Training Course in Statistics and Statistical Reporting |Smart

Overview

Why Attend

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.

This course 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.

Course Objectives

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

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

Course Outline

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

Schedule & Fees

2026 Schedule & Fees

29 Nov - 3 Dec, 2026

English

Per participant USD 2,900

29 Nov - 3 Dec, 2026

Arabic

Per participant USD 2,900

20 - 24 Dec, 2026

English

Per participant USD 2,900

20 - 24 Dec, 2026

Arabic

Per participant USD 2,900

2027 Schedule & Fees

28 Feb - 4 Mar, 2027

English

Per participant USD 2,900

28 Feb - 4 Mar, 2027

Arabic

Per participant USD 2,900

21 - 25 Mar, 2027

English

Per participant USD 2,900

21 - 25 Mar, 2027

Arabic

Per participant USD 2,900

30 May - 3 Jun, 2027

English

Per participant USD 2,900

30 May - 3 Jun, 2027

Arabic

Per participant USD 2,900

20 - 24 Jun, 2027

English

Per participant USD 2,900

20 - 24 Jun, 2027

Arabic

Per participant USD 2,900

29 Aug - 2 Sep, 2027

English

Per participant USD 2,900

29 Aug - 2 Sep, 2027

Arabic

Per participant USD 2,900

19 - 23 Sep, 2027

English

Per participant USD 2,900

19 - 23 Sep, 2027

Arabic

Per participant USD 2,900

28 Nov - 2 Dec, 2027

English

Per participant USD 2,900

28 Nov - 2 Dec, 2027

Arabic

Per participant USD 2,900

19 - 23 Dec, 2027

English

Per participant USD 2,900

19 - 23 Dec, 2027

Arabic

Per participant USD 2,900