Statistical agencies produce large volumes of data, information, and indicators to make their statistical outputs available to the public at various levels.

It is important to note that the purpose of the statistical system is to provide high-quality, timely data and information to support decision-makers and policymakers for planning, monitoring progress, evaluating performance, and informing the public, researchers, and all users about the performance of society, the economy, and the government.

The British Academy for Training and Development offers this course in Statistics and Statistical Reporting to enhance statisticians' skills in data analysis and report writing, which helps in decision-making and developing management and financial plans based on accurate and essential information to improve organizational performance.

Who Should Attend?

• Statistical analysts in public and private institutions

• Data analysis specialists in various fields such as education, health, and media

• Individuals working with statistical data who wish to improve their reporting skills

• Department managers and teams that rely on statistical reports for performance improvement and decision-making

• Students and graduates aspiring to work in analysis and statistics fields

Knowledge and Benefits:

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

• Understand the fundamentals of statistics and how to use it in data analysis

• Prepare comprehensive and accurate statistical reports

• Develop skills in using various statistical tools for data analysis

• Strengthen their ability to interpret statistical results and present them professionally

• Apply statistical methods in different fields such as business, education, and healthcare

• Concept of Statistics and Its Importance

  • Definition of statistics and its role in data analysis

  • Difference between descriptive and inferential statistics

  • Applications of statistics across various fields

• Types of Data and Statistical Measures

  • Qualitative and quantitative data

  • Measures of central tendency (mean, median, mode)

  • Measures of dispersion (variance, standard deviation)

• Data Collection and Organization

  • Methods of data collection (surveys, interviews, experiments)

  • Organizing data using tables

  • Tools and techniques for data cleaning

• Frequency Distributions and Graphs

  • Creating frequency distributions

  • Bar charts and pie charts

  • Interpreting graphs in statistical reports

• Measures of Dispersion and Variability

  • Calculating standard deviation and variance

  • Importance of dispersion measures in statistics

  • Practical applications of dispersion measures

• Introduction to Inferential Statistics

  • Difference between descriptive and inferential statistics

  • Using inferential statistics to interpret data

  • Basic concepts of statistical tests

• Hypotheses and Hypothesis Testing

  • Formulating and testing hypotheses

  • Hypothesis testing using T-tests and Z-tests

  • Importance of hypothesis testing in statistical reporting

• Inference Using Samples

  • Simple and representative sampling

  • Estimating the mean and standard deviation from samples

  • Importance of sampling in decision-making

• Normal and Standard Distributions

  • Understanding the normal distribution and its properties

  • Converting data to the standard Z-distribution

  • Practical applications of the normal distribution

• Other Distributions (Poisson, Binomial)

  • Using the Poisson distribution in data analysis

  • Applications of the binomial distribution

  • Comparison among different distributions

• Concept of Regression Analysis

  • Definition of regression and its importance in analyzing variable relationships

  • Types of regression analysis: simple linear and multiple regression

  • Practical business applications of regression

• Forecasting Using Statistical Data

  • Forecasting methods based on regression

  • Forecasting tools in Excel and other software

  • Using forecasting in future decision-making

• Testing Statistical Model Accuracy

  • Evaluating statistical models using R²

  • Improving model accuracy

  • Validating results and ensuring accuracy of predictions

• Introduction to Statistical Software

  • Overview of statistical tools: Excel, SPSS, R

  • Features of each tool and how to choose the right one

  • Hands-on exercises using software

• Using Excel in Statistical Analysis

  • Using statistical functions in Excel

  • Creating charts and performing analysis

  • Using pivot tables for analysis

• Training on SPSS and R

  • Working with data using SPSS and R

  • Data analysis and interpretation of results

  • Comparing results across different tools

• Introduction to Statistical Report Writing

  • Importance of accurate statistical reporting

  • Basic structure of a statistical report

  • Target audiences of statistical reports

• Organizing Data in Reports

  • Presenting data in a logical and easy-to-understand format

  • Using charts and tables effectively

  • Employing descriptive and statistical methods in presenting data

• Regression and Forecasting Report Preparation

  • Writing reports that include regression results

  • Presenting results in a clear and interpretable way

  • Highlighting recommendations derived from the analysis

• Collecting and Analyzing Qualitative Data

  • Difference between qualitative and quantitative data

  • Methods of collecting and analyzing qualitative data

  • Techniques for classifying and analyzing qualitative data using software

• Techniques for Combining Quantitative and Qualitative Analysis

  • Integrating both analysis types in reports

  • Importance of combining qualitative and quantitative data in decision-making

  • Case studies and practical applications

• Interpreting Qualitative Results in Reports

  • How to interpret and analyze qualitative data

  • Preparing comprehensive reports that include qualitative findings

  • Addressing challenges in using qualitative data

• Introduction to Time Series Analysis

  • Basic characteristics of time-series data

  • Importance of time-series analysis in business

  • Practical applications of time-series analysis

• Forecasting Using Time-Series Data

  • Forecasting methods based on time-series data

  • Analyzing trends and patterns

  • Using forecasting models in decision-making