Who Should Attend?

• Healthcare professionals.
• Health management and epidemiological surveillance specialists.
• Medical researchers and statisticians.
• Students in fields such as public health, medicine, or health statistics.
• Health program managers and health planners.

Knowledge and Benefits:

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

• Understand the statistical foundations used in analyzing health data.
• Learn how to collect and analyze health data accurately.
• Master basic statistical methods to interpret health data results.
• Develop the skills needed to apply health statistics tools in scientific research.
• Enhance the ability to make data-driven decisions in the health field.

• Introduction to Health Statistics

  • Importance of statistics in public health and medicine
  • The role of statistics in health decision-making
  • The relationship between health statistics and health policies

• Types of Health Data

  • Qualitative data: Definition and examples
  • Quantitative data: Definition and measures used
  • Difference between discrete and continuous data

• Health Data Collection

  • Methods of data collection in health studies
  • Sampling techniques: Random and stratified sampling
  • The importance of data credibility and accuracy

• Basic Statistical Measures

  • Central tendency measures: Mean, median, and mode
  • Dispersion measures: Standard deviation and variance
  • Other measures like range, minimum, and maximum

• Types of Statistical Distributions

  • Normal distribution and Gaussian distribution
  • Characteristics of normal distribution in health data
  • Applications of statistical distributions in data analysis

• Probabilities and Their Applications in Health Statistics

  • Basics of probability theory
  • Application of probability in predicting health outcomes
  • Using probability for risk estimation

• Descriptive Analysis of Health Data

  • Summarizing data using tables and graphs
  • Importance of graphical representation in examining health trends
  • Using descriptive analysis to understand health issues

• Graphs in Health Statistics

  • Creating graphs using various chart types
  • Using graphs to analyze distributions
  • Choosing the appropriate graph to represent data

• Measurement Using Statistical Indicators

  • Using indicators like mortality rate and incidence rate
  • The importance of indicators in evaluating public health
  • How to accurately calculate statistical indicators

• Introduction to Statistical Tests

  • The importance of statistical tests in testing health hypotheses
  • Difference between simple and complex tests
  • Selecting the appropriate statistical test based on data type

• T-Test for Samples

  • Concept and use of the T-test in comparing samples
  • Applying T-test in analyzing differences between health groups
  • Interpreting T-test results

• Chi-Square (χ²) Test

  • Definition and uses of the Chi-square test in health data
  • Applying Chi-square in analyzing the relationship between variables
  • Interpreting Chi-square test results in health studies

• Introduction to Statistical Software

  • Introduction to common software like SPSS and R
  • How to input and analyze data using these tools
  • Reviewing statistical analysis results using software

• Statistical Reporting Using Software

  • How to create accurate statistical reports using software
  • Presenting analysis results clearly and understandably
  • Exporting results in different formats

• Comparison of Statistical Software

  • Comparing different tools used for analyzing health data
  • Choosing the most suitable tool based on the analysis type and data
  • Reviewing features and limitations of common software in health statistics

• Correlation Concept in Health Statistics

  • Definition of correlation and how to measure relationships between health variables
  • Using Pearson's correlation coefficient
  • Applications of correlation in determining relationships between health variables

• Simple Regression

  • Using simple regression to interpret the relationship between two variables
  • Understanding the coefficients in regression equations
  • Applications of simple regression in health analysis

• Multiple Regression

  • Difference between simple and multiple regression
  • Applying multiple regression to examine the effect of several variables on a dependent variable
  • Analyzing and interpreting multiple regression results

• Health Study Design

  • Differences between cross-sectional and longitudinal studies
  • Designing studies based on secondary data
  • Importance of sampling designs in improving result accuracy

• Epidemiological Study Analysis

  • Designing and analyzing epidemiological studies using statistical methods
  • Effective strategies for analyzing epidemiological data
  • Analyzing the impact of environmental and social factors on public health

• Clinical Study Analysis

  • Designing clinical studies and analyzing clinical data
  • Applying health statistics in assessing treatment efficacy
  • Using statistical analysis methods to evaluate clinical trial results

• Statistical Modeling in Public Health

  • Applying advanced statistical models in health studies
  • Analyzing multivariate data using complex models
  • Using statistical modeling to predict future health trends

• Predictive Techniques in Health Statistics

  • Using predictive methods to assess health risks
  • Modeling predictive data and analyzing health trends
  • Applying predictive models in healthcare prevention and management

• Big Data Analysis in Public Health

  • Handling big data in the health field
  • Techniques for analyzing large datasets in health research
  • Challenges of managing big data in health statistics

• Identifying Errors in Statistical Analysis

  • Understanding different types of statistical errors
  • How to minimize errors in data analysis
  • Correcting errors caused by sampling or computational mistakes

• Ensuring Result Accuracy

  • Analyzing the accuracy and reproducibility of statistical results
  • How to test the validity of statistical hypotheses
  • Using variance analysis to examine accuracy

• Data Validation Tests

  • Methods for testing data validity and ensuring reliability
  • Preliminary data checking before analysis
  • The importance of data cleaning and filtering before analysis

• Writing Statistical Reports

  • Techniques for writing scientific and statistical reports in health
  • Organizing reports systematically and clearly
  • The importance of presenting statistical data accurately

• Presenting and Interpreting Statistical Results

  • How to present analysis results to medical or general audiences
  • The importance of simplifying statistical results
  • Using graphs and tables to present findings