Unit 3096: Statistical Consulting Fundamentals

1. Introduction to Statistical Consulting

• Definition and scope of statistical consulting
• Roles and responsibilities of a statistical consultant
• Value proposition: benefits to businesses and research projects
• Ethical considerations and professional standards

2. Types of Statistical Consulting Services

• Study design consultation
• Data analysis and interpretation
• Report writing and documentation
• Data visualization and presentation
• Specialized consulting: clinical trials, market research, social sciences

3. Statistical Software Tools for Consulting

• Overview of popular tools:
  • R: programming and statistical computing
  • SAS: advanced analytics and business intelligence
  • SPSS: user-friendly interface for social sciences
  • Python: flexible data analysis and machine learning
• Applications of each tool in consulting scenarios
• Criteria for tool selection based on project needs

4. Study Design and Sampling Techniques

• Importance of study design in consulting
• Types of study designs:
  • Experimental vs observational
  • Cross-sectional, longitudinal, case-control
• Sampling methods:
  • Probability sampling (simple random, stratified, cluster)
  • Non-probability sampling (convenience, quota)
• Sample size determination and power analysis
• Common pitfalls and strategies to avoid bias

5. Data Cleaning and Preparation

• Importance of data quality and integrity
• Identifying and handling missing data
• Detecting and managing outliers
• Data transformation and normalization
• Validation and consistency checks

6. Exploratory Data Analysis (EDA)

• Purpose and benefits of EDA
• Summary statistics:
  • Measures of central tendency and dispersion
  • Frequency distributions
• Data visualization techniques:
  • Histograms, boxplots, scatterplots
  • Correlation matrices and heatmaps
• Identifying patterns, trends, and anomalies

7. Hypothesis Testing and Statistical Inference

• Hypothesis formulation: null and alternative
• Types of errors: Type I and Type II
• Common tests:
  • t-tests, chi-square tests, ANOVA
• Confidence intervals and interpretation
• P-values and statistical significance
• Application in consulting decision-making

8. Regression Analysis

• Introduction to regression concepts
• Linear regression:
  • Model building and assumptions
  • Interpretation of coefficients
• Logistic regression for binary outcomes
• Other regression models overview (Poisson, Cox regression)
• Model diagnostics and validation

9. Multivariate Analysis

• Purpose and importance in complex data
• Factor analysis:
  • Exploratory and confirmatory
• Cluster analysis:
  • Types of clustering methods
  • Use cases in consulting
• Principal component analysis (PCA): dimensionality reduction
• Interpretation and reporting of multivariate results

10. Reporting and Presenting Results

• Principles of effective communication
• Structuring reports for clarity and impact
• Visualization best practices
• Tailoring presentations to client audiences
• Ethical reporting and transparency
• Use of storytelling to convey statistical findings