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A search of MERLOT materialsCopyright 1997-2016 MERLOT. All rights reserved.Fri, 6 May 2016 18:14:23 PDTFri, 6 May 2016 18:14:23 PDTMERLOT Search - materialType=Simulation&createdSince=2012-12-03&sort.property=dateCreatedhttps://www.merlot.org/merlot/images/merlot.gif
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4434Under Pressure - Boiling Water
https://www.merlot.org/merlot/viewMaterial.htm?id=1158051
Change pressure, adjust the temperature and measure the boiling point at various pressures. Contains interactive animations and sound. Developed on App Inventor 2. App inventor is available here : https://drive.google.com/open?id=0B5oVCzPFxKyGTkhLMEVwUDdSUGcThu, 28 Apr 2016 12:53:56 -0700Multimedia learning object for understanding gene structure
https://www.merlot.org/merlot/viewMaterial.htm?id=1156987
eLearning tool for mastering gene-structure function relationships and information flow from DNA to RNAMon, 25 Apr 2016 14:45:53 -0700Multimedia learning object for mastering post-translational modifications
https://www.merlot.org/merlot/viewMaterial.htm?id=1156983
eLearning tool for mastering post-translational modificationsMon, 25 Apr 2016 14:44:00 -0700APA Referencing Generator (6th ED)
https://www.merlot.org/merlot/viewMaterial.htm?id=1150535
APA Reference Generator creates and formats citations in the APA Referencing style. Fri, 8 Apr 2016 13:25:03 -0700Selective Repeat / Go Back N
https://www.merlot.org/merlot/viewMaterial.htm?id=1149882
Simulator on Selective Repeat / Go Back NWed, 6 Apr 2016 17:35:02 -0700Sketchpad - Online drawing/paint application
https://www.merlot.org/merlot/viewMaterial.htm?id=1145384
Sketchpad is a free, no frills, online painting and drawing program that can be accessed by going to https://mudcu.be/sketchpad/Mon, 28 Mar 2016 12:15:40 -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