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A search of MERLOT materialsCopyright 1997-2016 MERLOT. All rights reserved.Fri, 21 Oct 2016 11:07:18 PDTFri, 21 Oct 2016 11:07:18 PDTMERLOT Search - materialType=Simulation&category=2595&sort.property=dateCreatedhttps://www.merlot.org/merlot/images/merlot.gif
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4434Power Curve Applets
https://www.merlot.org/merlot/viewMaterial.htm?id=1203882
These four applets demonstrate effects on the power curve for the difference in means, the difference in proportions, the mean change, and the correlation coefficient. Each applet allows users to manipulate different parameters to see the effects on power. This is a great collection of statistical calculators, perfect for either students or instructors.Wed, 27 Jul 2016 18:30:37 -0700Distribution of the Sample Sum, Sample Mean, Sample Variance, and Sample Chi-Squared
https://www.merlot.org/merlot/viewMaterial.htm?id=1203880
This applet, created by Philip B. Stark of the University of California, Berkeley, illustrates this concept of the sampling distribution of a statistic by simulating the sampling distribution of the sample sum, mean, variance, and chi-square. Users can choose from a normal or uniform population or can supply their own data. Relevant statistics from each sample are calculated.Wed, 27 Jul 2016 18:30:35 -0700DIG Stats: Insulation Activity
https://www.merlot.org/merlot/viewMaterial.htm?id=1203877
In this activity, students will generate scatter plots and use regression and logarithms to explore a dataset with time and temperature data for an insulation pack. Questions about the exercise are given at the bottom of the page as well as links to instructions for Excel and the TI-83 calculator. The data exists in TI-83 group, Excel, and text formats.Wed, 27 Jul 2016 18:30:33 -0700Finding One Variable Statistics With a Graphing Calculator
https://www.merlot.org/merlot/viewMaterial.htm?id=1203875
This quick YouTube video from high school statistics teacher Roger W. Davis explains how to find one variable statistics using the TI-84 graphing calculator. The demonstration goes through three steps: entering the data, finding one variable statistics using the STAT menu, and interpreting the results. The data created includes mean, sum, median and more. Flash player is required to view this video, and the running time for the clip is 3:12.Wed, 27 Jul 2016 18:30:32 -0700[Shiny App] Hierarchical Models
https://www.merlot.org/merlot/viewMaterial.htm?id=1142729
Hierarchical models are used when there is nesting of observational units in the data and variables are observed on multiple levels of the hierarchy. Failure to account for the hierarchy in the data may result in invalid conclusions. However, hierarchical models are not always needed for nested data as the intraclass correlation coefficient determines the requirement. This app focuses on illustrating the concept of hierarchical models by comparing the method to the two others at the extremes: the pooled and unpooled methods. Users are shown mathematically and visually how the hierarchical estimates are weighted averages and how they serve as a balance between the pooled and unpooled estimates; the two related ideas of shrinkage and borrowing strength are illustrated in this process.Users have the capability to either use sample data sets or upload their own data to learn about hierarchical models through case studies. The three different scenarios for learning are varying-intercept, varying-intercept and varying-slope, and varying-intercept and varying-slope with level 2 predictor. In each scenario, users are first presented outputs and graphs of the pooled and unpooled method. Then they proceed to the hierarchical model and different concepts of this method are explained in compartments. Interpretations are included throughout the outputs for users to comprehend the ideas. Additionally, each scenario contains a comparison of the three modelling methods with visualizations. For those who are familiar with Bayesian methods, a tab is available to run a Bayesian hierarchical model. After grasping the concept of hierarchical models, users can analyze their own data with their own specified model.Wed, 23 Mar 2016 11:53:27 -0700[Shiny App] Heaped Distribution Estimation
https://www.merlot.org/merlot/viewMaterial.htm?id=1142718
Data often exhibit a heaped distribution in situations when there are either rounding or recall issues. Then, heaping is observed in the distribution when there are unusual spikes at certain values. In this app, the focus is heaping present at multiples of 5. Two rounding behaviors are assumed and they are accounted for in the form of two rounding probabilities. The first rounding probability describes the tendency to round with smaller values, while the second rounding probability describes the tendency to round with larger values. Therefore, a mixture model is constructed with a specified distribution and the two rounding probabilities. Throughout the app, interpretations in popovers are provided for users to understand the different stages of the demonstration.Users have the option to either simulate data or upload data to begin the app. There are five distributions for users to choose and the parameters can be adjusted. The proceeding tab describes the rounding process to users; the actual and rounded/heaped distributions are visually displayed for users to compare. With the heaped distribution, the goal for users is to estimate the actual distribution with maximum likelihood. After obtaining the estimates, confidence intervals can be produced either based on the inverse Fisher information matrix or bootstrapping. For users to validate the method, a simulation study can be performed in the last tab of the app. They can compare the means of the MLE distributions to the specified underlying parameters.Wed, 23 Mar 2016 11:51:30 -0700[Shiny App] Sampling Distributions of Various Statistics
