Q-Q Plots

Requirements Clarification & Assessment

1. Understanding Q-Q Plots:

   • Grasp the fundamental purpose of Q-Q plots in statistics, which is to compare the quantiles of a dataset against a theoretical distribution (usually normal) to assess the similarity in distribution.
   • Learn how Q-Q plots are used to detect deviations from normality, skewness, and kurtosis in data.

2. Application Context:

   • Be prepared to discuss specific instances where Q-Q plots were used in past projects, particularly in assessing data distribution before applying statistical methods or transformations.
   • Understand the importance of distribution assessment in hypothesis testing and statistical analysis.

3. Technical Proficiency:

   • Demonstrate familiarity with generating and interpreting Q-Q plots using statistical software or programming languages like Python or R.
   • Be ready to explain the process of creating Q-Q plots and interpreting the results in a real-world context.

4. Problem-solving with Q-Q Plots:

   • Identify scenarios where Q-Q plots can be crucial, such as validating assumptions of normality in regression analysis or ensuring data appropriateness for parametric tests.