Students work as physicists to understand centripetal acceleration concepts. They also learn about a good robot design and the accelerometer sensor. They also learn about the relationship between centripetal acceleration and centripetal force governed by the radius between the motor and accelerometer and the amount of mass at the end of the robot's arm. Students graph and analyze data collected from an accelerometer, and learn to design robots with proper weight distribution across the robot for their robotic arms. Upon using a data logging program, they view their own data collected during the activity. By activity end , students understand how a change in radius or mass can affect the data obtained from the accelerometer through the plots generated from the data logging program. More specifically, students learn about the accuracy and precision of the accelerometer measurements from numerous trials.

Students prepare for the associated activity in which they investigate acceleration by collecting acceleration vs. time data using the accelerometer of a sliding Android device. Based on the experimental set-up for the activity, students form hypotheses about the acceleration of the device. Students will investigate how the force on the device changes according to Newton's Second Law. Different types of acceleration, including average, instantaneous and constant acceleration, are introduced. Acceleration and force is described mathematically and in terms of processes and applications.

Students investigate the motion of a simple pendulum through direct observation and data collection using Android® devices. First, student groups create pendulums that hang from the classroom ceiling, using Android smartphones or tablets as the bobs, taking advantage of their built-in accelerometers. With the Android devices loaded with the (provided) AccelDataCapture app, groups explore the periodic motion of the pendulums, changing variables (amplitude, mass, length) to see what happens, by visual observation and via the app-generated graphs. Then teams conduct formal experiments to alter one variable while keeping all other parameters constant, performing numerous trials, identifying independent/dependent variables, collecting data and using the simple pendulum equation. Through these experiments, students investigate how pendulums move and the changing forces they experience, better understanding the relationship between a pendulum's motion and its amplitude, length and mass. They analyze the data, either on paper or by importing into a spreadsheet application. As an extension, students may also develop their own algorithms in a provided App Inventor framework in order to automatically note the time of each period.

In this simulation of a doctor's office, students play the roles of physician, nurse, patients, and time-keeper, with the objective to improve the patient waiting time. They collect and graph data as part of their analysis. This serves as a hands-on example of using engineering principles and engineering design approaches (such as models and simulations) to research, analyze, test and improve processes.

Students use their senses to describe what the weather is doing and predict what it might do next. After gaining a basic understanding of weather patterns, students act as state park engineers and design/build "backyard weather stations" to gather data to make actual weather forecasts.

Students investigate different balls' abilities to bounce and represent the data they collect graphically.

Students use ultrasonic sensors and LEGO© MINDSTORMS© NXT robots to emulate how bats use echolocation to detect obstacles. They measure the robot's reaction times as it senses objects at two distances and with different sensor threshold values, and again after making adjustments to optimize its effectiveness. Like engineers, they gather and graph data to analyze a given design (from the tutorial) and make modifications to the sensor placement and/or threshold values in order to improve the robot's performance (iterative design). Students see how problem solving with biomimicry design is directly related to understanding and making observations of nature.

In this math activity, students conduct a strength test using modeling clay, creating their own stress vs. strain graphs, which they compare to typical steel and concrete graphs. They learn the difference between brittle and ductile materials and how understanding the strength of materials, especially steel and concrete, is important for engineers who design bridges and structures.

Students learn a simple technique for quantifying the amount of photosynthesis that occurs in a given period of time, using a common water plant (Elodea). They can use this technique to compare the amounts of photosynthesis that occur under conditions of low and high light levels. Before they begin the experiment, however, students must come up with a well-worded hypothesis to be tested. After running the experiment, students pool their data to get a large sample size, determine the measures of central tendency of the class data, and then graph and interpret the results.

