La probabilità è una misura del grado di incertezza di un evento in un certo esperimento casuale.
E’ ragionevole misurare l’incertezza degli eventi assegnando ad essi un numero compreso tra 0 e 1, detto probabilità di un evento.
Quanto più la probabilità è vicina a zero tanto più l’evento si verifica raramente e quanto più la probabilità è vicina a 1 tanto più l’evento è frequente.
This document provides an overview of key concepts in probability. It defines probability as a measure of likelihood or chance that can be estimated using relative frequency or subjective estimates. It introduces common probability notation and outlines three basic rules for computing probability: relative frequency approximation, classical approach, and subjective probabilities. Examples are provided to illustrate these concepts and rules. The document also discusses the law of large numbers, probability limits between 0 and 1, and guidelines for rounding off probabilities.
This presentation provides an introduction to basic probability concepts. It defines probability as the study of randomness and uncertainty, and describes how probability was originally associated with games of chance. Key concepts discussed include random experiments, sample spaces, events, unions and intersections of events, and Venn diagrams. The presentation establishes the axioms of probability, including that a probability must be between 0 and 1, the probability of the sample space is 1, and probabilities of mutually exclusive events sum to the total probability. Formulas for computing probabilities of unions, intersections, and complements of events are also presented.
This document defines key terminology related to probability, including:
- Sample space: The set of all possible outcomes of a random experiment.
- Events: One or more possible outcomes of an experiment.
- Equally likely events: Events with an equal chance of occurring.
- Mutually exclusive events: Events that cannot occur together.
- Exhaustive events: A set of events where one of the events must occur.
It also provides examples and introduces the classical definition of probability as the number of favorable outcomes divided by the total number of outcomes.
Probability In Discrete Structure of Computer SciencePrankit Mishra
This document provides an overview of probability, including its basic definition, history, interpretations, theory, and applications. Probability is defined as a measure between 0 and 1 of the likelihood of an event occurring, where 0 is impossible and 1 is certain. It has been given a mathematical formalization and is used in many fields including statistics, science, and artificial intelligence. Historically, the scientific study of probability began in the 17th century and was further developed by thinkers like Bernoulli, Legendre, and Kolmogorov. Probability can be interpreted objectively based on frequencies or subjectively as degrees of belief. Important probability terms covered include experiments, outcomes, events, joint probability, independent events, mutually exclusive events, and conditional probability.
Probability is the chance that something will happen or the likelihood of an event. It is measured by the number of favorable outcomes divided by the total number of possible outcomes. Some key contributors to the development of probability include Buffon, Kerrich, and Pearson who performed coin toss experiments to determine experimental probabilities. Probability is used in various real-life domains like weather forecasting, insurance policies, sports strategies, and medical decisions.
La probabilità è una misura del grado di incertezza di un evento in un certo esperimento casuale.
E’ ragionevole misurare l’incertezza degli eventi assegnando ad essi un numero compreso tra 0 e 1, detto probabilità di un evento.
Quanto più la probabilità è vicina a zero tanto più l’evento si verifica raramente e quanto più la probabilità è vicina a 1 tanto più l’evento è frequente.
This document provides an overview of key concepts in probability. It defines probability as a measure of likelihood or chance that can be estimated using relative frequency or subjective estimates. It introduces common probability notation and outlines three basic rules for computing probability: relative frequency approximation, classical approach, and subjective probabilities. Examples are provided to illustrate these concepts and rules. The document also discusses the law of large numbers, probability limits between 0 and 1, and guidelines for rounding off probabilities.
This presentation provides an introduction to basic probability concepts. It defines probability as the study of randomness and uncertainty, and describes how probability was originally associated with games of chance. Key concepts discussed include random experiments, sample spaces, events, unions and intersections of events, and Venn diagrams. The presentation establishes the axioms of probability, including that a probability must be between 0 and 1, the probability of the sample space is 1, and probabilities of mutually exclusive events sum to the total probability. Formulas for computing probabilities of unions, intersections, and complements of events are also presented.
