Full Description
Gain the statistics skills you need for the social sciences with this accessible introductory guide Statistical Methods for the Social Sciences, 5th Edition, Global Edition, by Alan Agresti, introduces you to statistical methods used in social science disciplines with no previous knowledge of statistics necessary. With an emphasis on concepts and applications, the book requires only a minimal mathematical background, maintaining a low technical level throughout to make it accessible to beginners.
The 5th edition has a strong focus on real examples to help you learn the fundamental concepts of sampling distributions, confidence intervals, and significance tests. This approach also helps you understand how to apply your learning to the real world. This edition also emphasises the interpretation of software output rather than the formulas for performing analysis, reflecting advances in statistical software - which are more frequently used by social scientists to analyse data today.
Other updates include:
Numerous homework exercises included in each chapter.
Updated data in most exercises.
New sections, such as that on maximum likelihood estimation in chapter 5
New examples ask students to use applets to help them learn the fundamental concepts of sampling distributions, confidence intervals, and significance tests.
The text also relies more on applets for finding tail probabilities from distributions such as the Normal, t, and chi-squared.
With a wide array of learning features and the latest available information, this text will equip you with the knowledge you need to succeed in your course - an ideal companion for students majoring in social science disciplines.
Contents
Introduction
Sampling and Measurement
Descriptive Statistics
Probability Distributions
Statistical Inference: Estimation
Statistical Inference: Significance Tests
Comparison of Two Groups
Analyzing Association between Categorical Variables
Linear Regression and Correlation
Introduction to Multivariate Relationships
Multiple Regression and Correlation
Regression with Categorical Predictors: Analysis of Variance Methods
Multiple Regression with Quantitative and Categorical Predictors
Model Building with Multiple Regression
Logistical Regression: Modeling Categorical Responses
