# Probability - Grade 10 *

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1 OpenStax-CNX module: m Probability - Grade 10 * Rory Adams Free High School Science Texts Project Sarah Blyth Heather Williams This work is produced by OpenStax-CNX and licensed under the Creative Commons Attribution License Introduction Very little in mathematics is truly self-contained. Many branches of mathematics touch and interact with one another, and the elds of probability and statistics are no dierent. A basic understanding of probability is vital in grasping basic statistics, and probability is largely abstract without statistics to determine the "real world" probabilities. Probability theory is concerned with predicting statistical outcomes. A simple example of a statistical outcome is observing a head or tail when tossing a coin. Another simple example of a statistical outcome is obtaining the numbers 1, 2, 3, 4, 5, or 6 when rolling a die. (We say one die, many dice.) For a fair coin, heads should occur for 1 2 of the number of tosses and for a fair die, each number should occur for 1 6 of the number of rolls. Therefore, the probability of observing a head on one toss of a fair coin is 1 2 and that for obtaining a four on one roll of a fair die is 1 6. In earlier grades, the idea has been introduced that dierent situations have dierent probabilities of occurring and that for many situations there are a nite number of dierent possible outcomes. In general, events from daily life can be classied as either: certain that they will happen; or certain that they will not happen; or uncertain. This chapter builds on earlier work and describes how to calculate the probability associated with dierent situations, and describes how probability is used to assign a number describing the level of chance or the odds associated with aspects of life. The meanings of statements like: `The HIV test is 85% reliable.' will also be explained. 2 Random Experiments The term random experiment or statistical experiment is used to describe any repeatable experiment or situation. * Version 1.4: Mar 30, :57 am

2 OpenStax-CNX module: m The term random experiment or statistical experiment is used to describe ay repeatable experiment or situation. To attain any meaningful information from an experiment we rst need to understand 3 key concepts: outcome, event and sample space. 2.1 Outcomes, Sample Space and Events We will be using 2 experiments to illustrate the concepts: Experiment 1 will be the value of a single die that is thrown Experiment 2 will be the value of 2 die that are thrown at the same time Outcome The outcome of an experiment is a single result of the experiment. In the case of experiment 1, throwing a 4 would be a single outcome Sample Space The sample space of an experiment is the complete set of outcomes that are possible as a result of the experiment. Experiment 1: the sample space is 1,2,3,4,5,6 Experiment 2: the sample space is 2,3,4,5,6,7,8,9,10,11, Event An event can be dened as the combination of outcomes that you are interested in. Experiment 1: The event that looks at all the even numbers is given as 2,4,6 Experiment 2: For experiment 2 it is given as 2,4,6,8,10,12 A Venn diagram can be used to show the relationship between the outcomes of a random experiment, the sample space and events associated with the outcomes. The Venn diagram in Figure 1 shows the dierence between the universal set, a sample space and events and outcomes as subsets of the sample space.

3 OpenStax-CNX module: m Figure 1: Diagram to show dierence between the universal set and the sample space. The sample space is made up of all possible outcomes of a statistical experiment and an event is a subset of the sample space. Venn diagrams can also be used to indicate the union and intersection between events in a sample space (Figure 2).

4 OpenStax-CNX module: m Figure 2: Venn diagram to show (left) union of two events, A and B, in the sample space S and (right) intersection of two events A and B, in the sample space S. The crosshatched region indicates the intersection. Exercise 1: Random Experiments (Solution on p. 14.) In a box there are pieces of paper with the numbers from 1 to 9 written on them. S = {1; 2; 3; 4; 5; 6; 7; 8; 9} Random Experiments 1. S = {whole numbers from1to16}, X = {even numbers from1to16} and Y = {prime numbers from1to16} a. Draw a Venn diagram S, X and Y. b. Write down n (S), n (X), n (Y ), n (X Y ), n (X Y ). Click here for the solution There are 79 Grade 10 learners at school. All of these take either Maths, Geography or History. The number who take Geography is 41, those who take History is 36, and 30 take Maths. The number who take Maths and History is 16; the number who take Geography and History is 6, and there are 8 who take Maths only and 16 who take only History. a. Draw a Venn diagram to illustrate all this information. b. How many learners take Maths and Geography but not History? c. How many learners take Geography only? d. How many learners take all three subjects? Click here for the solution

5 OpenStax-CNX module: m Pieces of paper labelled with the numbers 1 to 12 are placed in a box and the box is shaken. One piece of paper is taken out and then replaced. a. What is the sample space, S? b. Write down the set A, representing the event of taking a piece of paper labelled with a factor 12. c. Write down the set B, representing the event of taking a piece of paper labelled with a prime number. d. Represent A, B and S by means of a Venn diagram. e. Write down i. n (S) ii. n (A) iii. n (B) iv. n (A B) v. n (A B) f. Is n (A B) = n (A) + n (B) n (A B)? Click here for the solution. 3 3 Probability Models The word probability relates to uncertain events or knowledge, being closely related in meaning to likely, risky, hazardous, and doubtful. Chance, odds, and bet are other words expressing similar ideas. Probability is connected with uncertainty. In any statistical experiment, the outcomes that occur may be known, but exactly which one might not be known. Mathematically, probability theory formulates incomplete knowledge pertaining to the likelihood of an occurrence. For example, a meteorologist might say there is a 60% chance that it will rain tomorrow. This means that in 6 of every 10 times when the world is in the current state, it will rain tomorrow. A probability is a real number between 0 and 1. In everyday speech, probabilities are usually given as a percentage between 0% and 100%. A probability of 100% means that an event is certain, whereas a probability of 0% is often taken to mean the event is impossible. However, there is a distinction between logically impossible and occurring with zero probability; for example, in selecting a number uniformly between 0 and 1, the probability of selecting 1/2 is 0, but it is not logically impossible. Further, it is certain that whichever number is selected will have had a probability of 0 of being selected. Another way of referring to probabilities is odds. The odds of an event is dened as the ratio of the probability that the event occurs to the probability that it does not occur. For example, the odds of a coin landing on a given side are = 1, usually written "1 to 1" or "1:1". This means that on average, the coin will land on that side as many times as it will land on the other side. 3.1 Classical Theory of Probability 1. Equally likely outcomes are outcomes which have an equal chance of happening. For example when a fair coin is tossed, each outcome in the sample space S = heads, tails is equally likely to occur. 2. When all the outcomes are equally likely (in any activity), you can calculate the probability of an event happening by using the following denition: P(E)=number of favourable outcomes/total number of possible outcomes P(E)=n(E)/n(S) For example, when you throw a fair dice the possible outcomes are S = {1; 2; 3; 4; 5; 6} i.e the total number of possible outcomes n(s)=6. Event 1: get a 4 The only possible outcome is a 4, i.e E=4 i.e number of favourable outcomes: n(e)=1. Probability of getting a 4 = P(4)=n(E)/n(S)=1/6. 3

