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  • Using a parameter it's possible to represent a property of an entire population with a single number instead of millions of individual data points. There are a number of possible parameters to choose from such as the median, mode, or interquartile range. Each is calculated in a different manner and illuminates the data from a different point of view. The mean is one of the most useful and widely used and helps us understand populations. A population is simulated by generating 10,000 floating point random numbers between 0 and 10. Sample means are displayed in histograms and analyzed.
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  • Pseudo random number generators (PRNG) start with a seed value and will eventually repeat all the numbers they generate in exactly the same order. Putting in the same seed value will give precisely the same set of random numbers. On large scale Monte Carlo simulations (depends on generation of multiple random numbers), care has to be taken to make sure that the PRNG cycle is significantly longer than the quantity of random numbers needed or the pattern in the PRNG cycle can show up as an error producing pattern in the simulation results.
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  • This page provides an example of pseudo random number generators (PRNG) creating spread spectrum broadcasts and signals for encryption and decryption of wireless transmissions.
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  • This lesson plan uses the Birthday Paradox to introduce basic concepts of probability. Students run a Monte Carlo simulation using the TI-83 graphing calculator to generate random dates, and then search for matching pairs. Students also perform a graphical analysis of the birthday-problem function. Key Words: Permutations; Explicit Function; Recursive Function; Modeling.
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  • An independent, nonpartisan resource on trends in American public opinion. Gives examples of recent polls, margins of error, questions asked, and sample sizes.
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  • This Flash applet provides an introduction to simple linear regression for introductory statistics students. It combines a brief narrated animation with an interactive scatterplot function. Students are able to place points on the scatterplot by clicking with a mouse or typing X-Y coordinates. Students use these points to learn about the best fit line by placing a guess on the plot and comparing it with the least squares line. Students also learn about the value of the correlation coefficent and points that would be considered outliers. Students may also specify a value of x (within the range of the data) and obtain the resulting predicted value.
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  • This is a virtual spinner applet, which allows you to change spinner regions and the number of spins. It records the results and displays the data in a histogram.
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  • This is a virtual applet, which models repeaded coin tossing by a random number generator. It allows you to change the number of tosses as well as runs and records your results.
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  • This virtual applet simulates randomly drawing numbers from a box. You can choose which numbers you would like to choose from and the number of draws. The applet has the option to show theoretical probability and displays the results in histogram form.
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  • A "12 page" tutorial that explores the liner models via excel spreadsheets. The learning module leads the user through various aspects of linear modeling. This tutorial includes a worksheet that allows students to vary the scatter (or noise) level, by adjusting the scroll bar or by clicking on the arrows, to see how the slope and intercept of line respond to the addition of scatter to the data, while monitoring the value of r^2.

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