Analysis and Probability
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The Analysis, Probability and Stochastic Processes APSP interdisciplinary team considers the interface of two areas of mathematics: analysis and probability. The key areas of interest run across the intersection of common research areas of the Mathematical Analysis and Statistics research groups.
We are interested in nonlinear PDEs and PDEs with non-local or fractional operators and their stochastic representations, stochastic models for for fractional calculus, stochastic homogenisation for PDEs of the first and second order with random coefficients, large deviation theory, diffusion theory and continuous time random walk, fractality and mutifractality, limit theorems for functionals of spatio-temporal random fields and max-stable random fields under weak and strong dependence, spectral theory of random fields, point processes and random fields and extreme values of spatial temporal stochastic processes.
Our activities are focused on promotion and enhancement of collaborations between researchers from the Mathematical Analysis and Statistics research groups through organisation of seminars, workshops, and discussion groups.
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We aim to bring together researchers working in different areas of Mathematical Analysis, Probability and Stochastic Processes. Our researchers are working across disciplines to tackle major challenges facing society, the economy and physical sciences.
Our researchers are working across disciplines to tackle major challenges facing society, the economy and our environment. Skip to content. Search Search. Topics include fair and unfair allocations and how to measure variation about the mean.
Data analysis and probability
Various approaches for summarizing variation in data; how dividing data into groups can help provide other types of answers to statistical questions; numerical and graphic representations of the minimum, the maximum, the median, and quartiles; creating a box plot. More types of graphs and tables; analyzing and interpreting variation in data with stem and leaf plots and histograms; relative and cumulative frequency. Different ways to represent, analyze, and interpret data, including line plots, frequency tables, cumulative and relative frequency tables, and bar graphs; using intervals to describe variation in data; determining and understanding the median.
Defines statistics as a problem-solving process and examines its four components: asking questions, collecting appropriate data, analyzing the data, and interpreting the results. An investigation of the nature of data and potential sources of variation introduces the concepts of variables, bias, and random sampling. All Past Episodes Video Classroom Case Studies, Grades TV-G Grade teachers who have participated in the course show how they adapted the concepts developed in the workshops to their classrooms. Video Classroom Case Studies, Grades K-2 TV-G K-2 teachers who have participated in the course show how they adapted the concepts developed in the workshops to their classrooms.
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Video 9. Random Sampling and Estimation TV-G How to select a random sample and use it to estimate characteristics of an entire population; describing variation in estimates; the effect of sample size on an estimate's accuracy. Video 8. Probability TV-G Investigates some basic concepts of probability and the relationship between statistics and probability. Video 7.
Bivariate Data and Analysis TV-G Analyzing bivariate data; association and co-variation between two quantitative variables; scatter plots; the least squares line; modeling linear relationships. Video 6. Designing Experiments TV-G How to collect and compare data from observational and experimental studies and set up experiments.
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