The restless Universe introduces you to major achievements and figures in the history of physics, from Copernicus to Einstein and beyond. The route from classical to quantum physics will be laid out for you without recourse to challenging mathematics but with the fundamental features of theories and discoveries described in sufficient detail to whet your appetite for further physics study.
Students learn about probability through a LEGO® MINDSTORMS® NTX-based activity that simulates a game of "rock-paper-scissors." The LEGO robot mimics the outcome of random game scenarios in order to help students gain a better understanding of events that follow real-life random phenomenon, such as bridge failures, weather forecasts and automobile accidents. Students learn to connect keywords such as certainty, probable, unlikely and impossibility to real-world engineering applications.
Introduction to probability, statistical mechanics, and thermodynamics. Random variables, joint and conditional probability densities, and functions of a random variable. Concepts of macroscopic variables and thermodynamic equilibrium, fundamental assumption of statistical mechanics, microcanonical and canonical ensembles. First, second, and third laws of thermodynamics. Numerous examples illustrating a wide variety of physical phenomena such as magnetism, polyatomic gases, thermal radiation, electrons in solids, and noise in electronic devices. Concurrent enrollment in Quantum Physics I is recommended.
This course is an introduction to statistical data analysis. Topics are chosen from applied probability, sampling, estimation, hypothesis testing, linear regression, analysis of variance, categorical data analysis, and nonparametric statistics.
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.
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.
This course covers topics such as sums of independent random variables, central limit phenomena, infinitely divisible laws, Levy processes, Brownian motion, conditioning, and martingales.
Students conduct five experiments through stations to compare theoretical and experimental probability. Instruction begins with definitions, verbal and algebraic, of both theoretical and experimental probability, followed by experiments using coins and cards. The class data will be combined to compare with previously established theoretical probability.