Developed for fifth grade and above. Primary biological content area covered:; Plant growth; Seedling morphology; Hypothesis testing; Experimental design; Line graphing; Introductory statistics.

Biology In Elementary Schools is a Saint Michael's College student project. The teaching ideas on this page have been found, refined, and developed by students in a college-level course on the teaching of biology at the elementary level. Unless otherwise noted, the lesson plans have been tried at least once by students from our partner schools. This wiki has been established to share ideas about teaching biology in elementary schools. The motivation behind the creation of this page is twofold: 1. to provide an outlet for the teaching ideas of a group of college educators participating in a workshop-style course; 2. to provide a space where anyone else interested in this topic can place their ideas.

This unit is the second in the MSXR209 series of five units on mathematical modelling. In this unit you are asked to relate the stages of the mathematical modelling process to a previously formulated mathematical model. This example, that of skid mark produced by vehicle tyres, is typical of accounts of modelling that you may see in books, or produced in the workplace. The aim of this unit is to help you to draw out and to clarify mathematical modelling ideas by considering the example. It assumes that you have studied Modelling pollution in the Great Lakes (MSXR209_1).

Contains link to homework or review problems for R. Bloom custom collection of Collaborative Statistics by S. Dean and B. Illowsky

Students compare real-time Earth and Mars measurements for temperature, wind speed, humidity and atmospheric pressure by accessing Internet-data resources from NASA.

This course provides a broad introduction to machine learning and statistical pattern recognition. Topics include: supervised learning (generative/discriminative learning, parametric/non-parametric learning, neural networks, support vector machines); unsupervised learning (clustering, dimensionality reduction, kernel methods); learning theory (bias/variance tradeoffs; VC theory; large margins); reinforcement learning and adaptive control.
The course will also discuss recent applications of machine learning, such as to robotic control, data mining, autonomous navigation, bioinformatics, speech recognition, and text and web data processing.

This course is designed to introduce students to the fundamental concepts and ideas in natural language processing (NLP), and to get them up to speed with current research in the area. It develops an in-depth understanding of both the algorithms available for the processing of linguistic information and the underlying computational properties of natural languages. Wordlevel, syntactic, and semantic processing from both a linguistic and an algorithmic perspective are considered. The focus is on modern quantitative techniques in NLP: using large corpora, statistical models for acquisition, disambiguation, and parsing. Also, it examines and constructs representative systems.

These modules capture the broad array of activities conducted by vertically integrated research groups comprised of member of the Mathematics, Statistics, and Computational and Applied Mathematics departments at Rice University and supported by a VIGRE (Vertically Integrated Grants for Research and Education in the Mathematical Sciences) award from the National Science Foundation. VIGRE working groups are known as PFUGs. Pronounced like "fugue," the acronym derives from the groups' composition of Postdocs, Faculty, Undergraduates and Graduate students. The name derives from the musical meaning for fugue, "an idea that is introduced by one voice and developed by others."

The Art of the Probable" addresses the history of scientific ideas, in particular the emergence and development of mathematical probability. But it is neither meant to be a history of the exact sciences per se nor an annex to, say, the Course 6 curriculum in probability and statistics. Rather, our objective is to focus on the formal, thematic, and rhetorical features that imaginative literature shares with texts in the history of probability. These shared issues include (but are not limited to): the attempt to quantify or otherwise explain the presence of chance, risk, and contingency in everyday life; the deduction of causes for phenomena that are knowable only in their effects; and, above all, the question of what it means to think and act rationally in an uncertain world. Our course therefore aims to broaden students’ appreciation for and understanding of how literature interacts with--both reflecting upon and contributing to--the scientific understanding of the world. We are just as centrally committed to encouraging students to regard imaginative literature as a unique contribution to knowledge in its own right, and to see literary works of art as objects that demand and richly repay close critical analysis. It is our hope that the course will serve students well if they elect to pursue further work in Literature or other discipline in SHASS, and also enrich or complement their understanding of probability and statistics in other scientific and engineering subjects they elect to take.

Ask Dr. Math is a question and answer service for math students and their teachers. A searchable archive is available by level and topic, as well as summaries of Frequently Asked Questions (the Dr. Math FAQ)

In this video segment from TV411, figure skaters compute their average daily practice time.

Developed for third and fourth grade. Students will understand how their lungs work. They will understand what lung capacity is and be able to measure their own. They will test for a relationship between their lung capacity and their height.

Biology In Elementary Schools is a Saint Michael's College student project. The teaching ideas on this page have been found, refined, and developed by students in a college-level course on the teaching of biology at the elementary level. Unless otherwise noted, the lesson plans have been tried at least once by students from our partner schools. This wiki has been established to share ideas about teaching biology in elementary schools. The motivation behind the creation of this page is twofold: 1. to provide an outlet for the teaching ideas of a group of college educators participating in a workshop-style course; 2. to provide a space where anyone else interested in this topic can place their ideas.

This resource consists of a Java applet and expository text. The applet is a simulation of the ballot experiment: The votes in an election are randomly counted. The event of interest is that the winning candidate is always ahead in the vote count.

Introduction to bar charts.

Students will learn the concepts behind fractions, decimals, and percents by using sports statistics found on baseball cards.

A lesson plan for grade 8 Mathematics.

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.

David McCandless turns complex data sets (like worldwide military spending, media buzz, Facebook status updates) into beautiful, simple diagrams that tease out unseen patterns and connections. Good design, he suggests, is the best way to navigate information glut -- and it may just change the way we see the world. A quiz, thought provoking question, and links for further study are provided to create a lesson around the 18-minute video. Educators may use the platform to easily "Flip" or create their own lesson for use with their students of any age or level.

This resource consists of a Java applet and expository text. The applet is a simulation of Bertrand's experiment: a random chord on a circle The event of interest is whether the length of the chord is larger than the length of the inscribed equilateral triangle. Three models for generating the random chord can be used.

Covers the basics of R software and the key capabilities of the Bioconductor project (a widely used open source and open development software project for the analysis and comprehension of data arising from high-throughput experimentation in genomics and molecular biology and rooted in the open source statistical computing environment R), including importation and preprocessing of high-throughput data from microarrays and other platforms. Also introduces statistical concepts and tools necessary to interpret and critically evaluate the bioinformatics and computational biology literature. Includes an overview of of preprocessing and normalization, statistical inference, multiple comparison corrections, Bayesian Inference in the context of multiple comparisons, clustering, and classification/machine learning.

This text manual introduces statistical analysis and its underlying philosophy, enabling students to understand how to describe the confidence they have in their analysis.

Statistical analysis is one of the most widely used, and abused, techniques in the biological sciences. Statistics are ostensibly used to allow an investigator to be objective. That is, the researcher uses statistical tests to determine whether or not his/her hypothesis is supported by the data collected.

Unfortunately, the choice of the particular statistical test is often not objective and the underlying limitations of individual tests are often ignored or unknown by the researcher. Yet statistical analysis, when appropriately applied, allows scientists to examine the probability that their hypotheses are or are not supported by the data collected.