Language : English
Published : 2016
Pages : 158
Statistics for Applied Science
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This book is a new type of intermediate microeconomics textbook.Previously, the choice was between books that use calculus to present formal theory dryly and with few, if any, applications to the real world and books that include applications but present theory using algebra and graphs only. This book uses calculus, algebra, and theory to analyze real-world problems. My purpose is to show that economic theory has practical, problem-solving uses and is not an empty academic exercise.
This book shows how individuals, policymakers, and firms use microeconomic tools to analyze and resolve problems. For examples, students learn that:
- individuals can draw on microeconomic theories when deciding whether to invest and whether to sign a contract that pegs prices to the government’s measure of inflation;
- policymakers (and voters) can employ microeconomics to predict the impact of taxes, regulations, and other measures before they are enacted;
- lawyers and judges use microeconomics in antitrust, discrimination, and contract cases; and
- firms apply microeconomic principles to produce at least cost and maximize profit, select strategies, decide whether to buy from a market or to produce internally, and write contracts to provide optimal incentives for employees.
My experience in teaching microeconomics for the departments of economics at the Massachusetts Institute of Technology; the University of Pennsylvania; the University of California, Berkeley; and the Wharton Business School has convinced me that students prefer the emphasis on real-world issues.
This book emphasizes the basics of statistics and its application. The concepts are explained in clear and simple language, with the aid of illustrative examples, charts and diagrams.
- Chapter 1. Introduction to Statistics
- Chapter 2. Data Presentation
- Chapter 3. Measures of Central Tendency
- Chapter 4. Measures of Dispersion
- Chapter 5. Index Number
- Chapter 6. Correlation and Regression
- Chapter 7. Time Series Data Analysis