Statistics - Probability

 
   
     
       

3. Probability

       

Understanding Probability in Mathematics

       

          Probability measures the likelihood of an event occurring and is a fundamental concept in statistics and real-world decision-making. It is represented as a number between 0 and 1, where 0 means an impossible event and 1 means a certain event.        

     
     
       
         

Basic Probability Rules:

         
           

              \[               P(A) \in [0,1] \quad \text{(Probability ranges between 0 and 1)}               \]               \[               P(A') = 1 - P(A) \quad \text{(Complement Rule)}               \]            

         
         

Union and Intersection of Events:

         
           

              \[               P(A \cup B) = P(A) + P(B) - P(A \cap B)               \]               \[               P(A \cap B) = P(A)P(B) \quad \text{(if A and B are independent)} \tag{1}               \]            

         
         

Conditional Probability:

         
           

              \[               P(A \cap B) = P(A|B)P(B) = P(B|A)P(A)               \]               \[               P(A|B) = \frac{P(A \cap B)}{P(B)} = \frac{P(B|A)P(A)}{P(B)} \tag{2}               \]            

         
         

Key Concepts:

         
               
  • Mutually Exclusive Events: Cannot occur at the same time, i.e., \( A \cap B = \emptyset \), so \( P(A \cup B) = P(A) + P(B) \)
  •            
  • Independent Events: Occurrence of one does not affect the other, i.e., \( P(A \cap B) = P(A)P(B) \)
  •            
  • Conditional Probability: Measures the probability of event A given that B has occurred.
  •          
         

Applications of Probability:

         
               
  • Forecasting weather and market trends
  •            
  • Evaluating risk and reliability in engineering
  •            
  • Medical testing and diagnostics (false positive/negative rates)
  •            
  • Machine learning, data science, and AI modeling
  •            
  • Games, gambling, and decision theory
  •          
       
     
         
 
×

×