct nonparametric tests; explain the principles underlying General Linear Model, Multilevel Modeling, Data Mining, Machine Learning, Bayesian Belief Networks, Neural Network, and Support Vector Machine. This free course may be completed online at any time. (Mathematics 251)

This course is a broad treatment of statistics, concentrating on specific statistical techniques used in science and industry. Topics include: hypothesis testing and estimation, confidence intervals, chi-square tests, nonparametric statistics, analysis of variance, regression, correlation, decision theory, and Bayesian statistics.