This document defines key terminology related to probability, including:
- Sample space: The set of all possible outcomes of a random experiment.
- Events: One or more possible outcomes of an experiment.
- Equally likely events: Events with an equal chance of occurring.
- Mutually exclusive events: Events that cannot occur together.
- Exhaustive events: A set of events where one of the events must occur.
It also provides examples and introduces the classical definition of probability as the number of favorable outcomes divided by the total number of outcomes.
Probability In Discrete Structure of Computer SciencePrankit Mishra
This document provides an overview of probability, including its basic definition, history, interpretations, theory, and applications. Probability is defined as a measure between 0 and 1 of the likelihood of an event occurring, where 0 is impossible and 1 is certain. It has been given a mathematical formalization and is used in many fields including statistics, science, and artificial intelligence. Historically, the scientific study of probability began in the 17th century and was further developed by thinkers like Bernoulli, Legendre, and Kolmogorov. Probability can be interpreted objectively based on frequencies or subjectively as degrees of belief. Important probability terms covered include experiments, outcomes, events, joint probability, independent events, mutually exclusive events, and conditional probability.
Probability is the chance that something will happen or the likelihood of an event. It is measured by the number of favorable outcomes divided by the total number of possible outcomes. Some key contributors to the development of probability include Buffon, Kerrich, and Pearson who performed coin toss experiments to determine experimental probabilities. Probability is used in various real-life domains like weather forecasting, insurance policies, sports strategies, and medical decisions.
1) The document introduces basic concepts of probability such as sample spaces, events, outcomes, and how to calculate classical and empirical probabilities.
2) It discusses approaches to determining probability including classical, empirical, and subjective probabilities. Simulations can also be used to estimate probabilities.
3) Examples are provided to illustrate calculating probabilities using classical and empirical approaches for single and compound events with different sample spaces.
The document introduces complex numbers and their properties. It defines the imaginary unit i as the square root of -1. Complex numbers have both a real and imaginary part and can be added, subtracted, multiplied and divided. Powers of i rotate through the values of i, -1, -i, and 1, depending on whether the exponent is 1, 2, 3, or 4 modulo 4. Real and imaginary numbers are subsets of complex numbers.
It gives detail description about probability, types of probability, difference between mutually exclusive events and independent events, difference between conditional and unconditional probability and Bayes' theorem
This document discusses basic concepts of probability, including:
- The addition rule and multiplication rule for calculating probabilities of compound events.
- Events can be disjoint (mutually exclusive) or not disjoint.
- The probability of an event occurring or its complement must equal 1.
- How to calculate the probability of at least one occurrence of an event using the complement.
- When applying the multiplication rule, you must consider whether events are independent or dependent.
The document discusses conditional probability and provides examples. It defines conditional probability P(A|B) as the probability of event A occurring given that event B has already occurred. An example calculates probabilities for drawing marbles from a bag. Another example finds probabilities for selecting chocolates with different flavors from a box containing chocolates of various flavors. Formulas and step-by-step workings are provided for calculating conditional probabilities.
The document outlines a lesson plan to teach students the difference between independent and dependent events and how to calculate the probability of each. The lesson will begin with explaining the key concepts, then provide examples to illustrate the difference between independent and dependent events. Students will have opportunities to ask questions and practice calculating probabilities of independent and dependent events.
This document discusses sequences and series, their definitions, types, and applications in daily life. It begins by defining a sequence as a collection of objects in a particular order, with each element having a specific rank or index. Two main types of sequences are discussed: arithmetic sequences, where each term is determined by adding or subtracting a constant from the previous term, and geometric sequences, where each term is determined by multiplying the previous term by a fixed ratio.
The document then provides several examples of how sequences and series are applied in various areas of daily life, such as calculating investment growth, depreciation of objects, population growth, compound interest, and calculating simple interest. In conclusion, the document emphasizes that sequences and series
The document provides an introduction to probability. It discusses:
- What probability is and the definition of probability as a number between 0 and 1 that expresses the likelihood of an event occurring.
