Download presentation

Presentation is loading. Please wait.

Published byJoaquin Morie Modified over 5 years ago

1
1 Pertemuan 07 Hitung Peluang Matakuliah: I0134 – Metoda Statistika Tahun: 2005 Versi: Revisi

2
2 Learning Outcomes Pada akhir pertemuan ini, diharapkan mahasiswa akan mampu : Mahasiswa dapat menghitung dan menyusun sebaran peluang kejadian.

3
3 Outline Materi Ruang contoh dan Peluang kejadian Kejadian gabungan dan irisan Kejadian komplementasi Kaidah Penghitungan

4
4 Probability is: l A measure of uncertainty l A measure of the strength of belief in the occurrence of an uncertain event l A measure of the degree of chance or likelihood of occurrence of an uncertain event l Measured by a number between 0 and 1 (or between 0% and 100%)

5
5 Types of Probability l Objective or Classical Probability –based on equally-likely events –based on long-run relative frequency of events –not based on personal beliefs – is the same for all observers (objective) –examples: toss a coins, throw a die, pick a card

6
6 Types of Probability (2) l Subjective Probability –based on personal beliefs, experiences, prejudices, intuition - personal judgment –different for all observers (subjective) –examples: Super Bowl, elections, new product introduction, snowfall

7
7 Basic Definitions l Set - a collection of elements or objects of interest –Empty set (denoted by ) l a set containing no elements –Universal set (denoted by S) l a set containing all possible elements –Complement (Not). The complement of A is –a set containing all elements of S not in A

8
8 –Intersection (And) – a set containing all elements in both A and B –Union (Or) – a set containing all elements in A or B or both Basic Definitions

9
9 Mutually exclusive or disjoint sets – sets having no elements in common, having no intersection, whose intersection is the empty set Partition – a collection of mutually exclusive sets which together include all possible elements, whose union is the universal set Basic Definitions

10
10 Partition AB B A A Sets: Diagrams

11
11 Process that leads to one of several possible outcomes *, e.g.: –Coin toss Heads,Tails –Throw die 1, 2, 3, 4, 5, 6 –Pick a card AH, KH, QH,... – Introduce a new product Each trial of an experiment has a single observed outcome. The precise outcome of a random experiment is unknown before a trial. * Also called a basic outcome, elementary event, or simple event Experiment

12
12 l Sample Space or Event Set –Set of all possible outcomes (universal set) for a given experiment l E.g.: Throw die –S = (1,2,3,4,5,6) l Event –Collection of outcomes having a common characteristic l E.g.: Even number –A = (2,4,6) –Event A occurs if an outcome in the set A occurs l Probability of an event –Sum of the probabilities of the outcomes of which it consists l P(A) = P(2) + P(4) + P(6) Events : Definition

13
13 For example: –Throw a die Six possible outcomes (1,2,3,4,5,6) If each is equally-likely, the probability of each is 1/6 =.1667 = 16.67% Probability of each equally-likely outcome is 1 over the number of possible outcomes –Event A (even number) P(A) = P(2) + P(4) + P(6) = 1/6 + 1/6 + 1/6 = 1/2 for e in A Equally-likely Probabilities (Hypothetical or Ideal Experiments)

14
14 l Range of Values l Complements - Probability of not A l Intersection - Probability of both A and B –Mutually exclusive events (A and C) : l Range of Values l Complements - Probability of not A l Intersection - Probability of both A and B –Mutually exclusive events (A and C) : Basic Rules for Probability

15
15 Selamat Belajar Semoga Sukses.

Similar presentations

© 2020 SlidePlayer.com Inc.

All rights reserved.

To make this website work, we log user data and share it with processors. To use this website, you must agree to our Privacy Policy, including cookie policy.

Ads by Google