Key features

Strong focus on real examples help students learn the fundamental concepts of sampling distributions, confidence intervals, and significance tests.
Low technical level in first nine chapters makes the text accessible to undergraduate students.
Integration of descriptive and inferential statistics features from an early point in the text.
Strong emphasis on regression topics outlines special cases of a generalised linear model, supported by a wide variety of regression models (such as linear regression, ANOVA, logistic, and regression).
Emphasis on concepts on advanced topics (such as regression and ANOVA) underline the importance of interpreting output from computer packages rather than complex computing formulas.
Descriptive statistic chapter gives students early exposure to contingency tables, regression, concepts of association, and response and explanatory variables.
Relative frequency concept is introduced in chapter four and briefly summarises three basic probability rules occasionally applied in the text.
Full focus on t distribution makes results consistent with software output and emphasises that the normality assumption for the t distribution is mainly needed for small samples with one-sided inference.
Confidence intervals present methods for proportion before the mean. This allows students to learn the basic concept of a confidence interval without being confronted with too many topics all at once.
Comparing Two Groups chapter introduces ideas of bivariate analysis, highlights the distinction between response and explanatory variables, defines independent and dependent samples, discusses how to compare two groups with a difference or a ratio of two parameters, and shows the general formula for finding a standard error of a difference between two independent estimates.
ANOVA focus explains the ideas behind the F test and gives examples before presenting the sums of square formulas.
Logistic regression is explained in a less technical way so it's widely understood by all students.

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Statistical Methods for the Social Sciences, Global Edition, 5th edition

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