- A brief history of probability including its development in French society in the 1650s and key figures like James Bernoulli, Abraham De Moivre, and Pierre-Simon Laplace.
- Key terms used in probability like events, outcomes, sample space, theoretical probability, empirical probability, and subjective probability.
- The three types of probability: theoretical, empirical, and subjective probability.
- General probability rules including: the probability of impossible/certain events; the sum of all probabilities equaling 1; complements
El documento describe los principales aportes al álgebra en los siglos XVII, XVIII, XIX y XX. Menciona a importantes matemáticos como Newton, Gauss, Galois y Grassmann y cómo revoluciones industriales y conflictos políticos influyeron en el desarrollo del álgebra en cada época. Concluye que es importante conocer la historia del álgebra para entender los cambios y nuevos enfoques a lo largo del tiempo.
This document discusses the addition rules for probability of compound events. It defines mutually exclusive events as events that cannot occur at the same time, and explains that for mutually exclusive events A and B, the probability of A or B is equal to the probability of A plus the probability of B. For events that are not mutually exclusive, the probability of A or B is equal to the probability of A plus the probability of B minus the probability of A and B occurring together. Several examples are provided to illustrate calculating probabilities using these addition rules.
This document defines key concepts in probability and provides examples. It discusses probability vocabulary like sample space, outcome, trial, and event. It defines probability as the number of times a desired outcome occurs over total trials. Events are independent if the outcome of one does not impact others, and mutually exclusive if they cannot occur together. The addition and multiplication rules for probability are explained. Conditional probability describes the probability of a second event depending on the first occurring. Counting techniques are discussed for finding total possible outcomes of combined experiments. Review questions are provided to test understanding of the material.
This document outlines basic probability concepts, including definitions of probability, views of probability (objective and subjective), and elementary properties. It discusses calculating probabilities of events from data in tables, including unconditional/marginal probabilities, conditional probabilities, and joint probabilities. Rules of probability are presented, including the multiplicative rule that the joint probability of two events is equal to the product of the marginal probability of one event and the conditional probability of the other event given the first event. Examples are provided to illustrate key concepts.
A set is a collection of distinct objects called elements or members. Sets can be represented symbolically using capital letters and elements using lowercase letters. Common operations on sets include union, intersection, and complement. These operations satisfy various algebraic laws such as commutative, associative, distributive, identity, involution, and De Morgan's laws. Sets are fundamental data structures in computer science and are commonly represented visually using Venn diagrams.
Ottimizzazione della funzione di perdita dei costi totali. Marika Torcitto
Presentazione di laurea per Luigi Torcitto. Laurea in ingegneria gestionale presso l'università statale di Catania.
Titolo della tesi:
Ottimizzazione della funzione di perdita dei costi totali.
1) The document introduces basic concepts of probability such as sample spaces, events, outcomes, and how to calculate classical and empirical probabilities.
2) It discusses approaches to determining probability including classical, empirical, and subjective probabilities. Simulations can also be used to estimate probabilities.
3) Examples are provided to illustrate calculating probabilities using classical and empirical approaches for single and compound events with different sample spaces.
The document introduces complex numbers and their properties. It defines the imaginary unit i as the square root of -1. Complex numbers have both a real and imaginary part and can be added, subtracted, multiplied and divided. Powers of i rotate through the values of i, -1, -i, and 1, depending on whether the exponent is 1, 2, 3, or 4 modulo 4. Real and imaginary numbers are subsets of complex numbers.
It gives detail description about probability, types of probability, difference between mutually exclusive events and independent events, difference between conditional and unconditional probability and Bayes' theorem
This document discusses basic concepts of probability, including:
- The addition rule and multiplication rule for calculating probabilities of compound events.
- Events can be disjoint (mutually exclusive) or not disjoint.
- The probability of an event occurring or its complement must equal 1.
- How to calculate the probability of at least one occurrence of an event using the complement.
- When applying the multiplication rule, you must consider whether events are independent or dependent.
