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.

Data Analysis, Statistics, and Probability introduces statistics as a problem-solving process. In this course, you can build your skills through investigations of different ways to collect and represent data, and describe and analyze variation in data. Through practical examples, you will come to understand some statistical concepts, such as data representation, variation, the mean and median, bivariate data, probability, designing statistical experiments, and population estimations. The concluding case studies, divided into grade bands for K-2, 3-5, and 6-8 teachers, show you how to apply what you have learned in your own classroom.

Data Analysis

The applet in this section allows for simple data analysis of univariate data. Users can either generate normal or uniform data for k samples or copy and paste data from another source to a text box. A univariate analysis is performed for all k samples.

In Data Analysis: As Real World As It Gets, we feature resources for teaching about data and statistics as supported by the NCTM Standards (NCTM, 2000). Data collection and analysis can be an avenue into the meaningful mathematics and problem-solving skills needed by students in the twenty-first century. And an answer to the student question, Why do we have to study math? can be found when teaching mathematics with a real-world statistics approach.

A whirl-wind tour of the statistics used in behavioral science research, covering topics including: data visualization, building your own null-hypothesis distribution through permutation, useful parametric distributions, the generalized linear model, and model-based analyses more generally. Familiarity with MATLABA, Octave, or R will be useful, prior experience with statistics will be helpful but is not essential. This course is intended to be a ground-up sketch of a coherent, alternative perspective to the "null-hypothesis significance testing" method for behavioral research (but don't worry if you don't know what this means).

In NASA CONNECT Having a Solar Blast, NASA engineers and researchers use data analysis and measurement to predict solar storms, anticipate how they will affect the Earth, and improve our understanding of the Sun-Earth system. Grades 6-8.

Covers computational and data analysis techniques for environmental engineering applications. First third of subject introduces MATLAB and numerical modeling. Second third emphasizes probabilistic concepts used in data analysis. Final third provides experience with statistical methods for analyzing field and laboratory data. Numerical techniques such as Monte Carlo simulation are used to illustrate the effects of variability and sampling. Concepts are illustrated with environmental examples and data sets. This subject is a computer-oriented introduction to probability and data analysis. It is designed to give students the knowledge and practical experience they need to interpret lab and field data. Basic probability concepts are introduced at the outset because they provide a systematic way to describe uncertainty. They form the basis for the analysis of quantitative data in science and engineering. The MATLABĺ¨ programming language is used to perform virtual experiments and to analyze real-world data sets, many downloaded from the web. Programming applications include display and assessment of data sets, investigation of hypotheses, and identification of possible casual relationships between variables. This is the first semester that two courses, Computing and Data Analysis for Environmental Applications (1.017) and Uncertainty in Engineering (1.010), are being jointly offered and taught as a single course.

Using two different coins and recording the results of both coins helps students dispel this initial misconception as they analyze the graph results. Class discussion should focus on analyzing the data to determine if the game is fair or not. Directions and gameboard are included in the download.

These activities support students as they conceptually develop a sense of how probability affects the outcome of games. Students will find that applying their knowledge of probability will help them win some of the games

This course emphasizes the analysis of ethnographic and other forms of qualitative data in public health research. We introduce various interpretive analytic approaches, explore their use, and guide students in applying them to data. We also introduce the use of computer software for coding textual data (Atlas.ti). Students analyze data they have collected as part of fieldwork projects initiated in 410.690 and write up the results in a final paper. Classroom sessions include lectures, discussions, intensive group work related to the fieldwork projects, and instruction in the computer lab.

The objectives of this lesson are to understand the characteristics of the data that is collected using the Radio JOVE antenna/receiver system. Using calibrations for the equipment, one can determine a proper measure of the peak intensity of the output, identify the duration of the solar or Jovian radio activity, and calculate the approximate total power emitted by the source. Master these concepts by completing example problems.

Students will analyze quantitative data and interpret the results, learning about the relevance of education and family type to earnings, how it has changed over time and the relevance of race in understanding these relationships.

This is an example of using LabVIEW MathScript to solve a simple engineering data analysis problem in which velocity and acceleration are computed from altitude data from a home-built rocket test launch.

Once students realize that the outcomes of tossing one die are equally likely, they sometimes transfer that knowledge to tossing two dice. This is a common misconception that is best addressed through data collection and analyzing that data rather than through telling. See suggested two-dice games below that students can play to gain experiential knowledge of the results of tossing two dice. Several of the games encourage students to develop better strategies in order to win and their growing understanding of probability will be shaped by these experiences.

Students need many experiences tossing one die, collecting data and analyzing that data to construct meaning for the probability of the different outcomes. Since a small student sample is often skewed, it is necessary for teachers to help students collate class data to better approximate the theoretical probability that the outcomes of tossing a single die are equally likely. The one-die games below motivate data collection opportunities that students will play again and again while generating larger data samples for class discussion. Many teachers find a simple class tally chart an effective way to organize data for this ongoing experiment. Students simply add results to the class results as they complete a game. Class discussions focus on the fairness of the games and discussion about whether each player has an equal chance of winning.

Public Health C242C / Statistics C247C: This course covers the statistical issues surrounding estimation of effects using data on subjects followed through time. The course emphasizes a regression model approach and discusses disease incidence modeling and both continuous outcome data/linear models and longitudinal extensions to nonlinear models (e.g., logistic and Poisson). The primary focus is from the analysis side, but mathematical intuition behind the procedures will also be discussed. The statistical/mathematical material includes some survival analysis, linear models, logistic and Poisson regression, and matrix algebra for statistics. The course will conclude with an introduction to recently developed causal regression techniques (e.g., marginal structural models). Time permitting, serially correlated data on ecological units will also be discussed. Also listed as Statistics C247C.

Commit one of the Seven Deadly Sins of Data Analysis and you run a significant risk of missing AYP under No Child Left Behind (NCLB).

The purpose of this assignment is to apply what you have learned in this course regarding the consequences of marginalization to an analysis of actual Census data for the United States in the year 2000. For this assignment, we will explore the impact of racial affiliation and sex on social class, as represented by socio-economic status (SES): level of education, occupation and income.