https://www.merlot.org/merlot/viewMaterial.htm?id=1142715
This app allows the user to draw repeated samples from a specified population shape (normal, left-skewed, right-skewed, uniform, or bimodal). The user also specifies a statistic from the pull-down menu in the left panel. When a sample is generated by pressing the "Draw samples" button, a histogram of that sample is plotted in the graph at left, and the sample statistic is added to the sampling distribution histogram at right. The total number of samples is tracked at the bottom of the page, and the user may also elect to display the mean and standard deviation of the sampling distribution by checking the box. Above these two graphs, the user may also click to display the population curve and parameter of interest.Wed, 23 Mar 2016 11:49:01 -0700[Shiny App] Random Variable Generation
https://www.merlot.org/merlot/viewMaterial.htm?id=1142702
The Probability Integral Transform and the Accept-Reject Algorithm are two methods for generating a random variable with some desired distribution. This Shiny app demonstrates how they work, through two examples of each method.For the Accept-Reject Algorithm (shown above), the examples demonstrated in this app are the Beta distribution and the truncated Normal distribution. A side-by-side plot shows each point that has been generated. Users have the option to generate one replicate at a time, to examine and understand the mechanics of how the algorithm is accomplishing its task, with details of each replicate given below the plots. Additionally, up to 500 replicates can be generated at once, to build towards a greater representation of points and confirm that the algorithm does in fact result in the desired distribution.The Probability Integral Transform (not shown) is demonstrated with the Exponential distribution, and an arbitrary, unnamed distribution. In this demonstration, users again have the option to generate one replicate at a time, with side-by-side plots showing each point, and details of each replicate given below the plots. Users can also generate up to 500 replicates at once to view the overall distribution that is produced.Wed, 23 Mar 2016 11:46:29 -0700[Shiny App] Gambler's Ruin
https://www.merlot.org/merlot/viewMaterial.htm?id=1142678
The Gambler’s Ruin is a well-known problem that can be used to illustrate a variety of probability concepts.Two players are playing a game against each other, betting the same amount on each turn (here, we use $1). On each turn of the game, Player A has a fixed probability p of winning $1 from Player B, where 0<p<1. The probability that Player B will win $1 from Player A is 1-p. Player A and Player B each start with some initial fortune (which may or may not be equal to each other), and the game continues until one player has all of the money.The Gambler’s Ruin problem is useful for teaching conditional probability, Markov chains, and for simply visualizing a stochastic process. This app shows a graphical representation of one iteration of the Gambler’s Ruin, and also can simulate many runs under a variety of settings that may be manipulated, to obtain simulated estimates of the average length of a game, and the probability that Player A will win under those settings. In a mathematical statistics class, the simulated estimates from this app could be used to corroborate analytic solutions.Wed, 23 Mar 2016 11:38:42 -0700[Shiny App] Longest Run of Heads or Tails
https://www.merlot.org/merlot/viewMaterial.htm?id=1142667
One popular class activity to help students understand chance behavior is to observe the runs of consecutive heads or tails in a sequence of coin flips. When asked to write down a simulated sequence of 100 tosses of a fair coin, most students are hesitant to create runs of heads or tails exceeding 4. Students are often surprised to find that the longest run of heads or tails turns out to be much higher based on 100 tosses of an actual coin.This Shiny app allows the user to simulate the outcomes of a fair coin flipped n times (n = 10, 20, ..., 400). In an accompanying plot of outcomes any runs of at least a specified length are marked in color, and the length of the longest run is displayed. The user can easily re-randomize the sequence of coin flips and quickly get a sense of typical longest run values. From the plot students may also be quite surprised to see how many long runs occur in the sequence.The user may choose to display the predicted approximate length of the longest run and an approximate 95% prediction interval for the length of the longest run. Details on these two estimators can be found in Schilling (1990). See Schilling (2012) for a more recent and related article.Wed, 23 Mar 2016 11:36:17 -0700