Introduces statistical data analysis, concentrating on techniques used in management science and finance. Topics chosen from: statistical graphics, basics of sampling, estimation, hypothesis testing, linear and logistic regression, analysis of variance, contingency tables, forecasting, statistical quality control, principal components, and factor analysis. SAS or similar package used for data analysis. This course is an introduction to applied statistics and data analysis. Topics include collecting and exploring data, basic inference, simple and multiple linear regression, analysis of variance, nonparametric methods, and statistical computing. It is not a course in mathematical statistics, but provides a balance between statistical theory and application. Prerequisites are calculus, probability, and linear algebra. We would like to acknowledge the contributions that Prof. Roy Welsch (MIT), Prof. Gordon Kaufman (MIT), Prof. Jacqueline Telford (Johns Hopkins University), and Prof. Ramón León (University of Tennessee) have made to the course material.

Collecting, displaying, and interpreting data has become a part of life in our fast paced technological world. In the following lessons students will be responsible for gathering and displaying data in a line plot. They will find two measures of central tendency according to the data. Students will work as a whole class, in groups, pairs, and individually.

The purpose of this investigation is to have students analyze cloud cover and to compose written conclusions to a given related scenario.

Students play a game in which they place beans on numbers that represent the sum of two dice. Each time a number comes up in a dice roll, a corresponding bean may be removed. The first person who removes all his beans wins the game. Students mathematically analyze the game to develop strategies.

Introduces the concepts, techniques, and devices used to measure engineering properties of materials. Emphasis on measurement of load-deformation characteristics and failure modes of both natural and fabricated materials. Weekly experiments include data collection, data analysis, and interpretation and presentation of results.

Covers computational and data analysis techniques for environmental engineering applications. First third of subject introduces MATLAB and numerical modeling. Second third emphasizes probabilistic concepts used in data analysis. Final third provides experience with statistical methods for analyzing field and laboratory data. Numerical techniques such as Monte Carlo simulation are used to illustrate the effects of variability and sampling. Concepts are illustrated with environmental examples and data sets. This subject is a computer-oriented introduction to probability and data analysis. It is designed to give students the knowledge and practical experience they need to interpret lab and field data. Basic probability concepts are introduced at the outset because they provide a systematic way to describe uncertainty. They form the basis for the analysis of quantitative data in science and engineering. The MATLAB® programming language is used to perform virtual experiments and to analyze real-world data sets, many downloaded from the web. Programming applications include display and assessment of data sets, investigation of hypotheses, and identification of possible casual relationships between variables. This is the first semester that two courses, Computing and Data Analysis for Environmental Applications (1.017) and Uncertainty in Engineering (1.010), are being jointly offered and taught as a single course.

We've argued that societal stratification is "both a condition and a process" (Kerckhoff, 2000). The former captures what the distribution of valued resources (e.g., money, education) among other things look like in a society. The question, most simply, is 'who gets what'?

Using two different coins and recording the results of both coins helps students dispel this initial misconception as they analyze the graph results. Class discussion should focus on analyzing the data to determine if the game is fair or not. Directions and gameboard are included in the download.

Students need many experiences tossing one die, collecting data and analyzing that data to construct meaning for the probability of the different outcomes. Since a small student sample is often skewed, it is necessary for teachers to help students collate class data to better approximate the theoretical probability that the outcomes of tossing a single die are equally likely. The one-die games below motivate data collection opportunities that students will play again and again while generating larger data samples for class discussion. Many teachers find a simple class tally chart an effective way to organize data for this ongoing experiment. Students simply add results to the class results as they complete a game. Class discussions focus on the fairness of the games and discussion about whether each player has an equal chance of winning.

Once students realize that the outcomes of tossing one die are equally likely, they sometimes transfer that knowledge to tossing two dice. This is a common misconception that is best addressed through data collection and analyzing that data rather than through telling. See suggested two-dice games below that students can play to gain experiential knowledge of the results of tossing two dice. Several of the games encourage students to develop better strategies in order to win and their growing understanding of probability will be shaped by these experiences.

