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

By the end of this course, participants will be able to:

·        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

This course is designed for:

·        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