📄 Executive Summary
Introductory Business Statistics (2nd edition) serves undergraduate students in business, economics, and related disciplines enrolled in a standard one-semester introductory course. The textbook assumes a background in basic algebra and arithmetic, presenting quantitative methods through commercial scenarios, management problems, and economic data. Its primary scope covers the foundation of statistical literacy, moving systematically from raw data collection and descriptive metrics to probability distributions, formal statistical inference, and linear regression modeling.
The curriculum follows a structured progression across thirteen chapters. It starts with data classification, sampling techniques, levels of measurement, experimental design, and descriptive statistics, teaching readers to summarize data using measures of center, spread, location, and various graphical representations. The text then establishes the mathematical foundations of probability, covering basic set operations, conditional probability, contingency tables, and discrete distributions such as the binomial, hypergeometric, geometric, and Poisson distributions. It subsequently introduces continuous variables through the uniform, exponential, and normal distributions, establishing the Central Limit Theorem and sampling distributions as the critical bridge to inferential statistics.
The second half of the book concentrates on estimation and hypothesis testing. Readers learn to construct and interpret point estimates and confidence intervals for population means and proportions using standard normal and Student's t-distributions, alongside sample size determination methods. The text develops one-sample and two-sample hypothesis testing frameworks for means, proportions, and paired differences. It further extends inferential testing to categorical data and variance analysis through chi-square goodness-of-fit, independence, and homogeneity tests, as well as the F-distribution and one-way Analysis of Variance (ANOVA). The final chapter introduces correlation and Ordinary Least Squares (OLS) regression, addressing slope significance, the coefficient of determination, dummy variables, logarithmic transformations, residual diagnostics, and computation with Microsoft Excel.
Upon completing the text, readers should be able to organize data sets, calculate appropriate summary statistics, formulate and conduct formal hypothesis tests, and specify linear regression equations to evaluate relationships between business variables. The book focuses exclusively on foundational applied methods, leaving advanced topics such as multi-way ANOVA, time-series forecasting models, non-linear estimation methods, and advanced econometric theory outside its scope.
