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![maths - Probability Complete Formulas Bosic Terms Classical Definition of Probability 2 1 Random Experiment: An experiment] Number of favourable outcomes P(E) whose outcome connot be predicted with Total number of outcomes certainty: Sample Space (S): The set of all n(E) P(E) ible outcomes. LH n(S) possi coin H7} Event (E): A subset of the sample space 4. Complementary Event ३. Properties of Probability If E is an event, then its complement 0 < P(E) < 1 is E P(S) P(E') = 1 - P(E) P(o) Impossible and Sure Events 6. 5. Equdlly Likely Events Probability Impossible event 0 Events having the same chance of > Probability Sure event 7 occurrence 8. Conditional Probability Addition Theorem of Probability P(AnB) (a) For Two Events: P(B) # 0 P(A | B) P(B) P(AUB) = P(A) + P(B) - P(AnB) (b) For Mutudlly Exclusive Events: P(AUB) = P(A) + P(B) १०. Independent Events independent A and B are 9. Multiplication Theorem Two events P(An B) = P(A)P(B) if: P(A) x P(B|A) P(AnB) If A and B are independent events : 12. Probability Distribution P(A)x P(B) P(AnB) (Discrete Random Variable) E(X) = ZxP(x) Mean (Expectation) ११. Bayes ' Theorem E(X२) - [E(X)]२ dre mutudlly exclusive If A1 A2, A3, Variance: Var(X) qnd exhqustive events qnd B is qny event: 13. Meon of Binomidl Distribution P(A;)P(B | Ai) Mean np P(Ai |B) ZP(Aj)P(B|Aj) Vorionce npq Where: p probability of success] ৭ = 1 - p Probability Complete Formulas Bosic Terms Classical Definition of Probability 2 1 Random Experiment: An experiment] Number of favourable outcomes P(E) whose outcome connot be predicted with Total number of outcomes certainty: Sample Space (S): The set of all n(E) P(E) ible outcomes. LH n(S) possi coin H7} Event (E): A subset of the sample space 4. Complementary Event ३. Properties of Probability If E is an event, then its complement 0 < P(E) < 1 is E P(S) P(E') = 1 - P(E) P(o) Impossible and Sure Events 6. 5. Equdlly Likely Events Probability Impossible event 0 Events having the same chance of > Probability Sure event 7 occurrence 8. Conditional Probability Addition Theorem of Probability P(AnB) (a) For Two Events: P(B) # 0 P(A | B) P(B) P(AUB) = P(A) + P(B) - P(AnB) (b) For Mutudlly Exclusive Events: P(AUB) = P(A) + P(B) १०. Independent Events independent A and B are 9. Multiplication Theorem Two events P(An B) = P(A)P(B) if: P(A) x P(B|A) P(AnB) If A and B are independent events : 12. Probability Distribution P(A)x P(B) P(AnB) (Discrete Random Variable) E(X) = ZxP(x) Mean (Expectation) ११. Bayes ' Theorem E(X२) - [E(X)]२ dre mutudlly exclusive If A1 A2, A3, Variance: Var(X) qnd exhqustive events qnd B is qny event: 13. Meon of Binomidl Distribution P(A;)P(B | Ai) Mean np P(Ai |B) ZP(Aj)P(B|Aj) Vorionce npq Where: p probability of success] ৭ = 1 - p - ShareChat](https://cdn4.sharechat.com/bd5223f_s1w/compressed_gm_40_img_253513_1d81b553_1779599250267_sc.jpg?tenant=sc&referrer=pwa-sharechat-service&f=267_sc.jpg "maths - Probability Complete Formulas Bosic Terms Classical Definition of Probability 2 1 Random Experiment: An experiment] Number of favourable outcomes P(E) whose outcome connot be predicted with Total number of outcomes certainty: Sample Space (S): The set of all n(E) P(E) ible outcomes. LH n(S) possi coin H7} Event (E): A subset of the sample space 4. Complementary Event ३. Properties of Probability If E is an event, then its complement 0 < P(E) < 1 is E P(S) P(E') = 1 - P(E) P(o) Impossible and Sure Events 6. 5. Equdlly Likely Events Probability Impossible event 0 Events having the same chance of > Probability Sure event 7 occurrence 8. Conditional Probability Addition Theorem of Probability P(AnB) (a) For Two Events: P(B) # 0 P(A | B) P(B) P(AUB) = P(A) + P(B) - P(AnB) (b) For Mutudlly Exclusive Events: P(AUB) = P(A) + P(B) १०. Independent Events independent A and B are 9. Multiplication Theorem Two events P(An B) = P(A)P(B) if: P(A) x P(B|A) P(AnB) If A and B are independent events : 12. Probability Distribution P(A)x P(B) P(AnB) (Discrete Random Variable) E(X) = ZxP(x) Mean (Expectation) ११. Bayes ' Theorem E(X२) - [E(X)]२ dre mutudlly exclusive If A1 A2, A3, Variance: Var(X) qnd exhqustive events qnd B is qny event: 13. Meon of Binomidl Distribution P(A;)P(B | Ai) Mean np P(Ai |B) ZP(Aj)P(B|Aj) Vorionce npq Where: p probability of success] ৭ = 1 - p Probability Complete Formulas Bosic Terms Classical Definition of Probability 2 1 Random Experiment: An experiment] Number of favourable outcomes P(E) whose outcome connot be predicted with Total number of outcomes certainty: Sample Space (S): The set of all n(E) P(E) ible outcomes. LH n(S) possi coin H7} Event (E): A subset of the sample space 4. Complementary Event ३. Properties of Probability If E is an event, then its complement 0 < P(E) < 1 is E P(S) P(E') = 1 - P(E) P(o) Impossible and Sure Events 6. 5. Equdlly Likely Events Probability Impossible event 0 Events having the same chance of > Probability Sure event 7 occurrence 8. Conditional Probability Addition Theorem of Probability P(AnB) (a) For Two Events: P(B) # 0 P(A | B) P(B) P(AUB) = P(A) + P(B) - P(AnB) (b) For Mutudlly Exclusive Events: P(AUB) = P(A) + P(B) १०. Independent Events independent A and B are 9. Multiplication Theorem Two events P(An B) = P(A)P(B) if: P(A) x P(B|A) P(AnB) If A and B are independent events : 12. Probability Distribution P(A)x P(B) P(AnB) (Discrete Random Variable) E(X) = ZxP(x) Mean (Expectation) ११. Bayes ' Theorem E(X२) - [E(X)]२ dre mutudlly exclusive If A1 A2, A3, Variance: Var(X) qnd exhqustive events qnd B is qny event: 13. Meon of Binomidl Distribution P(A;)P(B | Ai) Mean np P(Ai |B) ZP(Aj)P(B|Aj) Vorionce npq Where: p probability of success] ৭ = 1 - p - ShareChat")