Statistics - Normal Distribution(gaussion Distribution)

 
   
     
       

Normal Distribution (Gaussian Distribution)

       

Understanding the Normal Distribution in Statistics

       

          The Normal Distribution, also known as the Gaussian Distribution, is a symmetric, bell-shaped probability distribution that describes how values are distributed around the mean. It is widely used in statistics, natural sciences, and social sciences due to its applicability in real-world data.        

        Normal Distribution Graph      
     
       
         

Key Notation:

         
               
  • \( \mu = \bar{x} \): Mean (center of the distribution)
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  • \( \sigma \): Standard deviation (spread of the distribution)
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  • \( f(x) \): Probability density function (PDF)
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  • \( F(x) \): Cumulative distribution function (CDF)
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Probability Density Function (PDF):

         
           

              \[               f(x) = \frac{1}{\sigma \sqrt{2\pi}} \, e^{ -\frac{(x - \mu)^2}{2\sigma^2} }               \]            

         
         

Cumulative Distribution Function (CDF):

         
           

              \[               F(x) = \int_{-\infty}^{x} \frac{1}{\sigma \sqrt{2\pi}} \, e^{ -\frac{(t - \mu)^2}{2\sigma^2} } \, dt               \]            

         
         

Key Properties of Normal Distribution:

         
               
  • Symmetrical around the mean: \( \mu \)
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  • Mean = Median = Mode
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  • Area under the curve = 1 (total probability)
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  • Empirical Rule: ~68% within 1σ, ~95% within 2σ, ~99.7% within 3σ
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  • Defined completely by its mean and standard deviation
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Applications of Normal Distribution:

         
               
  • Standardized testing (e.g., IQ scores, SAT scores)
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  • Modeling measurement errors and physical characteristics
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  • Stock returns and financial risk analysis
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  • Inferential statistics (confidence intervals, hypothesis testing)
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