Econometrics, Spring 2003

(Complete Item Description)

Abstract:: Introduction to econometric models and techniques, emphasizing regression. Advanced topics include instrumental variables, panel data methods, measurement error, and limited dependent variable models. Includes problem sets. May not count toward HASS requirement. This course covers the statistical tools needed to understand empirical economic research and to plan and execute independent research projects. Topics include statistical inference, regression, generalized least squares, instrumental variables, simultaneous equations models, and the evaluation of government policies and programs.

Subject:: Social Sciences
Grade Level:: Post-secondary
Collection:: MIT OpenCourseWare

Introduction to Probability and Statistics, Spring 2005

(Complete Item Description)

Abstract:: Elementary introduction with applications. Basic probability models. Combinatorics. Random variables. Discrete and continuous probability distributions. Statistical estimation and testing. Confidence intervals. Introduction to linear regression.

Subject:: Mathematics and Statistics
Grade Level:: Post-secondary
Collection:: MIT OpenCourseWare

No Strings Attached

Introduction to Statistics

(Complete Item Description)

Abstract:: Introduction to Statistics. Random Variable, Mean, Variance, Standard Deviation and Mathematical Expectation. Discrete Distributions: Bernoulli trials and Bernoulli distribution, geometric distribution, Poisson distribution. Continuous Distributions: random variables of the continuous type, uniform distribution, exponential distribution, gamma distribution, chi-square distribution, normal distribution, t-distributions. Estimation: biased and unbiased esimators, convidence intervals for means, convidence intervals for variances, sample size, maximum error of the point estimate, Likelihood function, Maximum Likelihood Estimation (MLE), Asymptotic Distributions of Maximum Likelihood Estimators, Chebyshev's Inequality. Hypothesis: tests of statistical hypotheses, Type I error, Type II error, tests about proportions, null hypothesis, alternative hypothesis, significance level of the test, probability value, tail-end probability, standard error of the mean, tests about one mean and one variance, test of the equality of two independent normal distributions, best critical region, Neyman-Pearson Lemma, most powerful test, uniformly most powerful critical region, Likelihood Ratio tests, critical region for the likelihood ratio test. Pseudo-Numbers: uniform pseudo-random variable generation, congruential generators, shift-register generators, Fibonacci generators, Combinations of Generators (Shuffling). The Inverse Probability Method for Generating Random Variables. The Logistic Distribution.

Subject:: Mathematics and Statistics
Grade Level:: Post-secondary
Collection:: Connexions

Mathematical Statistics, Spring 2003

(Complete Item Description)

Abstract:: Decision theory, estimation, confidence intervals, hypothesis testing. Introduces large sample theory. Asymptotic efficiency of estimates. Exponential families. Sequential analysis.

Subject:: Mathematics and Statistics
Grade Level:: Post-secondary
Collection:: MIT OpenCourseWare

Statistical Methods in Brain and Cognitive Science, Spring 2004

(Complete Item Description)

Abstract:: 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.

Subject:: Science and Technology
Grade Level:: Post-secondary
Collection:: MIT OpenCourseWare

Statistics for Applications, Fall 2003

(Complete Item Description)

Abstract:: 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.

Subject:: Mathematics and Statistics
Grade Level:: Post-secondary
Collection:: MIT OpenCourseWare

Statistics for Applications, Fall 2006

(Complete Item Description)

Abstract:: 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.

Subject:: Mathematics and Statistics
Grade Level:: Post-secondary
Collection:: MIT OpenCourseWare

"Statistics for Applications, Spring 2009"

(Complete Item Description)

Abstract:: " 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. Note: Please see the syllabus for a description of the different versions of 18.443 taught at MIT."

Subject:: Mathematics and Statistics
Grade Level:: Post-secondary
Collection:: MIT OpenCourseWare

Statistics for Laboratory Scientists I

(Complete Item Description)

Abstract:: This course introduces the basic concepts and methods of statistics with applications in the experimental biological sciences. Demonstrates methods of exploring, organizing, and presenting data, and introduces the fundamentals of probability. Presents the foundations of statistical inference, including the concepts of parameters and estimates and the use of the likelihood function, confidence intervals, and hypothesis tests. Topics include experimental design, linear regression, the analysis of two-way tables, sample size and power calculations, and a selection of the following: permutation tests, the bootstrap, survival analysis, longitudinal data analysis, nonlinear regression, and logistic regression. Introduces and employs the freely-available statistical software, R, to explore and analyze data.

Subject:: Mathematics and Statistics, Science and Technology, Social Sciences
Grade Level:: Post-secondary
Collection:: JHSPH OpenCourseWare

Statistics for Laboratory Scientists II

(Complete Item Description)

Abstract:: This course introduces the basic concepts and methods of statistics with applications in the experimental biological sciences. Demonstrates methods of exploring, organizing, and presenting data, and introduces the fundamentals of probability. Presents the foundations of statistical inference, including the concepts of parameters and estimates and the use of the likelihood function, confidence intervals, and hypothesis tests. Topics include experimental design, linear regression, the analysis of two-way tables, sample size and power calculations, and a selection of the following: permutation tests, the bootstrap, survival analysis, longitudinal data analysis, nonlinear regression, and logistic regression. Introduces and employs the freely-available statistical software, R, to explore and analyze data.

Subject:: Mathematics and Statistics, Science and Technology, Social Sciences
Grade Level:: Post-secondary
Collection:: JHSPH OpenCourseWare

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Econometrics, Spring 2003

Introduction to Probability and Statistics, Spring 2005

Introduction to Statistics

Mathematical Statistics, Spring 2003

Statistical Methods in Brain and Cognitive Science, Spring 2004

Statistics for Applications, Fall 2003

Statistics for Applications, Fall 2006

"Statistics for Applications, Spring 2009"

Statistics for Laboratory Scientists I

Statistics for Laboratory Scientists II

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