The document discusses basic concepts of probability, including: 1) Random conditions are those whose outcomes cannot be predicted. Probability is a number between 0 and 1 that indicates the likelihood of a random outcome. 2) Probability is determined by the relative frequency of outcomes from repeating random events over the long run or based on observations. 3) Examples are given for estimating probabilities from observed data and for simple events like lotteries.

1. KONSEP DASAR PROBABILITAS BUDHI SETIAWAN TEKNIK SIPIL UNSRI STATISTIK DAN PROBABILISTIK

2. Kondisi acak Kondisi acak adalah satu kondisi dimana hasil atau keadaan tidak dapat diprediksi Contoh: Status penyakit Anda memiliki penyakit Anda tidak memiliki penyakit Hasil test positif Hasil test negatif

3. Definisi Probabilitas Probabilitas adalah nilai antara 0 dan 1 yang dituliskan dalam bentuk desimal ataupun pecahan. Secara sederhana, Probability adalah bilangan antara 0 dan 1 yang menunjukkan suatu hasil yang diperoleh dari kondisi acak. Untuk satu susunan kemungkinan yang lengkap dalam kondisi acak, maka total atau jumlah probabilitas adalah harus sama dengan 1 .

4. Assigning Probability How likely it is that a particular outcome will be the result of a random circumstance The Relative Frequency Interpretation of Probability In situations that we can imagine repeating many times, we define the probability of a specific outcome as the proportion of times it would occur over the long run -- called the relative frequency of that particular outcome.

5. Contoh: Probabilitas dalam perencanaan transportasi Probabilitas kejadian 5 mobil menunggu untuk berbelok kanan adalah 3/60 (2/60 + 1/60) Di suatu ruas jalan direncanakan untuk membuat jalur khusus belok kanan. Probabilitas 5 mobil menunggu berbelok diperlukan untuk menentukan panjang garis pembagi jalan. Untuk keperluan ini dilakukan survey selama 2 bulan dan diperoleh 60 hasil pengamatan. . . . 0 0 8 0 0 7 1/60 1 6 2/60 2 5 3/60 3 4 14/60 14 3 20/60 20 2 16/60 16 1 4/60 4 0 Frekuensi relative Jumlah Pengamatan Banyaknya Mobil

6. Determining the Relative Frequency (Probability) of an Outcome Method 1: Make an Assumption about the Physical World (there is no bias) A Simple Lottery Choose a three-digit number between 000 and 999. Player wins if his or her three-digit number is chosen. Suppose the 1000 possible 3-digit numbers (000, 001, 002, 999) are equally likely. In long run , a player should win about 1 out of 1000 times . Probability = 0.0001 of winning. This does not mean a player will win exactly once in every thousand plays.

7. Determining the Relative Frequency (Probability) of an Outcome Method 2: Observe the Relative Frequency of random circumstances The Probability of Lost Luggage “1 in 176 passengers on U.S. airline carriers will temporarily lose their luggage .” This number is based on data collected over the long run. So the probability that a randomly selected passenger on a U.S. carrier will temporarily lose luggage is 1/176 or about 0.006.

8. Proportions and Percentages as Probabilities Ways to express the relative frequency of lost luggage: The proportion of passengers who lose their luggage is 1/176 or about 0.006 (6 out of 1000). About 0.6% of passengers lose their luggage. The probability that a randomly selected passenger will lose his/her luggage is about 0.006. The probability that you will lose your luggage is about 0.006. Last statement is not exactly correct – your probability depends on other factors (how late you arrive at the airport, etc.).

9. Estimating Probabilities from Observed Categorical Data Assuming data are representative, the probability of a particular outcome is estimated to be the relative frequency (proportion) with which that outcome was observed. Approximate margin of error for the estimated probability is

10. Nightlights and Myopia Assuming these data are representative of a larger population, what is the approximate probability that someone from that population who sleeps with a nightlight in early childhood will develop some degree of myopia ? Note : 72 + 7 = 79 of the 232 nightlight users developed some degree of myopia. So we estimate the probability to be 79/232 = 0.34. This estimate is based on a sample of 232 people with a margin of error of about 0.066 (1/√232 = ±0.666)

11. The Personal Probability Interpretation Personal probability of an event = the degree to which a given individual believes the event will happen. Sometimes subjective probability used because the degree of belief may be different for each individual. Restrictions on personal probabilities: Must fall between 0 and 1 (or between 0 and 100%). Must be coherent.

12. Probability Definitions and Relationships Sample space: collection of unique, nonoverlapping possible outcomes of a random circumstance. Simple event: one outcome in the sample space; a possible outcome of a random circumstance. Event: a collection of one or more simple events in the sample space; often written as A, B, C, and so on.

13. Assigning Probabilities to Simple Events P (A) = probability of the event A Conditions for Valid Probabilities Each probability is between 0 and 1. The sum of the probabilities over all possible simple events is 1. Equally Likely Simple Events If there are k simple events in the sample space and they are all equally likely, then the probability of the occurrence of each one is 1/ k .

14. Example: Probability of Simple Events Random Circumstance: A three-digit winning lottery number is selected. Sample Space: {000,001,002,003, . . . ,997,998,999}. There are 1000 simple events. Probabilities for Simple Event: Probability any specific three-digit number is a winner is 1/1000. Assume all three-digit numbers are equally likely. Event A = last digit is a 9 = {009,019, . . . ,999}. Since one out of ten numbers in set, P (A) = 1/10 . Event B = three digits are all the same = {000, 111, 222, 333, 444, 555, 666, 777, 888, 999}. Since event B contains 10 events, P (B) = 10/1000 = 1/100 .