HD and Dean's List Full Course Notes

Description

HD and Dean's List Extensive Complete Course Notes. Covering every topic and lecture for the whole term. Topics covered: Week 1 (Problems and Evidence-Based Problem Solving): The BPPS 7-step framework (Scoping, Analysing, Deciding), bounded rationality (Herbert Simon), cognitive biases (framing, availability, confirmation, representative, anchoring), satisficers vs maximisers, evidence vs data, five sources of evidence, the medical diagnosis probability example with base rate neglect. Week 2 (Problem Articulation and Disaggregation): Logic trees, MECE (mutually exclusive, collectively exhaustive), factor logic trees and prioritisation, types of statistical data (qualitative, quantitative, cross-sectional, time series), measures of central tendency (mean, median, mode), measures of variability (range, variance, standard deviation, coefficient of variation), Z-scores, histograms, bar charts, pie charts, covariance and correlation coefficient, scatter plots. Week 3 (Frameworks for Ethical Decision Making): Definitions of morality, ethics, business ethics, rights and justice, three dimensions of ethical thinking (moral awareness, judgement, behaviour), four ethics frameworks (consequentialism and utilitarianism, deontology and Kantian ethics, care ethics, virtue ethics), types of justice (distributive, procedural, interactional), Rawls' veil of ignorance, 7-step ethical decision-making framework, James Hardie Industries case study. Week 4 (Understanding Problems: Fact Gathering): Evidence-based problem solving criteria, data vs evidence distinction, evidence-gathering cycle, heuristics (Occam's Razor, rough order of magnitude, 80/20 thinking, break-even analysis), correlation and scatter plots, technology and GDP case study, simple linear regression (intercept, slope, residuals), confoundment and omitted variable bias, Melbourne property price example, multiple regression, binary and dummy variables, randomised controlled trials (RCTs). Week 5 (Patterns, Biases and Hypothesising Effects): Pattern recognition (System 1 vs System 2 thinking), thematic analysis (6-step process), language for communicating probabilistic findings, probability fundamentals (sample space, events, complement, addition rule, conditional probability, independence, Bayes' Rule), animal shelter worked example, Type I and Type II errors, statistical biases (sampling, survivorship, recall, omitted variable, algorithmic), binomial distribution with beer advertising case study, normal distribution (.7 rule, standard normal, Z-score probability calculations). Week 7 (Analysing the Issues: Identifying Causes): Root cause analysis principles, fishbone (Ishikawa) diagram with furniture store example, research design types and their causal inference capacity (cross-sectional, time series, quasi-experiment, RCT), reverse causality, sampling distributions, population parameters vs sample statistics, unbiasedness and standard error, Central Limit Theorem, confidence intervals (formula, interpretation, key properties), confidence intervals for regression coefficients. Week 8 (Analysing the Issues: With Limited Evidence): Limitations of organisational data and expert evidence, internal validity, external validity, reliability, formal hypothesis testing (null and alternative hypotheses, significance level, test statistic, p-value, three equivalent testing methods), furniture store regression worked example, the t-distribution and degrees of freedom, statistical significance vs economic/practical significance (WHO processed meat example), multiple regression inference, sensitivity analysis. Week 9 (Problem Solving and Making Decisions): Logic trees as decision support tools, profit tree structure with worked numbers, prioritisation matrix (impact vs ability to influence), brainstorming process and rules, brainwriting 6-3-5 method, communicating statistical results honestly (mean vs median, absolute vs relative numbers, y-axis framing), language checklist (estimate vs prove, likely vs definitely, relationship vs causation), best practice checklist for presenting empirical work. Week 10 (Evaluation, Communication and Course Synthesis): Full BPPS toolkit synthesis table (critical thinking, ethics and statistical tools by phase), evaluating empirical work checklist (problem, data, analysis, conclusions), kidney cancer example and small sample size pitfalls, confirmation bias in hypothesis testing, survivorship bias in business and finance, replication vs reproduction, Milton Friedman on evidence accumulation, 5-step EBPS progress report.