The document discusses conditional probability and provides examples. It defines conditional probability P(A|B) as the probability of event A occurring given that event B has already occurred. An example calculates probabilities for drawing marbles from a bag. Another example finds probabilities for selecting chocolates with different flavors from a box containing chocolates of various flavors. Formulas and step-by-step workings are provided for calculating conditional probabilities.
The document outlines a lesson plan to teach students the difference between independent and dependent events and how to calculate the probability of each. The lesson will begin with explaining the key concepts, then provide examples to illustrate the difference between independent and dependent events. Students will have opportunities to ask questions and practice calculating probabilities of independent and dependent events.
This document discusses sequences and series, their definitions, types, and applications in daily life. It begins by defining a sequence as a collection of objects in a particular order, with each element having a specific rank or index. Two main types of sequences are discussed: arithmetic sequences, where each term is determined by adding or subtracting a constant from the previous term, and geometric sequences, where each term is determined by multiplying the previous term by a fixed ratio.
The document then provides several examples of how sequences and series are applied in various areas of daily life, such as calculating investment growth, depreciation of objects, population growth, compound interest, and calculating simple interest. In conclusion, the document emphasizes that sequences and series
The document provides an introduction to probability. It discusses:
- What probability is and the definition of probability as a number between 0 and 1 that expresses the likelihood of an event occurring.
- A brief history of probability including its development in French society in the 1650s and key figures like James Bernoulli, Abraham De Moivre, and Pierre-Simon Laplace.
- Key terms used in probability like events, outcomes, sample space, theoretical probability, empirical probability, and subjective probability.
- The three types of probability: theoretical, empirical, and subjective probability.
- General probability rules including: the probability of impossible/certain events; the sum of all probabilities equaling 1; complements
El documento describe los principales aportes al álgebra en los siglos XVII, XVIII, XIX y XX. Menciona a importantes matemáticos como Newton, Gauss, Galois y Grassmann y cómo revoluciones industriales y conflictos políticos influyeron en el desarrollo del álgebra en cada época. Concluye que es importante conocer la historia del álgebra para entender los cambios y nuevos enfoques a lo largo del tiempo.
This document discusses the addition rules for probability of compound events. It defines mutually exclusive events as events that cannot occur at the same time, and explains that for mutually exclusive events A and B, the probability of A or B is equal to the probability of A plus the probability of B. For events that are not mutually exclusive, the probability of A or B is equal to the probability of A plus the probability of B minus the probability of A and B occurring together. Several examples are provided to illustrate calculating probabilities using these addition rules.
This document defines key concepts in probability and provides examples. It discusses probability vocabulary like sample space, outcome, trial, and event. It defines probability as the number of times a desired outcome occurs over total trials. Events are independent if the outcome of one does not impact others, and mutually exclusive if they cannot occur together. The addition and multiplication rules for probability are explained. Conditional probability describes the probability of a second event depending on the first occurring. Counting techniques are discussed for finding total possible outcomes of combined experiments. Review questions are provided to test understanding of the material.
This document outlines basic probability concepts, including definitions of probability, views of probability (objective and subjective), and elementary properties. It discusses calculating probabilities of events from data in tables, including unconditional/marginal probabilities, conditional probabilities, and joint probabilities. Rules of probability are presented, including the multiplicative rule that the joint probability of two events is equal to the product of the marginal probability of one event and the conditional probability of the other event given the first event. Examples are provided to illustrate key concepts.
A set is a collection of distinct objects called elements or members. Sets can be represented symbolically using capital letters and elements using lowercase letters. Common operations on sets include union, intersection, and complement. These operations satisfy various algebraic laws such as commutative, associative, distributive, identity, involution, and De Morgan's laws. Sets are fundamental data structures in computer science and are commonly represented visually using Venn diagrams.
Ottimizzazione della funzione di perdita dei costi totali. Marika Torcitto
Presentazione di laurea per Luigi Torcitto. Laurea in ingegneria gestionale presso l'università statale di Catania.
Titolo della tesi:
Ottimizzazione della funzione di perdita dei costi totali.
