Collaborative Statistics was written by Barbara Illowsky and Susan Dean, faculty members at De Anza College in Cupertino, California. The textbook was developed over several years and has been used in regular and honors-level classroom settings and in distance learning classes. This textbook is intended for introductory statistics courses being taken by students at two– and four–year colleges who are majoring in fields other than math or engineering. Intermediate algebra is the only prerequisite. The book focuses on applications of statistical knowledge rather than the theory behind it. The textbook is also available in printed form from Qoop.com.

Collaborative Statistics was written by Barbara Illowsky and Susan Dean, faculty members at De Anza College in Cupertino, California. The textbook was developed over several years and has been used in regular and honors-level classroom settings and in distance learning classes. This textbook is intended for introductory statistics courses being taken by students at two– and four–year colleges who are majoring in fields other than math or engineering. Intermediate algebra is the only prerequisite. The book focuses on applications of statistical knowledge rather than the theory behind it. This custom textbook collection has been modified by R. Bloom for her classes at De Anza College; the homework content for the custom collection is now contained in a separate homework collection.

This is a custom collection (by R. Bloom) of homework and review problems to accompany Collaborative Statistics textbook custom collection by R. Bloom. Content is derived from Collaborative Statistics written by Barbara Illowsky and Susan Dean, faculty members at De Anza College in Cupertino, California. The textbook by S. Dean and B. Illowsky was developed over several years and has been used in regular and honors-level classroom settings and in distance learning classes. This textbook is intended for introductory statistics courses being taken by students at two– and four–year colleges who are majoring in fields other than math or engineering. Intermediate algebra is the only prerequisite. The book focuses on applications of statistical knowledge rather than the theory behind it. This custom version of their collection has been modified by R. Bloom for her classes at De Anza College.

Introduces students to the basic tools in using data to make informed management decisions. Covers introductory probability, decision analysis, basic statistics, regression, simulation, and linear and nonlinear optimization. Computer spreadsheet exercises and examples drawn from marketing, finance, operations management, and other management functions. Restricted to Sloan Fellows.

This module introduces the concept of modeling data using regression lines on a calculator.

Econometrics is the study of estimation and inference for economic models using economic data. Econometric theory concerns the study and development of tools and methods for applied econometric applications. Applied econometrics concerns the application of these tools to economic data.

In this class, students use data and systems knowledge to build models of complex socio-technical systems for improved system design and decision-making. Students will enhance their model-building skills, through review and extension of functions of random variables, Poisson processes, and Markov processes; move from applied probability to statistics via Chi-squared t and f tests, derived as functions of random variables; and review classical statistics, hypothesis tests, regression, correlation and causation, simple data mining techniques, and Bayesian vs. classical statistics. A class project is required.

The course focuses on the problem of supervised learning within the framework of Statistical Learning Theory. It starts with a review of classical statistical techniques, including Regularization Theory in RKHS for multivariate function approximation from sparse data. Next, VC theory is discussed in detail and used to justify classification and regression techniques such as Regularization Networks and Support Vector Machines. Selected topics such as boosting, feature selection and multiclass classification will complete the theory part of the course. During the course we will examine applications of several learning techniques in areas such as computer vision, computer graphics, database search and time-series analysis and prediction. We will briefly discuss implications of learning theories for how the brain may learn from experience, focusing on the neurobiology of object recognition. We plan to emphasize hands-on applications and exercises, paralleling the rapidly increasing practical uses of the techniques described in the subject.

This course examines how numerical methods are used by engineers to translate the language of mathematics and physics into information that may be used to make engineering decisions. Often, this translation is implemented so that calculations may be done by machines (computers). Upon successful completion of this course, the student will be able to: Quantify absolute and relative errors; Distinguish between round-off and truncation errors; Interconvert binary and base-10 number representations; Define and use floating-point representations; Quantify how errors propagate through arithmetic operations; Derive difference equations for first and second order derivatives; Evaluate first and second order derivatives from numerical evaluations of continuous functions or table lookup of discrete data; Describe situations in which numerical solutions to nonlinear equations are needed; Implement the bisection method for solving equations; List advantages and disadvantages of the bisection method; Implement both Newton-Raphson and secant methods; Describe the difference between Newton-Raphson and secant methods; Demonstrate the relative performance of bisection, Newton-Raphson, and secant methods; Define and identify special types of matrices; Perform basic matrix operations; Define and perform Gaussian elimination to solve a linear system; Identify pitfalls of Gaussian elimination; Define and perform Gauss-Seidel method for solving a linear system; Use LU decomposition to find the inverse of a matrix; Define and perform singular value decomposition; explain the significance of singular value decomposition; Define interpolation; Define and use direct interpolation to approximate data and find derivatives; Define and use NewtonŐs divided difference method of interpolation; Define and use Lagrange and spline interpolation; Define regression; Perform linear least-squares regression and nonlinear regression; Derive and apply the trapezoidal rule and Simpson's rule of integration; Distinguish Simpson's method from the trapezoidal rule; Estimate errors in trapezoidal and Simpson integration; Derive and apply Romberg and Gaussian quadrature for integration; Define and distinguish between ordinary and partial differential equations; Implement Euler's methods for solving ordinary differential equations; Investigate how step size affects accuracy in Euler's method; Implement and use the Runge-Kutta 2nd order method for solving ordinary differential equations; Apply the shooting method to solve boundary-value problems; Define Fourier series and the Fourier transform; Find Fourier coefficients for a given data set or function and domain; Describe the finite element method for one-dimensional problems. (Mechanical Engineering 205)

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. Elementary statistics, probability, and other types of quantitative reasoning useful for description, estimation, comparison, and explanation are covered. Emphasis is on the use and limitations of analytical techniques in planning practice."

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.

Focus on multivariate data analysis procedures, emphasizing regression. Considers model specification, autocorrelation, instrumental variables, and causal modelling. Students must have taken at least one previous subject in statistics. Open to qualified undergraduates. Course home page description: This course is the second semester in the statistics sequence for political science and public policy offered in the Political Science Department at MIT. The intellectual thrust of the course is a presentation of statistical models for estimating causal effects of variables. The model of an effect is a conditional mean (though we might imagine other effect). The notion of causality is the effect of one variable on another holding all else constant.

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.

This subject is on regional energy-environmental modeling rather than on general energy-environmental policies, but the models should have some policy relevance. We will start with some discussion of green accounting issues; then, we will cover a variety of theoretical and empirical topics related to spatial energy demand and supply, energy forecasts, national and regional energy prices, and environmental implications of regional energy consumption and production. Where feasible, the topics will have a spatial dimension. This is a new seminar, so we expect students to contribute material to the set of readings and topics covered during the semester.

This applet from Statistical Java allows the user to generate bivariate data for analysis with simple linear regression. The page describes the equations used to generate the data and estimate the regression lines.

This site provides visual resources and supporting material about the study of sequence stratigraphy. Resources accessible from this site include informational text, images, animations and short videos which can be integrated into lectures, labs or other activities.

Students will use a TI82 or TI83 calculator to construct a scatterplot, find the equation of the least-squares regression line for a set of data, find the coefficient of determination, and make predictions by using the line.

As teachers of statistics, we know that residual plots and other diagnostics are important to deciding whether or not linear regression is appropriate for a set of data. Despite talking with our students about this, many students might believe that if the correlation coefficient is strong enough, these diagnostic checks are not important. The data set included in this activity was created to lure students into a situation that looks on the surface to be appropriate for the use of linear regression but is instead based (loosely) on a quadratic function.