Keywords: Estimation (64)

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Estimating Length Using Scientific Notation

Estimating Length Using Scientific Notation

This lesson unit is intended to help teachers assess how well students ... (more)

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. (less)

Subject:
Mathematics and Statistics
Material Type:
Assessments
Lesson Plans
Collection:
Mathematics Assessment Project (MAP)
Provider:
Shell Center for Mathematical Education|University of California, Berkeley
Estimations and Approximations: The Money Munchers

Estimations and Approximations: The Money Munchers

This lesson unit is intended to help you assess how well students ... (more)

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. (less)

Subject:
Mathematics and Statistics
Material Type:
Assessments
Lesson Plans
Collection:
Mathematics Assessment Project (MAP)
Provider:
Shell Center for Mathematical Education|University of California, Berkeley
How Big Were the Dinosaurs?

How Big Were the Dinosaurs?

In this activity (located on page 4 of PDF), learners gain insight ... (more)

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. (less)

Subject:
Science and Technology
Material Type:
Activities and Labs
Lesson Plans
Collection:
SMILE Pathway
Provider:
Chicago Children's Museum
Read the Fine Print
How Many Jelly Beans?

How Many Jelly Beans?

In this video segment from Cyberchase, Inez estimates whether she has enough ... (more)

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. ***Access to Teacher's Domain content now requires free login to PBS Learning Media. (less)

Subject:
Mathematics and Statistics
Material Type:
Video Lectures
Collection:
Teachers' Domain
Provider:
PBS Learning Media
Read the Fine Print
Identification, Estimation, and Learning, Spring 2006

Identification, Estimation, and Learning, Spring 2006

This course provides a broad theoretical basis for system identification, estimation, and ... (more)

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. (less)

Subject:
Science and Technology
Material Type:
Activities and Labs
Assessments
Full Course
Homework and Assignments
Lecture Notes
Syllabi
Collection:
MIT OpenCourseWare
Provider:
M.I.T.
Author:
Asada, Harry
Remix and Share
Introduction to Estimation Theory

Introduction to Estimation Theory

This module introduces estimation theory and its terminology, including bias, consistency, and ... (more)

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. (less)

Subject:
Mathematics and Statistics
Science and Technology
Material Type:
Readings
Syllabi
Collection:
Connexions
Provider:
Rice University
Author:
Don Johnson
No Strings Attached
Introduction to Statistics

Introduction to Statistics

Introduction to Statistics. Random Variable, Mean, Variance, Standard Deviation and Mathematical Expectation. ... (more)

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. (less)

Subject:
Mathematics and Statistics
Material Type:
Full Course
Readings
Syllabi
Collection:
Connexions
Provider:
Rice University
Author:
Ewa Wosik
No Strings Attached
Investigating the Dynamic Martian Polar Caps

Investigating the Dynamic Martian Polar Caps

In this activity, students download NASA Hubble Space Telescope (HST) images of ... (more)

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. (less)

Subject:
Mathematics and Statistics
Science and Technology
Material Type:
Activities and Labs
Assessments
Lesson Plans
Collection:
NASA
Provider:
NASA|MSU-Bozeman Center for Educational Resources Project
Read the Fine Print
Learning Math: Data Analysis

Learning Math: Data Analysis

Data Analysis, Statistics, and Probability introduces statistics as a problem-solving process. In ... (more)

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. (less)

Subject:
Mathematics and Statistics
Material Type:
Case Study
Video Lectures
Collection:
Annenberg Learner
Provider:
Annenberg Learner
Read the Fine Print