Statistics Laminate Reference Chart: Parameters, Variables, Intervals, Proportions (Quickstudy: Academic )

BarCharts, Inc.

ISBN 10: 1572229446 / ISBN 13: 9781572229440

Published by Barcharts, 2005

About this Item

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Seller Inventory # S_463264448

Bibliographic Details

• Title: Statistics Laminate Reference Chart: Parameters, Variables, Intervals, Proportions (Quickstudy: Academic )
• Publisher: Barcharts
• Publication Date: 2005
• Language: English
• ISBN 10: 1572229446
• ISBN 13: 9781572229440
• Binding: Pamphlet
• Condition: Good

About this title

Synopsis

Essential #1 best-selling reference for more than 20 years sold in almost every college bookstore in the country. The core statistics course is a critical and difficult class for many and develops a foundation that will support use of statistics in higher level courses in so many subject areas and majors. This six-page laminated guide is filled with more need-to-know facts per page than any book or website and will continue to support in future study. The equations, tables, and graphs that matter most accompany concisely written explanations in an outline format for easy access. This reference tool for students and professionals is proven to work selling millions since it's release. With the answers you need at your fingertips this durable tool will last your entire career and is an incredible value.
6 page laminated guide includes:

• Terminology
• Frequency Distribution
• Measures of Central Tendency
• Measures of Dispersion
• Graphing Techniques
• Probability
• Random Variables
• Continuous Random Variables
• Statistical Inference
• Biased & Unbiased Estimators
• Standard Error of the Mean
• Hypothesis Testing
• Central Limit Theorem
• Inference for Population Mean Using the Z-Statistic
• Inference for Population Mean using the t-Statistic
• Confidence Intervals
• Comparing Population Means
• Comparing Variances
• Analysis of Variance (ANOVA)
• Proportions
• Correlation
• Chi-Square Tests
• Hypothesis Test for Linear Correlation
• Regression
• Simple Linear Regression
• Significance of a Regression Model
• Linear Determination
• Residuals