Applied Regression Analysis A Second Course in Business and Economic Statistics 4th Edition By Dielman
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Applied Regression Analysis A Second Course in Business and Economic Statistics 4th Edition By Dielman

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498
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4.21 MB
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Digital PDF
Course
Business
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eBook[PDF]
About this ebook
Applied Regression Analysis: A Second Course in Business and Economic Statistics (4th Edition) by Terry E. Dielman focuses on building, validating, and interpreting regression models using real-world data and commercial statistical software (such as Excel, MINITAB, and SAS). [1, 2]
The textbook is divided into core regression topics (the first 8 chapters) and optional business statistics topics. Below is a comprehensive breakdown of the major subjects covered in the text: [1]
Core Regression Topics
  • Introduction to Regression: Foundations of regression analysis and its utility in business and economic decision-making. [1, 2]
  • Review of Basic Statistics: A foundational review of descriptive statistics, discrete random variables, probability distributions, populations, sampling, and the normal distribution. [1]
  • Simple Linear Regression: Describing linear relationships between two variables, estimating parameters via least squares, evaluating model fit, using trend lines on time-series data, and understanding standard cautions when interpreting results. [1]
  • Multiple Regression Analysis: Extending models to include two or more independent variables, finding coefficients, and interpreting complex economic or business data. [1]
  • Model Building and Assumptions: Understanding the core assumptions behind linear models, checking for violations, and employing regression diagnostics to test data validity. [1, 2]
  • Qualitative Variables: Utilizing indicator or "dummy" variables to incorporate non-numeric data into quantitative regression models.
  • Variable Transformation: Scaling and altering independent or dependent variables to fix non-linear relationships or issues with heteroscedasticity.
Special & Advanced Business Statistics Topics
  • Analysis of Variance (ANOVA): Comparing means across multiple groups, often treated alongside or as an extension of experimental designs.
  • Time Series Forecasting: Applying statistical models to sequential data over time to predict future business and economic trends.
  • Discriminant Analysis & Logistic Regression: Basic entry into classification methods, focusing on categorical dependent outcomes (e.g., binary variables like yes/no or success/failure). [1, 2]

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Topics

. Special & Advanced Business Statistics Analysis of Variance (ANOVA) Discriminant Analysis & Logistic Regression ISBN 13: 978-0357233276 ISBN 13: 978-0357894026 ISBN 13: 978-0534465483 ISBN 13: 978-0534465506 Model Building and Assumptions Multiple Regression Analysis Qualitative Variables regression analysis sampling and the normal distribution Simple Linear Regression Time Series Forecasting Variable Transformation
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