PROBABILITY

Probability Concepts - Probability is used to represent the chance of an event occurring - Probabilities can be represented by fractions, decimals and percentages but NOT BY RATIOS - Probabilities lie between 0 and 1 inclusive - Impossible events have a probability of 0 - Certain events have a probability of 1 - The probability of event A is written as P(A) - To determine probability we need to know the sample space (total number of possible outcomes in an event)

Experimental Probability - Also known as long run relative frequency - When experimental results are used to determine the probability - Is calculated using: P(A) = Number of Times Event Occurs Total Number of Trials e.g. A drawing pin is tossed 300 times and lands pin down 204 times. Find the probability a tossed pin will land pin down P(Landing Pin Down) = Number of Times Event Occurs = 204 Total Number of Trials = , 0.68, or 68% also acceptable 25 e.g. A basketball player has 26 shots at the hoop and converts 22 of them. Find the probability the player will miss a shot. P(Misses shot) = 4 26 Number of Times Event Occurs = 4 (26 – 22) Total Number of Trials = 26 2, 0.15 (2 d.p.), or 15% also acceptable 13

Two Way Tables - When tables are used to calculate experimental probabilities. - Always make sure to check which group the person/item is being chosen from. e.g. A class of 35 students recorded the following sports they each played. FootballRugbyBasketballTotal Boy1062 Girl827 Total Calculate the probability that: a) A student chosen at random is a boy b) If a girl is chosen at random, she plays football c) A student chosen at random plays rugby or basketball P(Boy) = P(Football Girl) = 8 17 P(Rugby/BB player) = 17 35

Theoretical Probability - Is calculated using: P(A) = Number of Successful Outcomes Number in Sample Space - Can only be calculated if events are equally likely to occur e.g. Find the probability of getting an even number on a dice. P(even number) = 3 6 Number of Successful Outcomes = 3 (2, 4, or 6) Number in Sample Space = 6 (1, 2, 3, 4, 5, or 6) 1, 0.5, or 50% are also acceptable 2 e.g. Find the probability of a nine being drawn from a full deck. Number of Successful Outcomes = 4 (4 suits) Number in Sample Space = 52 (4 suits of Ace to King) P(Nine) = , 0.08 (2 d.p.), or 8% are also acceptable 13 - When prior knowledge is used to determine the probability

2 or More Events - If the events are independent (have no effect on each other), probabilities can then be multiplied together e.g. Find the probability of getting 3 tails when tossing 3 coins. P(3 tails) = P(tail) × P(tail) × P(tail) = 1 × 1 × = and 12.5% are also acceptable e.g. Find the probability of getting 2 tails and a head when tossing 3 coins. P(2 tails and 1 head) = P(tail) × P(tail) × P(head) × 3 = 1 × 1 × 1 × = and 37.5% are also acceptable Possible Combinations = TTH, THT, HTT

Tree Diagrams - Display all outcomes of a probability event - Always multiply along the branches and add the resulting probabilities that match the question e.g. A student walks to school 40% of the time and bikes 60%. If he walks he has an 80% chance of being late, if he bikes it is 10%. Calculate a) P(Student is late) b) P(Student is on time and biking) 1. Set up the tree diagram 2. Add probabilities (decimals/fractions) 3. Determine appropriate branch/branches Walk Bike Late On Time Late On Time a) P(Student is late) = 0.4 × 0.8 = 0.38 b)P(Student is on time and biking) = 0.6 × 0.9 = × 0.1

Tree Diagrams Continued - Sometimes harder questions can involve conditional probability e.g. From the previous example, it is found that if a student is late, the probability that they will be given a detention is 0.9. Walk Bike Late On Time Late On Time Detention No Detention Detention No Detention a)A student is chosen at random. Calculate the probability that the student biked to school and received a detention for being late P(Student biked and received a detention)= 0.6 × 0.1 × 0.9 = b) A student that walks to school is randomly chosen. Calculate the probability that they received a detention for being late. P(Student received a detention given they walked)= 0.8 × 0.9 = 0.72

Expected Number - If the probability of an event is known it can be used to predict outcomes or explain events. Expected number = Probability × Number of trials e.g. A coin is tossed 60 times. How many heads would you expect? Expected number = 0.5 × 60 = 30 e.g. If 12 cards are randomly selected from a full deck, how many diamonds would you expect? Expected number = 0.25 × 12 = 3 Note: Expected numbers are only estimations, if you actually carry out the event the results can be quite different and can be used to help decide if the probability tool is biased or not. = P(Head) × Number of trials = P(Diamond) × Number of trials

Probability Simulations - Simulations are used when the actual event cannot be reproduced or it is difficult to calculate the theoretical probability. -A simulation will often either involve the calculation of: a) The long run relative frequency of an event happening b) The average number of ‘visits’ taken to collect a full set Probability Tools Tools that can be used include: DiceCardsCoinsSpinners Random Number TableRandom Generator on a Calculator The probability tool chosen must always match the situation appropriately

Using the Random Number Generator - The Ran# button on the calculator yields a 3 digit number between and e.g. To simulate LOTTO balls in NZ use the calculator as follows 40Ran# + 1 (truncating the number to 0 d.p. every time) Number of lotto balls Rounding the numbers up to between 1 and 40 Reading only the whole number (not digits after decimal point) e.g. To simulate a situation where there is a 14% chance of success 100Ran# + 1 (truncating the number to 0 d.p. every time) For success we use the digits For failure we use the digits 1 –

Describing Simulations - Use the TTRC method to describe the four most important aspects T T R C ool - Definition of the probability tool - Statement of how the tool models the situation rial - Definition of a trial - Definition of a successful outcome of the trial (if applicable) esults - Statement of how the results will be tabulated giving an example of both a successful and unsuccessful outcome - Statement of how many trials should be carried out alculation - Statement of how the calculation needed for the conclusion will be done Either: Long-run relative frequency = Number of ‘successful’ results Number of trials Or: Mean = Sum of trial results Number of trials

Simulation Example 1 What is the probability that a four child family will contain exactly 2 boys and 2 girls? Design a simulation to solve the problem. Tool - A calculatorusing 10Ran# + 1where 1 – 5 = boy 6 – 10 = girl Trial - One trial will consist of4 randomly generated numbers to simulate a 4 child family Results TrialOutcome of trialResult of trial 1 5, 7, 3, 9  2 2, 2, 7, 1 X - Results will be recorded on a table as follows: Calculation - A successful outcome would be2 numbers between 1 – 5 (boys) and 2 numbers between 6 – 10 (girls) - 30 trials would be sufficient = number of ‘Successful’ results number of trials P(2 boys & 2 girls) - To calculate the estimated probability use:

Simulation Example 2 e.g. A cereal manufacturer includes a gift coupon in each box of a certain brand. These coupons can be exchanged for a gift when a complete set of six coupons has been collected. What is the expected number of boxes of cereal you will have to buy before you obtain a complete set of six coupons? Design a simulation. Tool - A calculatorusing 6Ran# + 1where 1 – 6 = each of the coupons Trial - One trial will consist ofrandomly generated numbers until all 6 numbers are obtained (all coupons) Results - Results will be recorded on a table as follows: Calculation - 30 trials would be sufficient = Sum of Number of Boxes number of trials Average number - To calculate the estimated number of boxes use: TrialOutcome of trialResult of trial