The student will learn the mechanics of editing and compiling a simple program written in C++ beginning with a discussion of the essential elements of C++ programming: variables, loops, expressions, functions, and string class. Next, the student will cover the basics of object-oriented programming: classes, inheritance, templates, exceptions, and file manipulation. The student will then review function and class templates and the classes that perform output and input of characters to/from files. This course will also cover the topics of namespaces, exception handling, and preprocessor directives. In the last part of the course, the student will learn some slightly more sophisticated programming techniques that deal with data structures such as linked lists and binary trees. Upon successful completion of this course, students will be able to: Compile and execute code written in C++ language; Work with the elementary data types and conditional and iteration structures; Define and use functions, pointers, arrays, struct, unions, and enumerations; Write C++ using principles of object-oriented programming; Write templates and manipulate the files; Code and use namespaces, exceptions, and preprocessor instructions; Write a code that represents linked lists and binary trees; Translate simple word problems into C++ language. (Computer Science 107)

Students are introduced to the "Walk the Line" challenge question. They write journal responses to the question and brainstorm what information they need to answer the question. Ideas are shared with the class (or in pairs and then to the class, if class size is large). Then students read an interview with an engineer to gain a professional perspective on linear data sets and best-fit lines. Students brainstorm for additional ideas and add them to the list. With the teacher's guidance, students organize the ideas into logical categories of needed knowledge.

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 course covers the algorithmic and machine learning foundations of computational biology combining theory with practice. We cover both foundational topics in computational biology, and current research frontiers. We study fundamental techniques, recent advances in the field, and work directly with current large-scale biological datasets.

The issue of confounding is of central importance in any analytic epidemiological study (as well as in those descriptive studies aiming to compare different populations), especially in the case of observational studies. Confounding results from non-random differences between the groups of animals being compared in relation to a second, 'confounding' exposure which is independently associated with both the exposure of interest (although not a consequence of this) and the outcome of interest (although not an effect of this). This results in the effect of the exposure of interest is 'mixed up' with the effect of the confounding exposure, and therefore an incorrect estimate of the true association. As such, confounding is viewed by many authors as a form of bias - however, unlike forms of selection and information bias, it is a natural feature of the data (in the case of an observational study), and techniques are available to account for it during analysis.

The students discover the basics of heat transfer in this activity by constructing a constant pressure calorimeter to determine the heat of solution of potassium chloride in water. They first predict the amount of heat consumed by the reaction using analytical techniques. Then they calculate the specific heat of water using tabulated data, and use this information to predict the temperature change. Next, the students will design and build a calorimeter and then determine its specific heat. After determining the predicted heat lost to the device, students will test the heat of solution. The heat given off by the reaction can be calculated from the change in temperature of the water using an equation of heat transfer. They will compare this with the value they predicted with their calculations, and then finish by discussing the error and its sources, and identifying how to improve their design to minimize these errors.

Students learn about viscoelastic material behavior, such as strain rate dependence and creep, by using silly putty, an easy-to-make polymer material. They learn how to make silly putty, observe its behavior with different strain rates, and then measure the creep time of different formulations of silly putty. By seeing the viscoelastic behavior of silly putty, students start to gain an understanding of how biological materials function. Students gain experience in data collection, graph interpretation, and comparison of material properties to elucidate material behavior. It is recommended that students perform Part 1of the activity first (making and playing with silly putty), then receive the content and concept information in the associated lesson, and then complete Part 2 of the activity (experimenting and making measurements with silly putty).

This unit will help you to understand the forms of data that are handled by software and look at the various processes that can be applied to the data. These ideas are demonstrated through the use of a supermarket till and illustrate how simple data sets can be manipulated.

All epidemiological investigations require some form of data description. A number of methods are available for describing data, and the most appropriate one will depend upon both the type of data available and the aims of the investigation. If these issues are not considered, useful information may be lost, or more seriously, a misleading estimate may be made.

Epidemiological investigation requires a good understanding of different data types, as this will strongly influence data analysis and interpretation. Data can broadly be classified as qualitative and quantitative, and within each of these groups, data can be further categorised as shown below. Although different grouping systems are available, it is important to consider the type of data being dealt with prior to any analysis. If desired, data can often be changed into different types through manipulation (for example, the quantitative variable weight can be converted to qualitative variables such as low/medium/high or low/not low).

In a multi-week experiment, student teams gather biogas data from the mini-anaerobic digesters that they build to break down different types of food waste with microbes. Using plastic soda bottles for the mini-anaerobic digesters and gas measurement devices, they compare methane gas production from decomposing hot dogs, diced vs. whole. They monitor and measure the gas production, then graph and analyze the collected data. Students learn how anaerobic digestion can be used to biorecycle waste (food, poop or yard waste) into valuable resources (nutrients, biogas, energy).