http://www.loc.gov/mods/v3 http://www.loc.gov/standards/mods/v3/mods-3-2.xsd
a gentle introduction for future data scientists
1 online resource (viii, 141 pages) : illustrations
"This book introduces the central concepts of modern data science, beginning with an introduction to simple statistical concepts such as mean and variance together with related methods for presenting and summarising data. The next chapters present the laws of probability, the probability distributions most commonly used to summarise data, the methods for estimating the parameters of these distributions, and the extent to which we can be confident concerning those estimated values. The next chapters are concerned with the relationships between two variables and the selection of models that involve several variables. The final chapter investigates the various methods that are used in dividing a population into distinct classes. The many worked examples include a study of the fate of those on the Titanic, and the factors affecting babies being born underweight. The book demands no particular software familiarity, since the methods presented can be implemented in Python, R (used by the authors), or any other statistical software"--Publisher's description.
Cover -- Titlepage -- Copyright -- Contents -- Preface -- 1 First steps -- 1.1 Types of data -- 1.2 Sample and population -- 1.2.1 Observations and random variables -- 1.2.2 Sampling variation -- 1.3 Methods for sampling a population -- 1.3.1 The simple random sample -- 1.3.2 Cluster sampling -- 1.3.3 Stratified sampling -- 1.3.4 Systematic sampling -- 1.4 Oversampling and the use of weights -- 2 Summarizing data -- 2.1 Measures of location -- 2.1.1 The mode -- 2.1.2 The mean -- 2.1.3 The trimmed mean -- 2.1.4 The Winsorized mean -- 2.1.5 The median -- 2.2 Measures of spread
2.2.1 The range -- 2.2.2 The interquartile range -- 2.3 Boxplot -- 2.4 Histograms -- 2.5 Cumulative frequency diagrams -- 2.6 Step diagrams -- 2.7 The variance and standard deviation -- 2.8 Symmetric and skewed data -- 3 Probability -- 3.1 Probability -- 3.2 The rules of probability -- 3.3 Conditional probability and independence -- 3.4 The total probability theorem -- 3.5 Bayes' theorem -- 4 Probability distributions -- 4.1 Notation -- 4.2 Mean and variance of a probability distribution -- 4.3 The relation between sample and population -- 4.4 Combining means and variances
4.5 Discrete uniform distribution -- 4.6 Probability density function -- 4.7 The continuous uniform distribution -- 5 Estimation and confidence -- 5.1 Point estimates -- 5.1.1 Maximum likelihood estimation (mle) -- 5.2 Confidence intervals -- 5.3 Confidence interval for the population mean -- 5.3.1 The normal distribution -- 5.3.2 The Central Limit Theorem -- 5.3.3 Construction of the confidence interval -- 5.4 Confidence interval for a proportion -- 5.4.1 The binomial distribution -- 5.4.2 Confidence interval for a proportion (large sample case)
6.3.1 Do the two samples come from the same population? -- 6.3.2 Do the two populations have the same mean? -- 7 Comparing proportions -- 7.1 The 2 2 table -- 7.2 Some terminology -- 7.2.1 Odds, odds ratios, and independence -- 7.2.2 Relative risk -- 7.2.3 Sensitivity, specificity, and related quantities -- 7.3 The R C table -- 7.3.1 Residuals -- 7.3.2 Partitioning -- 8 Relations between two continuous variables -- 8.1 Scatter diagrams -- 8.2 Correlation -- 8.2.1 Testing for independence -- 8.3 The equation of a line -- 8.4 The method of least squares -- 8.5 A random dependent variable, Y
Includes bibliographical references and index.
Oxford, United Kingdom : Oxford University Press, 2023