Numbers on their own do not make decisions — people do, and they can only decide well when the data in front of them has been collected properly, analysed correctly, and written up in a report they can actually understand. That gap, between raw data and a decision someone can act on, is what this course on statistics and statistical reporting is built to close.

Statistical agencies already produce enormous volumes of data, information, and indicators; the real value only shows up when that output supports decision-makers in planning, tracking progress, evaluating performance, and keeping the public, researchers, and other users informed about how society, the economy, and government are actually performing. Arab British Fellowship Training Academy designed this course to build exactly those skills: turning raw statistics into management and financial decisions grounded in accurate, essential information.

Who Should Attend?

• Statistical analysts and data analysis specialists in public and private institutions — across fields such as education, health, and media.
• Department managers and teams who rely on statistical reports for performance improvement and decision-making.
• Anyone working with statistical data who wants to improve their report-writing skills.
• Students and graduates aiming to build a career in analysis and statistics.

Knowledge and Benefits:

After completing the program, participants will be able to master the following:

• Grasp the fundamentals of statistics and apply them to real data analysis.
• Use a range of statistical tools to analyse data and produce comprehensive, accurate reports.
• Interpret statistical results and present them professionally to a non-technical audience.
• Apply statistical methods across different fields — business, education, and healthcare among them.

Module 1: Statistical Foundations

• Definition of statistics and its role in data analysis, the difference between descriptive and inferential statistics, and its applications across fields.
• Qualitative and quantitative data, measures of central tendency (mean, median, mode), and measures of dispersion (variance, standard deviation).
• Methods of data collection (surveys, interviews, experiments), organising data with tables, and tools for data cleaning.

Module 2: Describing Data Visually

• Creating frequency distributions, and bar and pie charts.
• Interpreting graphs in statistical reports.
• Calculating standard deviation and variance, and the importance and practical applications of dispersion measures.

Module 3: From Description to Inference

• The difference between descriptive and inferential statistics in context, and basic concepts of statistical tests.
• Formulating and testing hypotheses using T-tests and Z-tests, and why hypothesis testing matters in reporting.
• Simple and representative sampling, estimating the mean and standard deviation from samples, and the importance of sampling in decision-making.

Module 4: Distributions That Matter

• Understanding the normal distribution and its properties, and converting data to the standard Z-distribution.
• Practical applications of the normal distribution.
• Using the Poisson distribution and the binomial distribution, and comparing different distributions.

Module 5: Regression, Forecasting and Model Accuracy

• The definition and importance of regression in analysing relationships between variables, and simple linear versus multiple regression.
• Forecasting methods based on regression, forecasting tools in Excel and other software, and using forecasting in decision-making.
• Evaluating statistical models using R², improving model accuracy, and validating results.

Module 6: Software Toolkit — Excel, SPSS and R

• Overview of statistical tools — Excel, SPSS, R — and how to choose the right one.
• Using statistical functions, charts, and pivot tables in Excel.
• Working with data, analysing and interpreting results, and comparing outputs across SPSS and R.

Module 7: Writing Reports That Get Used

• The importance of accurate statistical reporting, the basic structure of a report, and its target audiences.
• Presenting data logically with charts and tables, using descriptive and statistical methods effectively.
• Writing reports that include regression results, presenting them clearly, and highlighting recommendations from the analysis.

Module 8: Working With Qualitative Data

• The difference between qualitative and quantitative data, and methods of collecting and analysing qualitative data.
• Integrating quantitative and qualitative analysis within the same report, with supporting case studies.
• Interpreting qualitative results, preparing comprehensive reports that include them, and addressing the challenges qualitative data brings.

Module 9: Time Series — Reading the Past to Forecast the Future

• The basic characteristics of time-series data and its importance in business, with practical applications.
• Forecasting methods based on time-series data, and analysing trends and patterns.
• Using forecasting models in decision-making.