1. Living beings are made up of cells, which are the basic structural and functional units of organisms.
2. Cells carry out three vital functions - nutrition, interaction, and reproduction. Nutrition allows cells to obtain energy and matter for growth and maintenance.
3. There are two main types of nutrition - autotrophic and heterotrophic. Autotrophs like plants can produce their own food via photosynthesis, while heterotrophs obtain organic nutrients from other living or dead organisms.
Probabilità e statistica: la scienza della previsioneAndrea Capocci
Come introdurre la probabilità e la statistica nella scuola superiore? Questa è la presentazione di un percorso didattico che prevede di introdurre la statistica con preciso punto di vista, non esaustivo ma compiuto. Questa presentazione è stata preparata per l'esame di Probabilità e Statistica nel Tirocinio Formativo Attivo 2014-15 dell'Università di Roma Tre
2. La parte della matematica che studia gli avvenimenti
legati al caso, al fine di stabilire quale possibilità di
verificarsi hanno tali avvenimenti, prende il nome di
CALCOLO DELLE PROBABILITA’
Essa nacque nel ‘600 per merito di Blaise Pascal, che iniziò ad occuparsi di
alcune questioni connesse al gioco d’azzardo; in seguito si occuparono di
questo settore, studiosi come FERMAT, NEWTON, LEIBNITZ e LAPLACE
3. Gli avvenimenti che hanno risultato incerto,
perché sono legati al caso, si dicono
AVVENIMENTI CASUALI o ALEATORI
Ogni possibile risultato di un avvenimento
casuale si dice
EVENTO SEMPLICE o ELEMENTARE
4. Tutti gli eventi semplici che possono verificarsi come risultato di
un avvenimento casuale, si dicono CASI POSSIBILI
dell’avvenimento casuale
Se tutti i casi possibili hanno la stessa possibilità di verificarsi si
dicono UGUALMENTE PROBABILI
Se si considera uno degli eventi semplici di un avvenimento
casuale, fra tutti i casi possibili, quelli che verificano l’evento
considerato, si dicono
CASI FAVOREVOLI
5. DEFINIZIONE CLASSICA di PROBABILITA’
In un avvenimento casuale la probabilità p(E) di un evento semplice E è il
rapporto fra il numero dei casi favorevoli all’evento E e il numero di casi
possibili, purchè siano tutti egualmente possibili
𝑃(𝐸) =
𝑐𝑎𝑠𝑖 𝑓𝑎𝑣𝑜𝑟𝑒𝑣𝑜𝑙𝑖
𝑐𝑎𝑠𝑖 𝑝𝑜𝑠𝑠𝑖𝑏𝑖𝑙𝑖
6. Se un evento si verifica sempre, si dice CERTO
e la sua probabilità vale 1
Se un evento non si verifica mai, si dice IMPOSSIBILE
e la sua probabilità vale 0
La probabilità di un evento quindi è sempre un numero
compreso fra 0 ed 1 : 0 ≤ 𝑃(𝐸) ≤ 1
N.B.:
La probabilità può anche essere espressa in forma
percentuale moltiplicando per 100 il suo valore
numerico
7. Esercizi:
1. Qual è la probabilità che lanciando il dado esca 6?
𝑃 𝑒𝑠𝑐𝑒 6 =
1
6
2. Qual è la probabilità che lanciando una moneta esca croce?
𝑃 𝑒𝑠𝑐𝑒 𝑐𝑟𝑜𝑐𝑒 =
1
2
8. Esercizi:
1. Vengono lanciati due dadi. Qual è la probabilità di ottenere lo
stesso numero con entrambi?
I casi possibili sono: 62 = 36
I casi favorevoli sono 6
Quindi:
𝑃 𝑒𝑠𝑐𝑒 𝑙𝑜 𝑠𝑡𝑒𝑠𝑠𝑜 𝑛𝑢𝑚𝑒𝑟𝑜 =
6
36
=
1
6
9. Esercizi:
1. Vengono lanciati due dadi. Qual è la probabilità di ottenere come
somma 4?
