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Review: Inferential Statistics Error vs. Bias Sampling Distributions Std. Error of the Mean Central Limit Theorem z-test Hypothesis testing t-ratio & t-tests. Unbiased estimator of population standard deviation t-tests One sample Two sample Confidence Intervals. EPE/EDP 557.
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Review: Inferential Statistics Error vs. Bias Sampling Distributions Std. Error of the Mean Central Limit Theorem z-test Hypothesis testing t-ratio & t-tests Unbiased estimator of population standard deviation t-tests One sample Two sample Confidence Intervals EPE/EDP 557
Populations (continued) • Unbiased estimator of Population Standard Deviation:
Mean of Means: Std. Error of the Mean: or Sampling Distributions
Sampling Distributions (continued) • Estimated Standard Error of the Mean: or
Mean of Distribution of Differences: Standard Error of the Difference: Sampling Distributions of Differences
Sampling Distributions of Differences(continued) • Estimated Standard Error of the Difference:
EPE/EDP 557t-Ratio • t-Ratio: Statistical test employed to determine whether significant (nonchance) differences exist between 2 means • One sample t-test • Comparing a sample mean and a population mean • Comparing a sample mean with chance expectations • Comparing a sample mean with a known value
EPE/EDP 557t-Ratio • One sample t-test (continued) • Degrees of Freedom (df): based on the number of values that are free to vary once a mean and sample size are set • df for one-sample t-test: (N-1) • One-sample hypotheses: • Two-tailed test: • One-tailed test:
EPE/EDP 557t-Ratio • t-Ratio: Statistical test employed to determine whether significant (nonchance) differences exist between 2 means • Two-sample t-test: • Comparing two sample means or just
EPE/EDP 557t-Ratio • Two-sample t-test (continued) • Degrees of Freedom (df): based on the number of values that are free to vary once a mean and sample size are set • df for two-sample t-test: (N + N - 2) • Two-sample hypotheses: • Two-tailed test: • One-tailed test:
EPE/EDP 557 • Confidence Intervals: Range of predicted values within one presumes with a stated degree of confidence that the a parameter falls