Ŀ    �part i  descriptive statisticsunit 1  statistics 31.1  what is statistics? 41.1.1  meanings of statistics 41.1.2  definition of statistics 51.1.3  types of statistics 61.1.4  applications of statistics 61.2  the language of statistics 91.2.1  population and sample 91.2.2  kinds of variables 111.3  measurability and variability 141.4  data collection 161.4.1  the data collection process 171.4.2  sampling frame and elements 181.5*  single-stage methods 211.5.1  simple random sample 211.5.2  systematic sample 221.6*  multistage methods 251.7*  types of statistical study 271.8  the process of a statistical study 31glossary 34reading english materials 35passage 1. what is statistics? 35passage 2. from data to foresight 35problems 36unit 2  descriptive analysis of single-variable data 402.1  graphs, pareto diagrams, and stem-and-leaf displays 412.1.1  qualitative data 412.1.2  quantitative data 432.2  frequency distributions and histograms 472.2.1  frequency distribution 472.2.2  histograms 512.2.3  cumulative frequency distribution and ogives 532.3  measures of central tendency 552.3.1  finding the mean 552.3.2  finding the median 562.3.3  finding the mode 572.3.4  finding the midrange 582.4  measures of dispersion 602.4.1  sample standard deviation 622.5  measures of position 642.5.1  quartiles 642.5.2  percentiles 642.5.3  other measures of position 662.6  interpreting and understanding standard deviation 702.6.1  the empirical rule and testing for normality 702.6.2  chebyshev’s theorem 72glossary 74problems 75unit 3  descriptive analysis of bivariate data 793.1  bivariate data 803.1.1  two qualitative variables 803.1.2  one qualitative and one quantitative variable 823.1.3  two quantitative variables 833.2  linear correlation 853.2.1  calculating the linear correlation coefficient, r 86    *3.2.2  causation and lurking variables 893.3  linear regression 913.3.1  line of best fit 923.3.2  making predictions 97reading english materials 99passage 1. the first regression 99passage 2. simpson’s paradox 99problems 100unit 4  introduction to probability 1044.1  sample spaces, events and sets 1054.1.1  introduction 1054.1.2  sample spaces 1054.1.3  events 1064.1.4  set theory 1084.2  probability axioms and simple counting problems 1094.2.1  probability axioms and simple properties 1094.2.2  interpretations of probability 1114.2.3  classical probability 1124.2.4  the multiplication principle 1134.3  permutations and combinations 1154.3.1  introduction 1154.3.2  permutations 1164.3.3  combinations 1184.3.4  the difference between permutations and combinations 1204.4  conditional probability and the multiplication rule 1224.4.1  conditional probability 1224.4.2  the multiplication rule 1234.5  independent events, partitions and bayes theorem 1244.5.1  independence 1244.5.2  partitions 1254.5.3  law of total probability 1264.5.4  bayes theorem 1264.5.5  bayes theorem for partitions 127reading english materials 130passage 1. probability and odds 130passage 2. the relationship between odds and probability 130passage 3. how the odds change across the range of the probability 131problems 132unit 5  discrete probability models 1345.1  introduction, mass functions and distribution functions 1355.1.1  introduction 1355.1.2  probability mass functions (pmfs) 1365.1.3  cumulative distribution functions (cdfs) 1375.2  expectation and variance for discrete random quantities 1385.2.1  expectation 1385.2.2  variance 1395.3  properties of expectation and variance 1405.3.1  expectation of a function of a random quantity 1405.3.2  expectation of a linear transformation 1405.3.3  expectation of the sum of two random quantities 1415.3.4  expectation of an independent product 1415.3.5  variance of an independent sum 1425.4  the binomial distribution 1425.4.1  introduction 1425.4.2  bernoulli random quantities 1435.4.3  the binomial distribution 1435.4.4  expectation and variance of a binomial random quantity 1455.5  the geometric distr