Unit 1171: Business Statistics and Analytics

1. Introduction to Business Statistics

• Overview of statistics in business decision-making
• Types of data: qualitative vs. quantitative, discrete vs. continuous
• Importance of statistical analysis in business
• Common statistical terms and concepts (population, sample, parameter, statistic)

2. Descriptive Statistics

• Measures of Central Tendency
  • Mean, median, mode: definitions, calculation, and interpretation
• Measures of Dispersion
  • Range, variance, standard deviation: concepts and calculations
• Graphical Representations
  • Histograms, box plots, bar charts, pie charts
  • Interpretation and best use cases

3. Probability Theory

• Basic Probability Concepts
  • Experiment, outcome, event
  • Probability rules: addition and multiplication rules
• Probability Distributions
  • Discrete distributions (e.g., Binomial, Poisson)
  • Continuous distributions (e.g., Normal distribution)
• Applications of Probability in Business
  • Risk assessment, decision making under uncertainty

4. Sampling Methods

• Importance of Sampling in Business Statistics
• Sampling Techniques
  • Random sampling
  • Stratified sampling
  • Cluster sampling
• Sample Size Determination
  • Factors influencing sample size
  • Balancing accuracy and cost

5. Hypothesis Testing

• Formulating Hypotheses
  • Null and alternative hypotheses
• Steps in Hypothesis Testing
  • Selecting significance level
  • Test statistics and critical values
  • Decision rules
• Types of Errors
  • Type I and Type II errors
• Interpretation of Results in Business Context

6. Regression Analysis

• Simple Linear Regression
  • Model formulation
  • Estimation of coefficients
  • Interpretation of slope and intercept
• Multiple Regression
  • Incorporating multiple independent variables
  • Assessing model fit (R-squared, adjusted R-squared)
• Assumptions and Diagnostics
• Application Examples in Business

7. Time Series Analysis

• Characteristics of Time Series Data
  • Trend, seasonality, cyclic patterns, irregular components
• Trend Analysis
• Seasonal Adjustment
• Forecasting Techniques
  • Moving averages
  • Exponential smoothing
  • ARIMA models (overview)
• Application in Business Forecasting and Decision-Making

8. Statistical Software Applications

• Overview of Popular Tools
  • SPSS, Microsoft Excel, R, Python
• Data Analysis Features
  • Data import and cleaning
  • Descriptive statistics and visualization
  • Conducting inferential tests
• Visualization Techniques
• Interpreting Outputs for Business Insights

9. Quality Control and Process Improvement

• Introduction to Quality Control in Business
• Control Charts
  • Types: X-bar, R-chart, p-chart
  • Interpretation and application
• Process Capability Analysis
• Six Sigma Principles
  • DMAIC methodology
  • Reducing variability and defects

10. Business Analytics and Big Data

• Role of Business Analytics in Modern Organizations
• Leveraging Big Data for Strategic Decision-Making
• Data Mining Techniques
• Predictive Modeling
• Advanced Statistical Techniques
  • Machine learning overview
  • Applications in marketing, finance, operations