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Total Size:
9.5 MB
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AF049461118FC73B2472742213C479C71AD0ADAA
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Jan. 1, 2026, 11:47 a.m.
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(Last updated: Jan. 1, 2026, 11:47 a.m.)
| File | Size |
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| Mratinkovic A. Engineering Statistics 2017.pdf | 9.5 MB |
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15.6 MB
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2025-06-06
| Uploaded by andryold1 | Size 15.6 MB | Health [ 41 /19 ] | Added 2025-06-06 |
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22.1 MB
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2025-12-02
| Uploaded by andryold1 | Size 22.1 MB | Health [ 104 /15 ] | Added 2025-12-02 |
NOTE
SOURCE: Mratinkovic A. Engineering Statistics 2017
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COVER
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MEDIAINFO
Textbook in PDF format Statistics is the mathematical science involved in the application of quantitative principles to the collection, analysis, and presentation of numerical data. The practice of statistics utilizes data from some population in order to describe it meaningfully, to draw conclusions from it, and make informed decisions. Today, statistics has become an important tool in the work of many academic disciplines such as medicine, psychology, education, sociology, engineering and physics, just to name a few. Statistics is also important in many aspects of society such as business, industry and government. Because of the increasing use of statistics in so many areas of our lives, it has become very desirable to understand and practice statistical thinking. This is important even if you do not use statistical methods directly. For example, civil engineers working in the transportation field are concerned about the capacity of regional highway systems. A typical problem would involve data on the number of nonwork, home-based trips, the number of persons per household, and the number of vehicles per household, and the objective would be to produce a trip-generation model relating trips to the number of persons per household and the number of vehicles per household. A statistical technique called regression analysis can be used to construct this model. The trip-generation model is an important tool for transportation systems planning. Regression methods are among the most widely used statistical techniques in engineering. Content Coverage Chapter One presents an overview of statistics that deals with collecting, processing, summarizing, analyzing and interpreting data. On the other hand, engineering and industrial management deal with such diverse issues as solving production problems, effective use of materials and labor, development of new products, quality improvement and reliability and, of course, basic research. The determinants of working capital investment needs and managing working capital under inflation are also highlighted. Chapter Two focuses on the role of statistics in engineering. Chapter Three describes the data collection methods, as well as estimating location and dispersion parameters are also presented. Chapter Four starts with probability concept and probability distributions. Moreover, law of total probability and Bayes’ theorem are also elaborated. Chapters Five and Chapter Six emphasize on simple linear regression and correlation and parameter estimation. Chapter Seven focuses on multiple regression that is an extension of simple linear regression. It is used to predictthe value of a variable based on the value of two or more other variables. Chapter Eight explores the approaches of variance in engineering statistics. The following are the salient features of the book: Provides engineers and undergraduate students all the essential statistical methods and techniques used in day-to-day engineering work. Topics are logically arranged according to the course outline of the subject. The use of simple language to facilitate a better understanding of each chapter. Illustrative examples of varying difficulties, wherever needed, are presented to enhance the interest of students. The provision of knowledge check test for each chapter is given at the end of chapter to strengthen the learning process of students. Case Study, Role Model, Keywords, and Examples are also provided to enrich the knowledge of students
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