Cambridge
Academy
In London

Introduction

Statistical Analysis for Performance helps analysts separate real signals from normal variation, understand trends, and make practical forecasts for planning and decision-making. This hands-on program covers core statistical thinking, simple tools for trend and variability analysis, and forecasting methods that work with typical performance datasets.

Course Objectives

• Explain variation and why it matters for performance interpretation

• Apply simple descriptive statistics to summarize performance data

• Identify trends, seasonality, and outliers using practical methods

• Build basic forecasts and communicate uncertainty clearly

• Produce a simple analysis pack with insights and recommendations

Target Audience

• Performance measurement analysts and reporting officers

• Strategy, planning, and PMO teams

• Operations analysts and service performance leads

• BI and data teams supporting KPI analysis

• Anyone responsible for interpreting KPI trends and forecasts

Course Outlines

Day 1: Statistical Thinking for Performance Data

• Common performance data types: counts, rates, percentages, time series

• Signal vs noise: understanding normal variation

• Data preparation basics: missing values, duplicates, definitions, units

• Choosing the right summary measures for KPIs

• Activity: Review a KPI dataset and identify data issues

Day 2: Descriptive Statistics and Visual Exploration

• Measures of center: mean, median (when to use each)

• Measures of spread: range, IQR, standard deviation (simple meaning)

• Distributions and skew (why averages can mislead)

• Visual basics: line charts, histograms, box plots (interpretation)

• Workshop: Build a simple descriptive stats summary for 5 KPIs

Day 3: Variation Analysis and Control Charts (Practical)

• Common vs special cause variation (simple definitions)

• Run charts: rules for shifts, trends, and cycles

• Control charts overview and when to use them (high level)

• Spotting outliers and investigating root questions

• Activity: Apply run chart rules to identify meaningful change

Day 4: Trend and Driver Analysis

• Trend lines: moving averages and smoothing (practical use)

• Seasonality basics: monthly/weekly patterns and comparisons

• Correlation vs causation (common pitfalls)

• Simple driver analysis: segmenting by region, service, customer type

• Case study: Explain a performance change using segmentation and visuals

Day 5: Forecasting Basics and Communicating Uncertainty

• Forecasting purpose: planning, targets, capacity, risk awareness

• Simple methods: naïve forecast, moving average, exponential smoothing

• Forecast accuracy checks: error, bias, and back-testing (simple)

• Communicating uncertainty: ranges, assumptions, and limitations

• Activity: Produce a one-page KPI forecast pack with key insights