Business Analytics 1st Edition - Applied Modelling and Prediction

AcknowledgementsAbout the authorPrefaceOnline resources1 Decision-making under uncertainty1.1 Introduction1.2 Uncertainty: friend or foe?1.3 Modern decision-making1.4 Advances in technology1.5 Data: the new oil?1.6 Qualitative versus quantitative analysis1.7 Uncertainty in the news1.8 Simplicity versus complexity – the need for models1.9 Model assumptions1.10 To switch, or not to switch?1.11 Chapter overview1.12 Key vocabulary1.13 Suggested reading1.14 Exercises2 Descriptive statistics2.1 Introduction2.2 Qualitative and quantitative variables2.3 Continuous and discrete variables2.4 The sample distribution2.5 Measures of central tendency2.6 Measures of dispersion2.7 Summary of common Excel functions2.8 Chapter overview2.9 Key vocabulary2.10 Datasets2.11 Exercises2.12 Proofs3 Data visualisation3.1 Introduction3.2 Getting started3.3 On your marks, get set, viz!3.4 Show Me (what works!)3.5 Adding and editing text and dynamic titles3.6 Maps3.7 Calculated fields and parameters3.8 Dig deeper3.9 Chapter overview3.10 Key vocabulary3.11 Exercises4 Probability4.1 Introduction4.2 Quantifying probabilities4.3 The concept of probability4.4 Combinatorial theory: counting4.5 Relative frequency4.6 Randomness4.7 Properties of probability4.8 Conditional probability and Bayes’ theorem4.9 Chapter overview4.10 Key vocabulary4.11 Exercises5 Probability distributions5.1 Introduction5.2 Random variables5.3 Probability distribution5.4 Binomial distribution5.5 Cumulative distribution functions5.6 Poisson distribution5.7 Poisson approximation to the binomial5.8 Expected value of a discrete random variable5.9 Variance of a discrete random variable5.10 Distributions related to the binomial distribution5.11 What is a continuous random variable?5.12 Probability density function and cumulative distribution function5.13 (Continuous) uniform distribution5.14 Exponential distribution5.15 Normal distribution5.16 Normal approximation to the binomial5.17 Summary of probability distributions5.18 Chapter overview5.19 Key vocabulary5.20 Exercises5.21 Proofs6 Decision tree analysis and game theory6.1 Introduction6.2 To advertise, or not to advertise?6.3 To drill, or not to drill?6.4 Risk attitudes6.5 Putting a price on information6.6 Game theory6.7 Cournot competition6.8 Bertrand competition6.9 Stackelberg leadership6.10 Chapter overview6.11 Key vocabulary6.12 Exercises7 Sampling and sampling distributions7.1 Introduction7.2 Sampling7.3 Classification of sampling techniques7.4 Non-random sampling techniques7.5 Random sampling techniques7.6 Sampling distributions7.7 Sampling distribution of the sample mean7.8 Sampling distribution of the sample proportion7.9 Sampling distribution of the sample variance7.10 Chapter overview7.11 Key vocabulary7.12 Exercises8 Opinion research8.1 Introduction8.2 Measurement and scaling8.3 Levels of measurement8.4 Scaling techniques8.5 Chapter overview8.6 Key vocabulary8.7 Exercises9 Estimation9.1 Introduction9.2 Estimation criteria: bias, variance and mean squared error9.3 Unbiased estimators9.4 Interval estimation9.5 General formulae for normally distributed statistics9.6 Confidence interval for a single proportion9.7 Sample size determination9.8 Difference between two population proportions9.9 Difference between two population means9.10 Summary of common Excel functions9.11 Chapter overview9.12 Key vocabulary9.13 Exercises9.14 Proof10 Hypothesis testing10.1 Introduction10.2 Statistical juries10.3 Type I and Type II errors10.4 p-values, effect size and sample size influences10.5 Testing a population mean claim10.6 Hypothesis test for a single mean (σ2 known)10.7 Hypothesis test for a single mean (σ2 unknown)10.8 Hypothesis test for a single proportion10.9 Difference between two population proportions10.10 Difference between two population means10.11 Summary of common Excel functions10.12 Chapter overview10.13 Key vocabulary10.14 Exercises11 Bivariate analysis11.1 Introduction11.2 Cross-tabulation analysis11.3 Side-by-side box plots11.4 Correlation (is not causation!)11.5 Chapter overview11.6 Key vocabulary11.7 Exercises12 Analysis of variance12.1 Introduction12.2 Testing for equality of three population means12.3 One-way analysis of variance12.4 From one-way to two-way ANOVA12.5 Two-way analysis of variance12.6 Chapter overview12.7 Key vocabulary12.8 Exercises13 Linear regression13.1 Introduction13.2 Parameter estimation13.3 Analysis of variance – regression style13.4 A general test for the effect of a variable13.5 Hypothesis testing and confidence intervals for regression model parameters13.6 Prediction13.7 Elasticities13.8 Tests of assumptions and robustness13.9 Chapter overview13.10 Key vocabulary13.11 Exercises14 Multiple regression14.1 Introduction14.2 Visualising relationships between three variables14.3 The multiple linear regression model14.4 To include, or not to include? The perils of variable misspecification14.5 Interactions14.6 Dummy variables14.7 Multicollinearity14.8 Model selection techniques14.9 Case study: Gender discrimination in pay14.10 Chapter overview14.11 Key vocabulary14.12 Exercises14.13 Optional material on multiple linear regression model statistical inference15 Time-series analysis and forecasting15.1 Introduction15.2 Classifying forecasts15.3 Decomposing a time series15.4 Assessing forecast accuracy15.5 Trend-based time-series models15.6 Forecasting in Tableau15.7 Chapter overview15.8 Key vocabulary15.9 Exercises16 Discriminant analysis16.1 Introduction16.2 Discriminant analysis model16.3 Two-group discriminant analysis: empirical example16.4 Three-group discriminant analysis: empirical example16.5 Chapter overview16.6 Key vocabulary16.7 Exercises17 Factor analysis17.1 Introduction17.2 Factor analysis model17.3 Factor analysis: full empirical workthrough17.4 Factor analysis: further example17.5 Chapter overview17.6 Key vocabulary17.7 Exercises18 Cluster analysis18.1 Introduction18.2 Cluster analysis18.3 Clustering procedures18.4 Cluster analysis: full empirical example18.5 Cluster analysis: further example18.6 Chapter overview18.7 Key vocabulary18.8 Exercises19 Constrained optimisation models19.1 Introduction19.2 Optimisation models19.3 Product mix19.4 Sensitivity analysis19.5 Conjoint analysis19.6 Chapter overview19.7 Key vocabulary19.8 Exercises20 Monte Carlo simulation20.1 Introduction20.2 Basics of simulation20.3 Probability distributions for input variables20.4 Pseudo-random number generators20.5 Simulating from common probability distributions in Excel20.6 Simulation case study20.7 Chapter overview20.8 Key vocabulary20.9 ExercisesAppendix A: Common Excel functionsA.1 Using Excel functionsA.2 Descriptive statisticsA.3 Discrete distributionsA.4 Continuous distributionsA.5 Statistical inferenceAppendix B: Compendium of probability distributionsB.1 Discrete uniform distributionB.2 Bernoulli distributionB.3 Binomial distributionB.4 Poisson distributionB.5 Geometric distributionB.6 Continuous uniform distributionB.7 Exponential distributionB.8 Normal distributionIndex