This module focuses on the solar wind information obtained by NASA-s Genesis spacecraft. Genesis collects pristine solar wind material -ionic particles from the Sun- that will provide clues about the elemental composition of the original solar nebula.

These activities support students as they conceptually develop a sense of how probability affects the outcome of games. Students will find that applying their knowledge of probability will help them win some of the games

In Data Analysis: As Real World As It Gets, we feature resources for teaching about data and statistics as supported by the NCTM Standards (NCTM, 2000). Data collection and analysis can be an avenue into the meaningful mathematics and problem-solving skills needed by students in the twenty-first century. And an answer to the student question, Why do we have to study math? can be found when teaching mathematics with a real-world statistics approach.

Elementary Statistics is an introduction to data analysis course that makes use of graphical and numerical techniques to study patterns and departures from patterns. The student studies randomness with emphasis on understanding variation, collects information in the face of uncertainty, checks distributional assumptions, tests hypotheses, uses probability as a tool for anticipating what the distribution of data may look like under a set of assumptions, and uses appropriate statistical models to draw conclusions from data. The course introduces the student to applications in engineering, business, economics, medicine, education, the sciences, and other related fields. The use of technology (computers or graphing calculators) will be required in certain applications.

Regression analysis is an enormously popular and powerful tool, used ubiquitously in the social and behavioral sciences. Most courses on the subject immediately dive into the mathematical aspects of the subject and illustrate the technique on problems that are already highly structured. As a result, most students come away with little idea of the wide range of problems to which regression analysis can be applied and how to represent those problems in a way that cleverly utilizes readily available data. Few understand, at a conceptual level, the limitations of regression analysis. The OLI Empirical Research Methods course bridges the gap between the mathematical foundations of regression and its practical application. We teach students how to move from an interesting question about the world to a regression model that, when estimated, meaningfully addresses the question asked. It emphasizes causal analysis as the main research goal and multivariate linear regression as the main statistical tool. We teach a process that involves: Formulating a research problem, Developing and formalizing hypotheses, Collecting data relevant to these hypotheses, Analyzing the data using an appropriate regression model, and Critically interpreting the results of these analyses.

Gingerbread men and gingerbread houses enjoy special popularity around the holidays, but many of these gingerbread activities are timeless and complement literature titles that teachers use at the beginning of school or after the holidays. It's very easy to incorporate mathematics into a study of gingerbread men, and students will enjoy the data collection activities and games while learning math skills and deepening their understanding of important mathematical concepts. Look through these math activities and add some to your repertoire. Consider broadening the gingerbread math to include measurement, games and problem solving this year.

What makes Earth the perfect home for life as we know it? Students in this activity explore the orbital characteristics a planetary home needs to support Earth-like life forms.

Basic concepts of computer modeling in science and engineering using discrete particle systems and continuum fields. Techniques and software for statistical sampling, simulation, data analysis and visualization. Use of statistical, quantum chemical, molecular dynamics, Monte Carlo, mesoscale and continuum methods to study fundamental physical phenomena encountered in the fields of computational physics, chemistry, mechanics, materials science, biology, and applied mathematics. Applications drawn from a range of disciplines to build a broad-based understanding of complex structures and interactions in problems where simulation is on equal-footing with theory and experiment. Term project allows development of individual interest. Student mentoring by a coordinated team of participating faculty from across the Institute.

Basic concepts of computer modeling in science and engineering using discrete particle systems and continuum fields. Techniques and software for statistical sampling, simulation, data analysis and visualization. Use of statistical, quantum chemical, molecular dynamics, Monte Carlo, mesoscale and continuum methods to study fundamental physical phenomena encountered in the fields of computational physics, chemistry, mechanics, materials science, biology, and applied mathematics. Applications drawn from a range of disciplines to build a broad-based understanding of complex structures and interactions in problems where simulation is on equal-footing with theory and experiment. Term project allows development of individual interest. Student mentoring by a coordinated team of participating faculty from across the Institute.