I casi possibili sono: 62 = 36
I casi favorevoli sono 3
Quindi:
𝑃 𝑒𝑠𝑐𝑒 𝑙𝑜 𝑠𝑡𝑒𝑠𝑠𝑜 𝑛𝑢𝑚𝑒𝑟𝑜 =
3
36
=
1
12
cioè 8,3%
11. Teorema della probabilità totale
Siano A e B due eventi diversi tra loro allora
𝑃 𝐴 ∪ 𝐵 = 𝑃 𝐴 +𝑃 𝐵 − 𝑃 𝐴 ∩ 𝐵
Probabilità che si
verifichi uno dei due
eventi
Probabilità che si verifichi
contemporaneamente i
due eventi
Osservazione: se gli eventi sono incompatibili allora 𝑃 𝐴 ∩ 𝐵 = 0
12. Esercizi:
1. Qual è la probabilità che lanciando un dado esca 3 oppure 4?
A= esce 3
B= esce 4
𝑃 𝐴 ∪ 𝐵 = 𝑃 𝐴 +𝑃 𝐵 − 𝑃 𝐴 ∩ 𝐵 =
1
6
+
1
6
− 0 =
2
6
=
1
3
2. Qual è la probabilità che lanciando un dado esca un numero pari o
un multiplo di 3?
A=esce un numero pari
B=esce un multiplo di 3
𝑃 𝐴 ∪ 𝐵 = 𝑃 𝐴 +𝑃 𝐵 − 𝑃 𝐴 ∩ 𝐵 =
3
6
+
2
6
−
1
6
=
4
6
=
2
3
13. Esercizi:
1. Qual è la probabilità che lanciando contemporaneamente due dadi
esca almeno un 6?
A= esce 6 nel primo dado
B= esce 6 nel secondo dado
𝑃 𝐴 ∪ 𝐵 = 𝑃 𝐴 +𝑃 𝐵 − 𝑃 𝐴 ∩ 𝐵 =
1
6
+
1
6
−
1
6
∙
1
6
=
2
6
−
1
36
=
11
36
14. Eventi dipendenti e indipendenti
Eventi indipendenti: se il fatto che si verifichi (o
meno) il primo evento NON altera la probabilità che
esca il secondo evento
Eventi dipendenti: se il fatto che si verifichi (o meno)
il primo evento altera la probabilità che esca il secondo
evento
15. Esercizi:
1. Qual è la probabilità che lanciando un dado e una moneta esca un
4 e una testa?
𝑃 𝑒𝑠𝑐𝑜𝑛𝑜 4 𝑒 𝑡𝑒𝑠𝑡𝑎 =
𝑐𝑎𝑠𝑖 𝑓𝑎𝑣𝑜𝑟𝑒𝑣𝑜𝑙𝑖
𝑐𝑎𝑠𝑖 𝑝𝑜𝑠𝑠𝑖𝑏𝑖𝑙𝑖
=
1
12
Cioè =
1
6
∙
1
2
17. Esercizi:
1. In una classe ci sono 20 alunni, 12 femmine e 8 ragazzi. La prof
ne sceglie 2 random da interrogare, Qual è la probabilità che
escano 2 ragazze?
𝑃 𝑒𝑠𝑐𝑜𝑛𝑜 2 𝑟𝑎𝑔𝑎𝑧𝑧𝑒 =
𝑐𝑎𝑠𝑖 𝑓𝑎𝑣𝑜𝑟𝑒𝑣𝑜𝑙𝑖
𝑐𝑎𝑠𝑖 𝑝𝑜𝑠𝑠𝑖𝑏𝑖𝑙𝑖
=
12
2
20
2
=
12!
2! ∙ 10!
20!
2! ∙ 18!
