One of the most popular introductory texts in its field, Statistics for Technology: A Course in Applied Studies presents the range of statistical methods commonly used in science, social science, and engineering. The mathematics are simple and straightforward; statistical concepts are explained carefully; and real-life (rather than contrived) examples are used throughout the chapters. Divided into three parts, the Introduction describes some simple methods of summarizing data. Theory examines the basic concepts and theory of statistics. Applications covers the planning and procedures of experiments, quality control, and life testing. Revised throughout, this Third Edition places a higher priority on the role of computers in analysis, and many new references have been incorporated. A new appendix describes general methods of tackling statistical problems, including guidance on literature searching and report writing.
Author(s): Chatfield, Chris
Series: Chapman and Hall/CRC Texts in Statistical Science
Edition: 3rd ed
Publisher: Routledge
Year: 2018
Language: English
Pages: 385
City: Boca Raton
Tags: Applied Statistics, Statistics For Technology
Content: Cover
Half Title
Title Page
Copyright Page
Table of Contents
Preface
Part One: Introduction
1: Outline of statistics
2: Simple ways of summarizing data
2.1 Introduction
2.2 Graphical methods
2.3 Summary statistics
Part Two: Theory
3: The concept of probability
3.1 Probability and statistics
3.2 Some definitions
3.3 Types of events
3.4 Permutations and combinations
3.5 Random variables
4: Discrete distributions
4.1 The discrete probability distribution
4.2 The binomial distribution
4.3 The binomial model
4.4 Types of distribution 4.5 The mean and variance of the binomial distribution4.6 The Poisson distribution
4.7 Bivariate discrete distributions
5: Continuous distributions
5.1 Definitions
5.2 The mean and variance of continuous distributions
5.3 The normal distribution
5.4 Uses of the normal distribution
5.5 Normal probability paper
5.6 The exponential distribution
5.7 Bivariate continuous distributions
6: Estimation
6.1 Point and interval estimates
6.2 Properties of the expected value
6.3 The sampling distribution of x̅
6.4 The sampling distribution of s2
6.5 Some properties of estimators 6.6 General methods of point estimation6.7 Interval estimation
7: Significance tests
7.1 Introduction
7.2 Tests on a sample mean
7.3 Comparing two sample means
7.4 The t-test applied to paired comparisons
7.5 The X2 goodness-of-fit test
7.6 The F-test
7.7 Distribution-free or non-parametric tests
7.8 Power and other considerations
8: Regression and correlation
8.1 Scatter diagram
8.2 Curve fitting
8.3 Regression
8.4 Confidence intervals and significance tests in linear regression
8.5 The coefficient of determination
8.6 Multiple and curvilinear regression 8.7 Orthogonal polynomials8.8 The design of regression experiments
8.9 The correlation coefficient
8.10 Estimating the regression lines
8.11 The bivariate normal distribution
8.12 Interpretation of the correlation coefficient
Part Three: Applications
9: Planning the experiment
9.1 Preliminary remarks
9.2 Measurements
9.3 The propagation of error
9.4 Improving precision with series and parallel arrangements
9.5 Combining dissimilar estimates by the method of least squares
10: The design and analysis of experiments --
1 Comparative experiments 10.1 Some basic considerations in experimental design10.2 A mathematical model for simple comparative experiments
10.3 The number of replications
10.4 Randomization
10.5 The analysis of a randomized comparative experiment
10.6 The range test
10.7 One-way analysis of variance
10.8 Follow-up study of the treatment means
10.9 Verifying the model
10.10 The randomized block experiment
10.11 Two-way analysis of variance
10.12 Latin squares
10.13 Balanced incomplete block designs
11: The design and analysis of experiments --
2 Factorial experiments
11.1 Introduction
