- Abstract:
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In this Cyberchase video segment, the CyberSquad estimates how much air supply they will need to complete an underwater mission.
- Subject:
- Mathematics and Statistics
- Grade Level:
- Primary, Secondary
- Collection:
- Teachers' Domain
In this Cyberchase video segment, the CyberSquad estimates how much air supply they will need to complete an underwater mission.
This lesson unit is intended to help teachers assess how well students are able to: estimate lengths of everyday objects; convert between decimal and scientific notation; and make comparisons of the size of numbers expressed in both decimal and scientific notation.
You can throw a collection of randomly-generated darts at a target. Some hit, some miss. We can use this to estimate Pi. Recommendation: Double-click the geogebra window to open separately. You may then simply use F9 to regenerate a new set o
Harry estimates how long it will take him to get to the front of a long ticket line in this Cyberchase video segment.
Estimating pi by rolling a Circles. Students can then compare the distance travelled by the Circles and its diameter.
Explore size estimation in one, two and three dimensions! Multiple levels of difficulty allow for progressive skill improvement.
Students develop their estimation skills while evaluating their television-watching habits and draw conclusions about the influence of television in their lives.
This lesson unit is intended to help you assess how well students are able to: Model a situation; make sensible, realistic assumptions and estimates; and use assumptions and estimates to create a chain of reasoning, in order to solve a practical problem.
Eighth grade teacher Patrick Roda has students apply their knowledge of the line of best fit to design successful bungee jumps. He tells students that they will test their bungee jumps in the stairwell, with the goal of getting Barbie as close to the ground as possible without touching the bottom step. Patrick has his students begin by constructing a bungee with two rubber bands, attaching a Barbie, and measuring how far the Barbie falls. Students add more rubber bands, perform multiple trials, and record their results in a table. Using their collected data, students construct a scatter plot and determine an equation for the line of best fit. After making predictions about the performance of their bungee jumps, the students test their bungee jumps and discuss their results as a class.
Estimate the center of a Circles.
Students will identify how technology has changed in an occupation over time. This is also an introductory rounding/estimation lesson using addition of money.
In this activity (located on page 4 of PDF), learners gain insight into the actual size of dinosaurs and practice making estimations and measurements. Learners measure the lengths of various dinosaurs by measuring lengths of string in field or gym. Learners also estimate and measure these lengths by lying head to foot. Learners also compare and contrast the sizes of different dinosaur species.
In this video segment from Cyberchase, Inez estimates whether she has enough jelly beans in her large container to decorate all of the cookies in her batch.
This course provides a broad theoretical basis for system identification, estimation, and learning. Students will study least squares estimation and its convergence properties, Kalman filters, noise dynamics and system representation, function approximation theory, neural nets, radial basis functions, wavelets, Volterra expansions, informative data sets, persistent excitation, asymptotic variance, central limit theorems, model structure selection, system order estimate, maximum likelihood, unbiased estimates, Cramer-Rao lower bound, Kullback-Leibler information distance, Akaike's information criterion, experiment design, and model validation.
This module introduces estimation theory and its terminology, including bias, consistency, and efficiency. In searching for methods of extracting information from noisy observations, this chapter describes estimation theory, which has the goal of extracting from noise-corrupted observations the values of disturbance parameters (noise variance, for example), signal parameters (amplitude or propagation direction), or signal waveforms. Estimation theory assumes that the observations contain an information-bearing quantity, thereby tacitly assuming that detection-based preprocessing has been performed (in other words, do I have something in the observations worth estimating?). Conversely, detection theory often requires estimation of unknown parameters: Signal presence is assumed, parameter estimates are incorporated into the detection statistic, and consistency of observations and assumptions tested. Consequently, detection and estimation theory form a symbiotic relationship, each requiring the other to yield high-quality signal processing algorithms.
Introduction to Statistics. Random Variable, Mean, Variance, Standard Deviation and Mathematical Expectation. Discrete Distributions: Bernoulli trials and Bernoulli distribution, geometric distribution, Poisson distribution. Continuous Distributions: random variables of the continuous type, uniform distribution, exponential distribution, gamma distribution, chi-square distribution, normal distribution, t-distributions. Estimation: biased and unbiased esimators, convidence intervals for means, convidence intervals for variances, sample size, maximum error of the point estimate, Likelihood function, Maximum Likelihood Estimation (MLE), Asymptotic Distributions of Maximum Likelihood Estimators, Chebyshev's Inequality. Hypothesis: tests of statistical hypotheses, Type I error, Type II error, tests about proportions, null hypothesis, alternative hypothesis, significance level of the test, probability value, tail-end probability, standard error of the mean, tests about one mean and one variance, test of the equality of two independent normal distributions, best critical region, Neyman-Pearson Lemma, most powerful test, uniformly most powerful critical region, Likelihood Ratio tests, critical region for the likelihood ratio test. Pseudo-Numbers: uniform pseudo-random variable generation, congruential generators, shift-register generators, Fibonacci generators, Combinations of Generators (Shuffling). The Inverse Probability Method for Generating Random Variables. The Logistic Distribution.
In this activity, students download NASA Hubble Space Telescope (HST) images of the Martian polar ice caps in summer and winter. Using image processing techniques, students measure and compare various images of the changing Martian and Earth polar ice caps.
Data Analysis, Statistics, and Probability introduces statistics as a problem-solving process. In this course, you can build your skills through investigations of different ways to collect and represent data, and describe and analyze variation in data. Through practical examples, you will come to understand some statistical concepts, such as data representation, variation, the mean and median, bivariate data, probability, designing statistical experiments, and population estimations. The concluding case studies, divided into grade bands for K-2, 3-5, and 6-8 teachers, show you how to apply what you have learned in your own classroom.
Students will weigh different products made from corn to determine if a cup of each product has the same mass. Students will use measurement and estimation skills.
At Inside Mathematics, we’ve assembled multiple ways for educators to begin to transform their teaching practices. You might be in search of materials and tasks you can use immediately with your students; you can search by grade level and content area below to find core mathematical principles as well as materials developed by the Mathematics Assessment Resource Service (MARS). If you want to develop your understanding of the national Common Core Standards for Mathematical Practice #6, look here.