=
12 ∙ 11
20 ∙ 19
Probabilità di scegliere una ragazza
Probabilità di scegliere una ragazza sapendo che né è già
uscita una
18. Teorema
Se A e B sono due eventi dipendenti allora
𝑃 𝐴 ∩ 𝐵 = 𝑃 𝐴 ∙ 𝑃 𝐵|𝐴
Probabilità che si
verifichino
entrambi gli eventi
Probabilità
che si
verifichi
l’evento A
Probabilità che si
verifichi l’evento B
sapendo che si è
verificato l’evento A
20. Prove ripetute
Problema:
Lancio 10 volte una moneta. È più probabile che io ottenga 10 volte croce
oppure 4 volte testa e 6 volte croce?
10 croci =
1
2
∙
1
2
∙
1
2
∙
1
2
∙
1
2
∙
1
2
∙
1
2
∙
1
2
∙
1
2
∙
1
2
=
1
2
10
10 volte croce
4 volte testa e 6 volte croce =
1
2
∙
1
2
∙
1
2
∙
1
2
∙
1
2
∙
1
2
∙
1
2
∙
1
2
∙
1
2
∙
1
2
=
1
2
10
4 volte testa 6 volte croce
Qualcosa non convince…
21. T T T T C C C C C C
T C T C T C T C C C
…..
In quanti modi posso formare questa sequenza:
10
4
=
10!
4! ∙ 6!
= 210
𝑃 4 𝑡𝑒𝑠𝑡𝑒 𝑒 6 𝑐𝑟𝑜𝑐𝑖 =
10
4
∙
1
2
10
≫≫ 𝑃 10 𝑐𝑟𝑜𝑐𝑖 =
1
2
10
22. Problema:
Lancio 6 volte un dado. Qual è la probabilità di ottenere come risultato 1
esattamente 4 volte?
1 1 1 1 x x
1 x 1 x 1 1
x x 1 1 1 1
….
𝑃 4 𝑣𝑜𝑙𝑡𝑒 1 𝑖𝑛 6 𝑙𝑎𝑛𝑐𝑖 =
6
4
1
6
4
1 −
1
6
2
6 - 4
23. Formula di Bernoulli
Consideriamo un «esperimento» in cui un certo evento
abbia una probabilità p di realizzarsi. Ripetiamo
l’esperimento n volte. Qual è la probabilità che
l’esperimento si realizzi k volte?
𝑃 𝐸 𝑠𝑖 𝑣𝑒𝑟𝑖𝑓𝑖𝑐𝑎 𝑘 𝑣𝑜𝑙𝑡𝑒 𝑠𝑢 𝑛 =
𝑛
𝑘
𝑝 𝑘 1 − 𝑝 𝑛−𝑘
24. Esercizi esame di Stato
2011 PNI
In una classe ci sono 16 studenti, di cui 12 maschi. Qual è la
probabilità che scegliendone 3 a caso da interrogare si
scelgano 3 maschi?
Svolgimento:
Strada 1:
12
16
∙
11
15
∙
10
14
= 0,39
Strada 2:
𝑐𝑎𝑠𝑖 𝑓𝑎𝑣𝑜𝑟𝑒𝑣𝑜𝑙𝑖
𝑐𝑎𝑠𝑖 𝑝𝑜𝑠𝑠𝑖𝑏𝑖𝑙𝑖
=
12
3
16
3
= 0,39
25. Esercizi esame di stato
2011 PNI:
Un tiratore ha la probabilità del 30% di centrare un certo bersaglio. Quante
volte deve sparare per avere una probabilità del 99% (o superiore) di colpire
il bersaglio almeno una volta sparando ripetutamente?
Svolgimento:
P(colpisce il bersaglio almeno una volta)=1-P(non lo colpisce mai)=
1 −
𝑛
0
3
10
0
1 −
3
10
𝑛
= 1 −
7
10
𝑛
Ma P(almeno una volta)≥
99
100
e poi risolvete la disequazione.
26. LA LEGGE DEI GRANDI NUMERI
(legge empirica del caso)
In una serie molto elevata di prove, effettuate tutte nelle
stesse condizioni, la probabilità sperimentale di un evento
assume un valore generalmente molto prossimo a quello della
probabilità classica e tale approssimazione aumenta
all’aumentare del numero delle prove