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CITATION

Stephens, Larry. Schaum's Outline of Beginning Statistics, Second Edition. US: McGraw-Hill, 2009.

Schaum's Outline of Beginning Statistics, Second Edition

Authors: Larry Stephens

Published:  August 2009

eISBN: 9780071702621 0071702628 | ISBN: 9780071635332
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  • Contents
  • Chapter 1 Introduction
  • Statistics
  • Descriptive Statistics
  • Inferential Statistics: Population and Sample
  • Variable, Observation, and Data Set
  • Quantitative Variable: Discrete and Continuous Variable
  • Qualitative Variable
  • Nominal, Ordinal, Interval, And Ratio Levels of Measurement
  • Summation Notation
  • Computer Software and Statistics
  • Chapter 2 Organizing Data
  • Raw Data
  • Frequency Distribution for Qualitative Data
  • Relative Frequency of a Category
  • Percentage
  • Bar Graph
  • Pie Chart
  • Frequency Distribution for Quantitative Data
  • Class Limits, Class Boundaries, Class Marks, and Class Width
  • Single-Valued Classes
  • Histograms
  • Cumulative Frequency Distributions
  • Cumulative Relative Frequency Distributions
  • Ogives
  • Stem-and-Leaf Displays
  • Chapter 3 Descriptive Measures
  • Measures of Central Tendency
  • Mean, Median, and Mode for Ungrouped Data
  • Measures of Dispersion
  • Range, Variance, and Standard Deviation for Ungrouped Data
  • Measures of Central Tendency and Dispersion for Grouped Data
  • Chebyshev’s Theorem
  • Empirical Rule
  • Coefficient of Variation
  • Z Scores
  • Measures of Position: Percentiles, Deciles, and Quartiles
  • Interquartile Range
  • Box-and-Whisker Plot
  • Chapter 4 Probability
  • Experiment, Outcomes, and Sample Space
  • Tree Diagrams and the Counting Rule
  • Events, Simple Events, and Compound Events
  • Probability
  • Classical, Relative Frequency and Subjective Probability Definitions
  • Marginal and Conditional Probabilities
  • Mutually Exclusive Events
  • Dependent and Independent Events
  • Complementary Events
  • Multiplication Rule for the Intersection of Events
  • Addition Rule for the Union of Events
  • Bayes’ Theorem
  • Permutations and Combinations
  • Using Permutations and Combinations to Solve Probability Problems
  • Chapter 5 Discrete Random Variables
  • Random Variable
  • Discrete Random Variable
  • Continuous Random Variable
  • Probability Distribution
  • Mean of a Discrete Random Variable
  • Standard Deviation of a Discrete Random Variable
  • Binomial Random Variable
  • Binomial Probability Formula
  • Tables of the Binomial Distribution
  • Mean and Standard Deviation of a Binomial Random Variable
  • Poisson Random Variable
  • Poisson Probability Formula
  • Hypergeometric Random Variable
  • Hypergeometric Probability Formula
  • Chapter 6 Continuous Random Variables and Their Probability Distributions
  • Uniform Probability Distribution
  • Mean and Standard Deviation for the Uniform Probability Distribution
  • Normal Probability Distribution
  • Standard Normal Distribution
  • Standardizing a Normal Distribution
  • Applications of the Normal Distribution
  • Determining the z and x Values When an Area Under the Normal Curve is Known
  • Normal Approximation to the Binomial Distribution
  • Exponential Probability Distribution
  • Probabilities for the Exponential Probability Distribution
  • Chapter 7 Sampling Distributions
  • Simple Random Sampling
  • Using Random Number Tables
  • Using the Computer to Obtain a Simple Random Sample
  • Systematic Random Sampling
  • Cluster Sampling
  • Stratified Sampling
  • Sampling Distribution of the Sampling Mean
  • Sampling Error
  • Mean and Standard Deviation of the Sample Mean
  • Shape of the Sampling Distribution of the Sample Mean and the Central Limit Theorem
  • Applications of the Sampling Distribution of the Sample Mean
  • Sampling Distribution of the Sample Proportion
  • Mean and Standard Deviation of the Sample Proportion
  • Shape of the Sampling Distribution of the Sample Proportion and the Central Limit Theorem
  • Applications of the Sampling Distribution of the Sample Proportion
  • Chapter 8 Estimation and Sample Size Determination: One Population
  • Point Estimate
  • Interval Estimate
  • Confidence Interval for the Population Mean: Large Samples
  • Maximum Error of Estimate for the Population Mean
  • The t Distribution
  • Confidence Interval for the Population Mean: Small Samples
  • Confidence Interval for the Population Proportion: Large Samples
