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

In this outdoor activity, learners measure familiar objects in nature to practice working with the metric system. The goal is for learners to develop their "metric senses" by measuring and estimating in metric units the mass, length, temperature, and volume of familiar objects. Can be adapted into an indoor activity on a rainy day.

This module motivates and introduces the minimum variance unbiased estimator (MVUE). This is the primary criterion in the classical (frequentist) approach to parameter estimation. We introduce the concepts of mean squared error (MSE), variance, bias, unbiased estimators, and the bias-variance decomposition of the MSE.

Introduces the concepts and applications of navigation techniques using celestial bodies and satellite positioning systems such as the Global Positioning System (GPS). Topics include astronomical observations, radio navigation systems, the relationship between conventional navigation results and those obtained from GPS, and the effects of the security systems, Selective Availability, and anti-spoofing on GPS results. Laboratory sessions cover the use of sextants, astronomical telescopes, and field use of GPS. Application areas covered include ship, automobile, and aircraft navigation and positioning, including very precise positioning applications.

This activity puts a mathematical twist on the familiar äóěguessing jar.äó No guesses allowed, estimates only. Choose an object that comes in two sizes, such as large and mini pompoms, and fill one of two identical jars with each. Ask learners to estimate how many in each jar and to write down their reasoning. Encourage them to read what each other has written and decide if they agree or not. If you must give prizes, give them out for good explanations of reasoning, but not for the closest answer. Itäóťs estimation strategies that count. Available as a web page and downloadable pdf.

This resource consists of a Java applet and expository text. The applet simulates a random sample from a normal distribution, and computes standard point estimates of the distribution mean and standard deviation. The bias and mean square error are also computed.

Beginning with the famous story of the village girl trying to feed her people, the lesson involves students in the mathematics of exponential growth. Students work collaboratively to come up with a bargaining plan to trick a raja into feeding the village using algebra and estimation. The complete activity includes the development of an exponential equation, but just following the growth of the number of rice grains throughout the story gives a good introduction to exponential growth. Questions for students and ideas for assessment are provided.

Gantt charts, critical path analysis, SMART objectives and estimation skills are just some of the topics covered in this unit to help you understand how to plan for a project. You will gain an appreciation of the range of planning techniques available and the situations in which it is appropriate to use them.

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)

Introduction to the application of elementary statistics to political analysis. A basic literacy subject, teaching the student how to read and interpret the quantitative literature in various subfields of political science and public policy. Students develop elementary statistical computation skills and learn to use a statistical computing package. From the course home page: This course provides students with a rigorous introduction to Statistics for Political Science. Topics include basic mathematical tools used in social science modeling and statistics, probability theory, theory of estimation and inference, and statistical methods, especially differences of means and regression. The course is often taken by students outside of political science, especially those in business, urban studies, and various fields of public policy, such as public health. Examples draw heavily from political science, but some problems come from other areas, such as labor economics.

Studies how randomization can be used to make algorithms simpler and more efficient via random sampling, random selection of witnesses, symmetry breaking, and Markov chains. Models of randomized computation. Data structures: hash tables, and skip lists. Graph algorithms: minimum spanning trees, shortest paths, and minimum cuts. Geometric algorithms: convex hulls, linear programming in fixed or arbitrary dimension. Approximate counting; parallel algorithms; online algorithms; derandomization techniques; and tools for probabilistic analysis of algorithms.

The three tasks in this set are not examples of tasks asking students to compute using the standard algorithms for multiplication and division because most people know what those kinds of problems look like. Instead, these tasks show what kinds of reasoning and estimation strategies students need to develop in order to support their algorithmic computations.

The three tasks (including part 1 and part 3) in this set are not examples of tasks asking students to compute using the standard algorithms for multiplication and division because most people know what those kinds of problems look like. Instead, these tasks show what kinds of reasoning and estimation strategies students need to develop in order to support their algorithmic computations.

This educators guide uses NASA's mission to Saturn (Cassini-Huygens) as real-world motivational context for learning fundamental, standards-based concepts in science. It includes six constructivist lessons, an FAQ, and a variety of connections to art, literature, and mythology.

6.780 covers statistical modeling and the control of semiconductor fabrication processes and plants. Topics include design of experiments, response surface modeling, and process optimization; defect and parametric yield modeling; process/device/circuit yield optimization; monitoring, diagnosis, and feedback control of equipment and processes; analysis and scheduling of semiconductor manufacturing operations.

Students participate in activities in which they develop number sense in and around the shopping mall. Two sets of activities, one developed for younger middle-grade students and one for older, deal with size and space, estimation, measurement, and applications involving percent.

In this adapted ZOOM video segment, cast members calculate how much water they each use during a typical shower. They compare their results to their original predictions.

In this activity, learners investigate the concepts of relative size and distance by creating a basic model of the solar system. Learners will work at one end of a hallway, large room or outdoor space. Learners will predict the distance of each planet from the Sun and then use toilet paper to measure the actual distances. This activity will help learners comprehend the immense scale of the solar system.

Experiments at Jefferson Lab will take weeks to months to complete. During this time scientists will collect millions of pieces of data. Once the scientists have the data, they begin to analyze the data using computers, looking for evidence to support or disprove their theories. To simulate the scientific data collection process, students will create the necessary data to calculate speed.