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North Carolina Standard Course of Study Advanced Functions and Modeling Data Analysis and Probability:Competency Goal 1: The learner will analyze data and apply probability concepts to solve problems.
Lesson (...)
Lesson: Introduces conditional probability and the probability of simultaneous events.
Lesson: Utilizes and reinforces concepts of probability, mean, line plots, experimental data, and chaos in analyzing a forest fire simulation.
Lesson: Looks at data structures and their applications to probability theory.
Lesson: Introduces students to simple probability concepts.
Lesson: Students learn about how probability can be represented using geometry.
Lesson: Considers probability concepts on the basis of statistics in professional sports.
Activity (...)
Activity: Students run a simulation of how a fire will spread through a stand of trees, learning about probability and chaos. Parameters: Forest density, wind direction, size of forest.
Activity: Run a simulation of how a fire spreads through a stand of trees, learning about probability and chaos. Track the results of multiple burns and use the data to draw conclusions.
Activity: Choose one of N doors to experimentally determine the odds of winning the grand prize behind one of the doors, as in the TV program "Let's Make a Deal." Parameters: Number of doors, number of trials, staying or switching between the two remaining doors.
Activity: Enter data to create a bar graph, then change many settings for the graph's appearance.
Activity: Students can create box plots for either built-in or user-specified data as well as experiment with outliers. User may choose to use or not use the median for calculation of interquartile range.
Activity: Experiment with a simulation to get an approximation of Pi by dropping a needle on a lined sheet of paper.
Activity: Enter your own data categories and the value of each category to create a pie chart. There are
also built in data sets which can be viewed.
Activity: Simulation of a coin toss allowing the user to input the number of flips. Toss results can be viewed as a list of individual outcomes, ratios, or table.
Activity: Compare two sets of objects, using estimation to determine which is greater. Estimate a number of objects, the length of a line, or the area of a shape. Parameter: error tolerance. Comparison Estimator is one of the Interactivate assessment explorers.
Activity: Compare theoretical and experimental probabilities, using dice, cards, spinners, or coin tosses. Three different probabilities can be compared at once. Parameters: Type of probabilities, number of trials.
Activity: Enter a set of data points, then derive a function to fit those points. Manipulate the function on a coordinate plane using slider bars. Learn how each constant and coefficient affects the resulting graph.
Activity: Experiment with the outcome distribution for a roll of two dice by simulating a dice throwing game. Parameters: Which player wins with which total rolled.
Activity: Run a simulation of how a fire will spread through a stand of trees, learning about probability and chaos. Parameters: Probability that a tree will set fire to each of its eight neighbors.
Activity: Practice estimation skills by determining the number of objects, the length of a line, or the area of a shape. Parameters: error tolerance of estimate. Estimator is one of the Interactivate assessment explorers.
Activity: Experiment with probability using a fixed size section spinner, a variable section spinner, two
regular 6-sided dice or customized dice.
Activity: Run a simulation of how a fire will spread through a stand of trees, learning about probability and chaos. Parameters: Probability that a tree catches fire if its neighbor is on fire.
Activity: This applet allows the user to experiment with randomly generated data sets at various sample sizes and standard deviations. Then, users can compare the distribution of the experimental data to the expected distribution.
Activity: View histograms for built-in or user-specified data. Experiment with how the size of the class intervals influences the appearance of the histogram. Parameters: Data sets, class sizes.
Activity: Enter data and view the mean, median, variance, and standard deviation of the data set. Parameters: Number of observations, range for observations, which statistics to view, identifiers for the data.
Activity: Practice estimation skills by determining whether the number of objects, the length of a line, or the area of a shape is more or less than the number given. Parameters: error tolerance of estimate. More or Less Estimator is one of the Interactivate assessment explorers.
Activity: Enter data to create a double bar graph, then manipulate the graph's maximum and minimum values.
Activity: Students compare multiple independent variables as predictors of one dependent variable. Students explore correlation and lines of best-fit.
Activity: Change the standard deviation of an automatically generated normal distribution to create a new histogram. Observe how well the histogram fits the curve, and how areas under the curve correspond to the number of trials. Parameters: standard deviation, number of trials, class intervals.
Activity: In this applet you can adjust the parameters on two Gaussian curves to determine if there is a possibility of a difference between the two means.
Activity: Create a pie chart, adjusting the size of the divisions using your mouse or by entering values.
Parameters: Number of sections, size of sections, whether to use percents or fractions.
Activity: PlopIt allows users to build dot plots of data using the mouse. View how the mean, median, and mode change as entries are added to the plot. Parameters: Range for observations.
Activity: Experiment with a simple ecosystem consisting of grass, rabbits, and wolves, learning about probabilities, chaos, and simulation.
Activity: Simulate a game where two players each roll a die, and the lucky player moves one step to the finish. Parameters: what rolls win and how many steps to the finish line.
Activity: Simulate a game where "N" players roll two dice, and the lucky player has an advantage for reaching the finish. Parameters: the number of players, number of trials and length of the race.
Activity: Plot a bivariate data set, determine the line of best fit for their data, and then check the accuracy of your line of best fit.
Activity: Graph ordered pairs and customize the graph title and axis labels. Points are connected from left to right, rather than being connected in the order they are entered.
Activity: Choose one of three doors to experimentally determine the odds of winning the grand prize behind one of the doors, as in the TV program "Let's Make a Deal." Parameters: Staying or switching between the two remaining doors.
Activity: Plot ordered pairs of numbers, either as a scatter plot or with the dots connected. Points are connected from right to left, rather than being connected in the order they are entered.
Activity: Change the median and standard deviation of an automatically generated normal distribution to create a skewed distribution, allowing you to observe properties like what it means for the mean, median, and mode to be different. Parameters: median, standard deviation, number of trials, class intervals.
Activity: Create a game spinner with one to twelve sectors in order to look at experimental and theoretical probabilities. Parameters: Number of sectors, number of trials.
Activity: Models how a population of susceptible, infected, and recovered people is affected by a disease.
Activity: View stem-and-leaf plots of your own data, and then practice finding means, medians and modes. Stem and Leaf Plotter is one of the Interactivate assessment explorers.
Activity: Choose one of three boxes and choose one ball from the box to look at conditional probabilities. Parameters: Number of trials.
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