  • Determining the Sample Size for the Estimation of the Population Mean
  • Determining the Sample Size for the Estimation of the Population Proportion
  • Chapter 9 Tests of Hypotheses: One Population
  • Null Hypothesis and Alternative Hypothesis
  • Test Statistic, Critical Values, Rejection and Nonrejection Regions
  • Type I and Type II Errors
  • Hypothesis Tests About a Population Mean: Large Samples
  • Calculating Type II Errors
  • P Values
  • Hypothesis Tests About a Population Mean: Small Samples
  • Hypothesis Tests About a Population Proportion: Large Samples
  • Chapter 10 Inferences for Two Populations
  • Sampling Distribution of X[sub(1)] – X[sub(2)] for Large Independent Samples
  • Estimation of μ[sub(1)] – μ[sub(2)] Using Large Independent Samples
  • Testing Hypothesis About μ[sub(1)] – μ[sub(2)] Using Large Independent Samples
  • Sampling Distribution of X[sub(1)] – X[sub(2)] for Small Independent Samples from Normal Populations with Equal (but Unknown) Standard Deviations
  • Estimation of μ[sub(1)] – μ[sub(2)] Using Small Independent Samples from Normal Populations with Equal (but Unknown) Standard Deviations
  • Testing Hypothesis About μ[sub(1)] – μ[sub(2)] Using Small Independent Samples from Normal Populations with Equal (but Unknown) Standard Deviations
  • Sampling Distribution of X[sub(1)] – X[sub(2)] For Small Independent Samples from Normal Populations with Unequal (and Unknown) Standard Deviations
  • Estimation of μ[sub(1)] – μ[sub(2)] Using Small Independent Samples from Normal Populations with Unequal (and Unknown) Standard Deviations
  • Testing Hypothesis About μ[sub(1)] – μ[sub(2)] Using Small Independent Samples from Normal Populations with Unequal (and Unknown) Standard Deviations
  • Sampling Distribution of d for Normally Distributed Differences Computed for Dependent Samples
  • Estimation of μ[sub(d)] Using Normally Distributed Differences Computed from Dependent Samples
  • Testing Hypothesis About μ[sub(d)] Using Normally Distributed Differences Computed from Dependent Samples
  • Sampling Distribution of P[sub(1)] = P[sub(2)] for Large Independent Samples
  • Estimation of P[sub(1)] – P[sub(2)] Using Large Independent Samples
  • Testing Hypothesis About P[sub(1)] – P[sub(2)] Using Large Independent Samples
  • Chapter 11 Chi-Square Procedures
  • Chi-Square Distribution
  • Chi-Square Tables
  • Goodness-of-Fit Test
  • Observed and Expected Frequencies
  • Sampling Distribution of the Goodness-of-Fit Test Statistic
  • Chi-Square Independence Test
  • Sampling Distribution of the Test Statistic for the Chi-Square Independence Test
  • Sampling Distribution of the Sample Variance
  • Inferences Concerning the Population Variance
  • Chapter 12 Analysis of Variance (ANOVA)
  • F Distribution
  • F Table
  • Logic Behind a One-way ANOVA
  • Sum of Squares, Mean Squares, and Degrees of Freedom for a One-Way ANOVA
  • Sampling Distribution for the One-Way ANOVA Test Statistic
  • Building One-Way ANOVA Tables and Testing the Equality of Means
  • Logic Behind a Two-Way ANOVA
  • Sum of Squares, Mean Squares, and Degrees of Freedom for a Two-Way ANOVA
  • Building Two-Way ANOVA Tables
  • Sampling Distributions for the Two-Way ANOVA
  • Testing Hypothesis Concerning Main Effects and Interaction
  • Chapter 13 Regression and Correlation
  • Straight Lines
  • Linear Regression Model
  • Least-Squares Line
  • Error Sum of Squares
  • Standard Deviation of Errors
  • Total Sum of Squares
  • Regression Sum of Squares
  • Coefficient of Determination
  • Mean, Standard Deviation, and Sampling Distribution of the Slope of the Estimated Regression Equation
  • Inferences Concerning the Slope of the Population Regression Line
  • Estimation and Prediction in Linear Regression
  • Linear Correlation Coefficient
  • Inference Concerning the Population Correlation Coefficient
  • Chapter 14 Nonparametric Statistics
  • Nonparametric Methods
  • Sign Test
  • Wilcoxon Signed-Ranks Test for Two Dependent Samples
  • Wilcoxon Rank-Sum Test for Two Independent Samples
  • Kruskal–Wallis Test
  • Rank Correlation
  • Runs Test for Randomness
  • Appendix 1 Binomial Probabilities
  • Appendix 2 Areas Under the Standard Normal Curve from 0 to Z
  • Appendix 3 Area In the Right Tail Under the t Distribution Curve
  • Appendix 4 Area In the Right Tail Under the Chi-square Distribution Curve
  • Appendix 5 Area in the Right Tail Under the F Distribution Curve
  • Index