Forecasting Patent Revenue with Regression Analysis and Monte Carlo Simulations - IIPLA
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Forecasting Patent Revenue with Regression Analysis and Monte Carlo Simulations

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About Course

About the Course:

Maximizing Patent Revenue: Unveiling Statistical Forecasting Techniques

Unlocking the potential revenue streams associated with patents demands sophisticated statistical analysis. In this session, regression analysis and Monte Carlo simulations take center stage, delving into over 20 Excel worksheets and unveiling the application of numerous calculations and formulas. Real-world case studies spanning emerging and mature technology companies provide invaluable insights.

Key Topics Discussed:

  1. Time Series Regression Analysis:
    • Understanding and applying regression analysis to analyze trends over time.
  2. Hypothesis Testing:
    • Utilizing regression analysis for hypothesis testing to validate assumptions.
  3. Excel Forecasting Tools:
    • Exploring Excel tools such as LINEST and FORECAST to facilitate accurate predictions.
  4. Enhancing Accuracy:
    • Improving the precision of regression analysis using residuals, ordinary least squares, and Excel’s Solver.
  5. Royalty Revenue Projection:
    • Projecting royalty revenue through various trendline methods including Linear, Polynomial, Logarithmic, and Exponential.
  6. Statistical Tools Utilization:
    • Leveraging statistical metrics like R2, F stat, and p-value to refine forecasting accuracy.
  7. Correcting for Statistical Biases:
    • Addressing issues such as homoscedasticity and autocorrelation to ensure robust forecasting models.
  8. Discounted Cash Flow and Monte Carlo Analysis:
    • Exploring the synergy between discounted cash flow methods and Monte Carlo simulations for comprehensive revenue forecasting.
  9. Early Stage Forecasting:
    • Introducing the Fisher-Pry method for early-stage forecasting of patent revenues.
  10. Monte Carlo Analysis Techniques:
    • Implementing Monte Carlo simulations with various distribution types, including normal, lognormal, triangle, and Latin Hypercube.
  11. Confidence Interval Creation:
    • Generating confidence intervals to gauge the reliability of revenue projections.
  12. Scenario Analysis:
    • Determining the incidence of different scenarios to assess potential outcomes comprehensively.

Course Length: Approx. 2.0 hours

Join us for an immersive journey into statistical forecasting techniques tailored specifically for patent revenue analysis. Gain practical skills and strategic insights to optimize your revenue projections effectively. Reserve your spot today!


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