Introduction and Overview

• The need for robust data in the decision-making process
• Defining key statistical terminology
• Identifying qualitative and quantitative data types
• Selecting the right charts to support decisions

Managing Decisions under Uncertainty

Assessing probable outcomes

• Determining events that impact your decisions
• Combining probabilities for a stronger estimate
• Weighing the probabilities of related and unrelated events

Planning a business strategy using payoff tables

• Building a payoff table from probabilities
• Applying expected values to determine the best outcome

Gaining competitive advantage

• Modelling a competitor's decision process
• Leveraging backward conditional probabilities
• Bolstering your estimates with public information

Supporting Decisions with Valid Data

Exploiting effective measuring techniques

• Interpreting data with evidence-based measures
• Ensuring the correct measures are used to structure results
• Improving the accuracy of your findings with key measures of dispersion

Bulletproofing decisions

• Validating your results by quantifying skewness
• Optimising your measures by removing outliers

Leveraging the power of distributions

• Calculating the probability of success
• Finding the probability of defects

Driving Your Decisions with Statistical Tests

Making informed decisions from a data subset

• Estimating key measures from a dataset
• Calculating ranges

Verifying decisions

• Deriving alternative possibilities
• Testing decisions using tangible data
• Selecting a one- or two-tailed test to guarantee accuracy

Assessing a performance improvement

• Collecting data to baseline the improvement
• Checking for changes in key performance indicators (KPIs)
• Mitigating error by choosing an appropriate confidence level

Answering Business Questions through Surveys

Gathering meaningful data

• Choosing the best type of survey
• Designing effective survey questions

Maximising the integrity of your data through probability-based sampling methods

• Random
• Stratified
• Systematic
• Cluster

Avoiding common mistakes in sampling

• Convenience
• Judgement

Ensuring data robustness

• Qualifying surveyed data by selecting the right sample size
• Deciding the required level of confidence
• Choosing the most cost-effective level of accuracy

Recognising Trends with Strong Forecasting Techniques

Evaluating the model that best predicts your data

• Linear
• Exponential
• Logarithmic
• Forecasting accurate projections
• Measuring the "goodness of fit"

Forecasting business developments

• Selecting the number of forecast periods
• Applying the trendline formula

Refining your forecast

• Discovering seasonality in your data
• Integrating seasonal factors for an improved forecast

Presenting Data and Conclusions Accurately

Pinpointing statistical quirks

• Uncovering techniques used to mislead
• Data dredging
• Biased samples

Preparing results for presentations

• Ensuring the main points are communicated
• Sidestepping common mistakes in charts