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Section 1
Section 1

Chapter 3 Descriptive Statistics
Chapter 3 Descriptive Statistics

... deviation of , the sample means, , are approximately normally distributed for sufficiently large samples (n 30*) regardless of the shape of the population distribution. If the population is normally distributed, the sample means are normally distributed for any sample size. From mathematical expecta ...
Sampling distributions
Sampling distributions

bagging
bagging

Document
Document

... One Sample z Test • From Table 2 in the Statistical Appendix, the probability of getting a more extreme value of z than 2.687 is less than 0.05. (Alternatively, the critical z value for a one-tailed test and a significance level of 0.05 is 1.645, which is less than the calculated value.) Therefore, ...
Worksheet #7
Worksheet #7

taxi problem - Ing-Stat
taxi problem - Ing-Stat

Ethics and Data Analysis
Ethics and Data Analysis

Statistics and Research Design
Statistics and Research Design

sample size consideration in clinical research
sample size consideration in clinical research

... • The required effect size is Δ= −15. • We specify that such an effect be detected with 80% power (1-β= .80) when the significance level α = .05. • Past experience with similar study-with similar sphygmomanometers and similar subjects-suggests that the data will be approximately normally distributed ...
Assignment 2
Assignment 2

... List all possible simple random samples of size n = 2 that can be selected from the population, along with their probability of selection. For each sample calculate the sample mean y and the sample variance s2 . Demonstrate ...
Two-Sample Inference Procedures
Two-Sample Inference Procedures

review - Penn State Department of Statistics
review - Penn State Department of Statistics

Final Exam Review 1 Topics summary
Final Exam Review 1 Topics summary

printable version
printable version

Population
Population

The 2 -test
The 2 -test

Review of key statistical concepts - Penn State Department of Statistics
Review of key statistical concepts - Penn State Department of Statistics

... • Set the significance level, α, the probability of making a Type I error to be small (0.05 or 0.01). • Compare the value of the test statistic to the known distribution of the test statistic. • If the test statistic is more extreme than expected, allowing for an α chance of error, reject the null h ...
introduction to hypothesis tests
introduction to hypothesis tests

... When the normality assumptions can not be made or when the data at hand are ranks rather than measurments on an interval or ratio scale, an alternative test must be sought. A frequently used nonparametric test does not depend on the assumptions of the t test, or measurement beyond the ordinal scale ...
Concepts for Week 1
Concepts for Week 1

... Hypothesis: a suggested explanation for a group of facts or phenomena, either accepted as a basis for further verification (working hypothesis) or accepted as likely to be true Theory: a set of hypotheses related by mathematical or logical arguments to explain and predict a wide variety of connected ...
6. Introduction to Regression and Correlation
6. Introduction to Regression and Correlation

... We will use the notation y   R  x for the linear regression straight line equation. Like any straight line it is defined by two parameters; here  R is the value of y when x = 0 (called the intercept on the y axis) and  is the slope of the line. Note 1: the use of the R subscript for alpha (the ...
Normal Approximation to the Binomial Distribution
Normal Approximation to the Binomial Distribution

Hypothesis Tests
Hypothesis Tests

chapter 9 – sampling distributions - Hatboro
chapter 9 – sampling distributions - Hatboro

Take Notes
Take Notes

... Suppose a team of biologists has been studying the Pinedale children’s fishing pond. Let x represent the length of a single trout taken at random from the pond. This group of biologists has determined that the length has a normal distribution with mean of 10.2 inches and standard deviation of 1.4 in ...
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Resampling (statistics)

In statistics, resampling is any of a variety of methods for doing one of the following: Estimating the precision of sample statistics (medians, variances, percentiles) by using subsets of available data (jackknifing) or drawing randomly with replacement from a set of data points (bootstrapping) Exchanging labels on data points when performing significance tests (permutation tests, also called exact tests, randomization tests, or re-randomization tests) Validating models by using random subsets (bootstrapping, cross validation)Common resampling techniques include bootstrapping, jackknifing and permutation tests.
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