This is a textbook for an undergraduate course in statistics for engineers with a minimal calculus prerequisite. The second edition differs from existing books in three main aspects: it is the only introductory statistics textbook written for engineers that uses R throughout the text, there is an emphasis on statistical methods most relevant to
This is a textbook for an undergraduate course in statistics for engineers with a minimal calculus prerequisite. The second edition differs from existing books in three main aspects: it is the only introductory statistics textbook written for engineers that uses R throughout the text, there is an emphasis on statistical methods most relevant to
Andrew Metcalfe, David Green, Andrew Smith, and Jonathan Tuke have taught probability and statistics to students of engineering at the University of Adelaide for many years and have substantial industry experience. Their current research includes applications to water resources engineering, mining, and telecommunications. Mahayaudin Mansor worked in banking and insurance before teaching statistics and business mathematics at the Universiti Tun Abdul Razak Malaysia and is currently a researcher specializing in data analytics and quantitative research in the Health Economics and Social Policy Research Group at the Australian Centre for Precision Health, University of South Australia. Tony Greenfield, formerly Head of Process Computing and Statistics at the British Iron and Steel Research Association, is a statistical consultant. He has been awarded the Chambers Medal for outstanding services to the Royal Statistical Society; the George Box Medal by the European Network for Business and Industrial Statistics for Outstanding Contributions to Industrial Statistics; and the William G. Hunter Award by the American Society for Quality. Visit their website here: http://www.maths.adelaide.edu.au/david.green/BookWebsite
Inhaltsangabe
I Foundations 1. Why Understand Statistics? Introduction Using the book Software 2. Probability and Making Decisions Introduction Random digits Concepts and uses Generating random digits Pseudo random digits Defining probabilities Defining probabilities {Equally likely outcomes Defining probabilities {relative frequencies Defining probabilities {subjective probability and expected monetary value Axioms of Probability The addition rule of probability Complement Conditional probability Conditioning on information Conditional probability and the multiplicative rule Independence Tree diagrams Bayes' theorem Law of total probability Bayes' theorem for two events Bayes' theorem for any number of events Decision trees Permutations and combinations Simple random sample Summary Notation Summary of main results MATLAB and R commands Exercises 3. Graphical Displays of Data and Descriptive Statistics Types of variables Samples and populations Displaying data Stem-and-leaf plot Time series plot Pictogram Pie chart Bar chart Rose plot Line chart for discrete variables Histogram and cumulative frequency polygon for continuous variables Pareto chart Numerical summaries of data Population and sample Measures of location Measures of spread Box-plots Outlying values and robust statistics Outlying values Robust statistics Grouped data Calculation of the mean and standard
I Foundations 1. Why Understand Statistics? Introduction Using the book Software 2. Probability and Making Decisions Introduction Random digits Concepts and uses Generating random digits Pseudo random digits Defining probabilities Defining probabilities {Equally likely outcomes Defining probabilities {relative frequencies Defining probabilities {subjective probability and expected monetary value Axioms of Probability The addition rule of probability Complement Conditional probability Conditioning on information Conditional probability and the multiplicative rule Independence Tree diagrams Bayes' theorem Law of total probability Bayes' theorem for two events Bayes' theorem for any number of events Decision trees Permutations and combinations Simple random sample Summary Notation Summary of main results MATLAB and R commands Exercises 3. Graphical Displays of Data and Descriptive Statistics Types of variables Samples and populations Displaying data Stem-and-leaf plot Time series plot Pictogram Pie chart Bar chart Rose plot Line chart for discrete variables Histogram and cumulative frequency polygon for continuous variables Pareto chart Numerical summaries of data Population and sample Measures of location Measures of spread Box-plots Outlying values and robust statistics Outlying values Robust statistics Grouped data Calculation of the mean and standard
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