The module will explain Autocorrelation and its function and properties. Also, examples will be provided to help you step through some of the more complicated statistical analysis.

The author demonstrates and provides evidence that there is a strong correlation between family wealth and student standardized achievement test scores.

The applets in this section allow you to see how different bivariate data look under different correlation structures. The Movie applet either creates data for a particular correlation or animates a multitude data sets ranging correlations from -1 to 1.

This module introduces correlation and covariance.

With your mouse, drag data points and their error bars, and watch the best-fit polynomial curve update instantly. You choose the type of fit: linear, quadratic, cubic, or quartic. The reduced chi-square statistic shows you when the fit is good. Or you can try to find the best fit by manually adjusting fit parameters.

This module describes a decision procedure for decoding received signals. The implementation and analysis of a correlation receiver is included.

Examples of the use of a correlator-type receiver

Frequency distributions of weave density should match if two paintings are from the same canvas roll according to the thread counting algorithm we employ.

Mathematical introduction to neural coding and dynamics. Convolution, correlation, linear systems, Fourier analysis, signal detection theory, probability theory, and information theory. Applications to neural coding, focusing on the visual system. Hodgkin-Huxley and related models of neural excitability, stochastic models of ion channels, cable theory, and models of synaptic transmission.

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.

A project by Rice University students done in Fall 2004 for ELEC 301. An investigation of signal processing techniques applied to iris recognition.

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)

A performance analysis of correlator-type and matched filter receivers on binary symbol transmission using both orthogonal and antipodal signal sets. Both types of receivers perform identically with the same detection criteria.

This course is designed to introduce you to quantitative analysis (QA), or the application of statistics in the workplace. The student will learn how to apply statistical tools to analyze data, draw conclusions, and make predictions of the future. Upon successful completion of this course, the student will be able to: Explain the importance of statistics to business; Explain the differences between quantitative and qualitative data; Define the following terms: data sets, mean, median, mode, standard deviation, and variance; Summarize data in a tabular format using frequency distributions and visually with histograms; Describe the concept of a probability distribution and the properties of different distributions; Describe the effect of skewness on distributions; Define what an outlier is and describe what it can do to summaries of data; Differentiate between discrete and continuous probability distributions; Define the concept of a random variable and the Law of Large Numbers; Differentiate the population from a sample; Define simple random sampling; Explain how to avoid selection bias and sampling errors in survey sampling, such as selection and estimation errors, and apply these techniques; Relate the central limit theorem to sample size; Describe the different sampling methods, including systematic, stratified random, cluster, convenience, panel, and quota sampling, and give an example of each; Use a point estimator from a sample to estimate the entire population; Estimate intervals where the population parameter could exist; Test hypotheses using one-tailed and two-tailed tests; Differentiate between the null and alternative hypotheses in hypothesis testing; Relate the significance level to hypothesis testing; Define a region of acceptance based on a test statistic; Differentiate between dependent and independent variables; Plot a regression line and demonstrate an understanding of how the regression coefficient shapes that line; Work with statistical data in a spreadsheet environment. (Business Administration 204)

This course develops logical, empirically based arguments using statistical techniques and analytic methods. It covers elementary statistics, probability, and other types of quantitative reasoning useful for description, estimation, comparison, and explanation. Emphasis is placed on the use and limitations of analytical techniques in planning practice. This course is required for and restricted to first-year M.C.P. students.

Speech analysis and synthesis with Linear Predictive Coding (LPC) exploit the predictable nature of speech signals. Cross-correlation, autocorrelation, and autocovariance provide the mathematical tools to determine this predictability.

Descriptive and inferential statistics for the behavioral and neurological sciences are considered. Techniques such as t-tests, factorial analysis of (co)variance, correlation, multiple regression, and nonparametric tests are introduced. Subject provides an introductory overview of some advanced methods such as path analysis, factor analysis, discriminant analysis, and analysis of functional MRI data. Basic issues of research design and methodology intimately associated with data analysis are discussed.

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)

A broad treatment of statistics, concentrating on specific statistical techniques used in science and industry. Topics: hypothesis testing and estimation. Confidence intervals, chi-square tests, nonparametric statistics, analysis of variance, regression, and correlation. Treatment more oriented toward application and less toward theory than 18.441.