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t - ISpatula
t - ISpatula

Theories - the Department of Psychology at Illinois State University
Theories - the Department of Psychology at Illinois State University

Handout 7a Example of calculating Beta
Handout 7a Example of calculating Beta

confidence intervals
confidence intervals

Measures of central tendency: The mean
Measures of central tendency: The mean

Chapter 4: Numerical Methods for Describing Data
Chapter 4: Numerical Methods for Describing Data

...  How do the mean and median compare, or if only one is given, was the appropriate measure used?  Is the standard deviation large or small, and what does it tell you about the variable being summarized?  Can anything be said about the values by applying Chebyshev’s Rule of the Empirical Rule? Caut ...
Clicker_chapter18 - ROHAN Academic Computing
Clicker_chapter18 - ROHAN Academic Computing

Hwk2Sol
Hwk2Sol

... ii) A 99% PI for sqrt(#episodes) is 3.2±3.1* 1*sqrt( 1+1/12) =(0,6.44). A 99% PI for the number of episodes is (0, 41.47) (or better (0,42) since we cannot have part of an episode) 25 episodes is not unusual. d. The investigator discoverered that mouse #7 was caged right beside the lab answering mac ...
Hatfield.Topic 8
Hatfield.Topic 8

Online 14 - Section 7.2
Online 14 - Section 7.2

... 2. For the population of farm workers in a certain country, suppose that weekly income has a distribution that is skewed to the right with a mean of μ = $400 and a standard deviation of σ = $153. A researcher, unaware of these values, plans to randomly sample 81 farm workers and use the sample mean ...
answers to problems 1-3
answers to problems 1-3

... selecting 6 or more smokers is the area under the same normal distribution that lies to the right of X = 5.5. For this value of X, the appropriate Z is Z= ...
252solnA2
252solnA2

...  The Anderson-Darling statistic will be small, and the associated p-value will be larger than your chosen -level. (Commonly chosen levels for  include 0.05 and 0.10.) Minitab also displays approximate 95% confidence intervals (curved blue lines) for the fitted distribution. These confidence inter ...
Review for Test 2
Review for Test 2

μ = 10 H
μ = 10 H

STAT101: A Review of the Basics
STAT101: A Review of the Basics

... First we test the null hypothesis that the variances of the two groups are equal. This is done with the F' statistics given at the bottom of the output which shows the probability that the variances are unequal due to chance alone. If the probability (Prob F') is small, usually less than .05, then r ...
Chapters 4-6: Estimation
Chapters 4-6: Estimation

... In other words, a statistic. Usually, estimators are used to give plausible values of some population parameter. • X, X̃, S 2 , and p are point estimators of the parameters µ, µ̃, σ 2 and p respectively. Point Estimate - The resulting value of a point estimator, when applied to a data set. • x = 27. ...
Ch6and7english
Ch6and7english

Chapter 5 and Chapter 6 Review READ: Here are some problems I
Chapter 5 and Chapter 6 Review READ: Here are some problems I

... 7. A key statistic used by football coaches to evaluate players is a player’s 40-yard sprint time. Can a drill be developed for improving a player’s speed in the sprint? Researchers at Northern Kentucky University designed and tested a speed-training program for junior varsity and varsity high schoo ...
S1: Chapter 1 Data: Location
S1: Chapter 1 Data: Location

t-Test Worksheet Answers
t-Test Worksheet Answers

Confidence Intervals
Confidence Intervals

Appendix A
Appendix A

Stt511 Lecture02
Stt511 Lecture02

... As with x/n, p is also between 0 and 1, and while x/n is a sample characteristic, p is a population characteristic. The relationship between the two parallels the relationship between and , and between x and . In particular, we will subsequently use x/n to make inferences about p. ...
Quantitative Data
Quantitative Data

Exam 1 PS 217, Spring 2010 Convert to z
Exam 1 PS 217, Spring 2010 Convert to z

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Taylor's law

Taylor's law (also known as Taylor’s power law) is an empirical law in ecology that relates the variance of the number of individuals of a species per unit area of habitat to the corresponding mean by a power law relationship.
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