Biostatistics Module #9 Quiz

Introduction to Biostatistics

• Statistics involve collection, organization, presentation, analysis, and interpretation of quantitative data.
• Biostatistics applies statistical methods specifically to biological and medical questions.

Data Collection and Analysis

• Data collection methods include interviews, questionnaires, experiments, observations, and documentary analysis.
• Presentation of data can be done through tables that may display frequency distributions in one or two dimensions.
• Graphical data presentation includes bar graphs, frequency polygons, and pie charts.
• Data analysis involves describing data properties and investigating correlations among variables.

Interpretation of Data

• Interpretation is based on data analysis and involves testing the significance of results obtained from statistical methods.

History and Development of Statistics

• The modern science of statistics has been shaped by four centuries of active research.
• John Graunt made significant early observations on mortality rates in 1662.
• Abraham de Moivre formulated the normal distribution in 1773; Carl Gauss derived it through measurement error studies.
• Key figures include:
  • Sir Francis Galton: Developed regression and correlation theories.
  • Adolf Quetelet: Known as the "Father of Modern Statistics," emphasized the importance of statistics in various sciences.
  • William S. applied statistical principles to estimate population data.

Fields of Application for Statistics

• Public Health: Analyzing disease prevalence in populations.
• Education: Studying effectiveness of teaching methods and student enrollment.
• Business: Evaluating sales trends and inventory management.
• Agriculture: Optimizing crop varieties and resources.
• Demography: Studying population dynamics such as birth and death rates.
• Economics: Measuring labor supply, resources, and market behavior.
• Environmental Studies: Investigating health impacts near industrial sites.
• Government: Managing statistical resources related to taxes and demographics.

Types of Variables

• Qualitative Variables: Non-numeric data representing categories (e.g., sex, birthplace).
• Quantitative Variables: Numeric data that can be measured or counted (e.g., height, weight).
  • Discrete Variables: Countable values (e.g., number of students, deaths).
  • Continuous Variables: Any value within an interval (e.g., temperature, time).

Measurement Scales

• Interval Scales: Numeric values with equal intervals between them (e.g., temperature).
• Continuous data allows expressions in decimal or fractional form.

Dependent and Independent Variables

• Dependent Variable: The outcome variable targeted for prediction.
• Independent Variable: The predictor variable that influences the dependent variable.
• Understanding variable relationships is key for statistical modeling and prediction.