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This Concept Map, created with IHMC CmapTools, has information related to: Inferential statistics, Random variables can follow Other distributions, Probability distributions are characterized by their Variance, Probability distributions are characterized by their Expected value, A Sample generates results containing Errors, Errors can be Random errors, Inferential statistics provides Non-parametric techniques, Estimates from many samples generate A sampling distribution, A Population contains Parameters, Probability distributions include Other distributions, Random variables can follow The Normal Distribution, Parametric techniques are used when populations follow The Normal Distribution, A Sample provides information to obtain Estimates, Random variables can follow The Binomial Distribution, Inferential statistics provides Parametric techniques, Interval estimates are computed using Confidence intervals, Systematic errors cause Bias, A Sample provides estimates of Parameters, A sampling distribution e.g. The sample distribution of the mean of x, Parameters characterize Probability distributions, Errors can be Systematic errors
Probability
Probability distribution
Discrete probability distribution
Population and sample
Sample statistic
Hypothesis testing
Random variable
Other probability distributions
Population and sample
Inference
Expected value
Binomial distribution
Parameters
Sampling distribution
Assessment test
Variance: definition
MDM_Introduction
Applet: Standard Normal
Applet: Areas under the normal
Areas under Normal distribution
Standard Normal distribution
Normal distribution
Normal distribution-description
Confidence interval
Sampling distribution
Applet: Central Limit Theorem