This textbook is part of the OpenIntro Statistics series and offers complete coverage of the high school AP Statistics curriculum. Real data and plenty of inline examples and exercises make this an engaging and readable book. Links to lecture slides, video overviews, calculator tutorials, and video solutions to selected end of chapter exercises make this an ideal choice for any high school or Community College teacher. In fact, Portland Community College recently adopted this textbook for its Introductory Statistics course, and it estimates that this will save their students $250,000 per year. Find out more at: openintro.org/ahss

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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.

Measuring the dimensions of nano-circuits requires an expensive, high-resolution microscope with integrated video camera and a computer with sophisticated imaging software, but in this activity, students measure nano-circuits using a typical classroom computer and (the free-to-download) GeoGebra geometry software. Inserting (provided) circuit pictures from a high-resolution microscope as backgrounds in GeoGebra's graphing window, students use the application's tools to measure lengths and widths of circuit elements. To simplify the conversion from the on-screen units to the real circuits' units and the manipulation of the pictures, a GeoGebra measuring interface is provided. Students export their data from GeoGebra to Microsoft® Excel® for graphing and analysis. They test the statistical significance of the difference in circuit dimensions, as well as obtain a correlation between average changes in original vs. printed circuits' widths. This activity and its associated lesson are suitable for use during the last six weeks of the AP Statistics course; see the topics and timing note below for details.

This course is an arithmetic course intended for college students, covering whole numbers, fractions, decimals, percents, ratios and proportions, geometry, measurement, statistics, and integers using an integrated geometry and statistics approach. The course uses the late integers model—integers are only introduced at the end of the course.

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.

Beginning econometrics students often have an uneven preparation in statistics. The simulation gives students a clearer understanding of the behavior of OLS estimators.

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.

This resource consists of a Java applet and expository text. The applet is a simulation of the birthday experiment: a sample of size n is chose at random and with replacement from the first m positive integers. The random variable of interest is the number of distinct sample values. The event of interest is that all sample values are distinct.

This resource consists of a Java applet and expository text. The applet simulates Buffon's coin experiment. The radius of the coin can be varied. The applet illustrates a random experiment, the sample space, random variables, events, probability, and relative frequency.

This resource consists of a Java applet and expository text. The applet simulates Buffon's needle experiment and the corresponding approximation of pi. The event of interest is that the needle crosses a crack. The length of the needle can be varied. The applet illustrates a random experiment, the sample space, random variables, probability, and relative frequency.

This resource consists of a Java applet and expository text. The applet is a simulation of drawing n cards from a standard deck. The parameter n can be varied.

This resource consists of a Java applet and expository text. The applet simulates the chuck-a-luck experiment of rolling 3 fair dice. The random variable of interest is the net profit of the player.

This resource consists of a Java applet and expository text. The applet is a simulation of the experiment that consists of tossing a coin and then rolling either a red die or a green die, depending on the outcome of the coin toss. The probability of heads and the distributions of the two dice can be specified. The applet illustrates a two-stage experiment.

The Coke vs. Pepsi Taste Test Challenge has students design and carry out an experiment to determine whether or not students are able to correctly identify two brands of cola in a blind taste test. In the first stage of the activity students design and conduct the experiment. In the second part of the activity students use Sampling SIM software (freely downloadable from http://www.tc.umn.edu/~delma001/stat_tools/) to simulate and gather information on what would be expected under chance conditions (i.e., if students obtained correct answers only by guessing). The students then compare the observed results to the chance results and make an inference about whether a given student can in fact correctly identify Coke and Pepsi in a blind taste test. Finally, the experiment is critiqued in terms of how well it met the standards for a good experiment. This activity allows students to gain a better understanding of the experimental process and causality through considering control, random assignment, and possible confounding variables. The activity also allows students to begin to understand the process of hypothesis testing by comparing their observed results of the taste test to the results obtained through Sampling SIM (which model would be obtained by chance). Students make an inference about whether particular students in their class can truly tell the difference between Coke and Pepsi by reasoning about how surprising the observed results are compared to the simulated distribution of correct identifications by guessing. The activity also provides an opportunity for discussing generalizability to a population.

In this model eliciting activity (MEA), students are hired by a travel magazine to determine if two airlines that fly into Chicago are equally reliable. They examine data of flight arrival delay times for both airlines flying out of the same city. They first identify measures that can be used to compare the two airlines. Working in small groups, the students decide the size of a meaningful difference between the airlines for each measure and use that information to determine a rule that for deciding if one airline is more reliable than another. The students apply their rule to flight arrival delay data for the two airlines from four additional departure cities, and use the results to write a report to the magazine editor on whether or not one airline is more reliable than the other. This activity can serve as an introduction to ideas of central tendency and variability, and prepares students for formal approaches to comparing groups.

This resource consists of a Java applet and expository text. The applet is a simulation of the coupon collector problem, which consists of sampling from the first m positive integers, with replacement, until k distinct values are obtained. The parameters m and k can be varied. The random variable of interest is the size of the sample.

This resource consists of a Java applet and expository text. The applet is a simulation of the standard casino craps game. The random variables of interest are the winning under various bets.

This activity asks students to work in a team to develop a set of rules that can be used to program a SPAM filter for a client. The rules are based on characteristics of the subject lines of emails. Students are given samples of SPAM and non-SPAM subject lines to examine. After their rules are ready, they are given a test set of data to use and are asked to come up with a numerical measure to quantify how well their method (model) works. Each team writes a report describing how their model works and how well it performed on the test data. This activity could serve as an introduction to ideas of classification. Alternatively, the activity could be the basis for student introduction to types of statistical errors.

With your mouse, drag data points and their error bars, and watch the best-fit polynomial curve update instantly. You choose the type of fit: linear, quadratic, cubic, or quartic. The reduced chi-square statistic shows you when the fit is good. Or you can try to find the best fit by manually adjusting fit parameters.