6 OpenStax-CNX module: m Event 2: get a number greater than 3 Favourable outcomes: E = {4; 5; 6} Number of favourable outcomes: n(e)=3 Probability of getting a number more than 3 = P(more than 3) = n(e)/n(s)=3/6=1/2 Exercise 2: Classical Probability (Solution on p. 14.) A standard deck of cards (without jokers) has 52 cards. There are 4 sets of cards, called suites. The suite a card belongs to is denoted by either a symbol on the card, the 4 symbols are a heart, club, spade and diamond. In each suite there are 13 cards (4 suites 13 cards = 52) consisting of one each of ace, king, queen, jack, and the numbers If we randomly draw a card from the deck, we can think of each card as a possible outcome. Therefore, there are 52 possible outcomes. We can now look at various events and calculate their probabilities: 1. Out of the 52 cards, there are 13 clubs. Therefore, if the event of interest is drawing a club, there are 13 favourable outcomes, what is the probability of this event? 2. There are 4 kings (one of each suit). The probability of drawing a king is? 3. What is the probability of drawing a king OR a club? Probability Models 1. A bag contains 6 red, 3 blue, 2 green and 1 white balls. A ball is picked at random. What is the probablity that it is: a. red b. blue or white c. not green d. not green or red? Click here for the solution A card is selected randomly from a pack of 52. What is the probability that it is: a. the 2 of hearts b. a red card c. a picture card d. an ace e. a number less than 4? Click here for the solution Even numbers from are written on cards. What is the probability of selecting a multiple of 5, if a card is drawn at random? Click here for the solution. 6 4 Relative Frequency vs. Probability There are two approaches to determining the probability associated with any particular event of a random experiment: 1. determining the total number of possible outcomes and calculating the probability of each outcome using the denition of probability

7 OpenStax-CNX module: m performing the experiment and calculating the relative frequency of each outcome Relative frequency is dened as the number of times an event happens in a statistical experiment divided by the number of trials conducted. It takes a very large number of trials before the relative frequency of obtaining a head on a toss of a coin approaches the probability of obtaining a head on a toss of a coin. For example, the data in Table 1 represent the outcomes of repeating 100 trials of a statistical experiment 100 times, i.e. tossing a coin 100 times. H T T H H T H H H H H H H H T H H T T T T T H T T H T H T H H H T T H T T H T T T H H H T T H T T H H T T T T H T T H H T T H T T H T T H T H T T H T T T T H T T H T T H H H T H T T T T H H T T T H T Table 1: Results of 100 tosses of a fair coin. H means that the coin landed heads-up and T means that the coin landed tails-up. The following two worked examples show that the relative frequency of an event is not necessarily equal to the probability of the same event. Relative frequency should therefore be seen as an approximation to probability. Exercise 3: Relative Frequency and Probability (Solution on p. 15.) Determine the relative frequencies associated with each outcome of the statistical experiment detailed in Table 1. Exercise 4: Probability (Solution on p. 15.) Determine the probability associated with an evenly weighted coin landing on either of its faces. 5 Project Idea Perform an experiment to show that as the number of trials increases, the relative frequency approaches the probability of a coin toss. Perform 10, 20, 50, 100, 200 trials of tossing a coin. 6 Probability Identities The following results apply to probabilities, for the sample space S and two events A and B, within S. P (S) = 1 (2) P (A B) = P (A) P (B) (2)

9 OpenStax-CNX module: m Mutually Exclusive Events Mutually exclusive events are events, which cannot be true at the same time. Examples of mutually exclusive events are: 1. A die landing on an even number or landing on an odd number. 2. A student passing or failing an exam 3. A tossed coin landing on heads or landing on tails This means that if we examine the elements of the sets that make up A and B there will be no elements in common. Therefore, A B = (where refers to the empty set). Since, P (A B) = 0, equation (2) becomes: for mutually exclusive events. P (A B) = P (A) + P (B) (3) 7.1 Mutually Exclusive Events Answer the following questions 1. A box contains coloured blocks. The number of each colour is given in the following table. Colour Purple Orange White Pink Number of blocks Table 2 A block is selected randomly. What is the probability that the block will be: a. purple b. purple or white c. pink and orange d. not orange? Click here for the solution A small private school has a class with children of various ages. The table gies the number of pupils of each age in the class. 3 years female 3 years male 4 years female 4 years male 5 years female 5 years male Table 3 If a pupil is selceted at random what is the probability that the pupil will be: a. a female b. a 4 year old male c. aged 3 or 4 d. aged 3 and 4 e. not 5 f. either 3 or female? 12

10 OpenStax-CNX module: m Click here for the solution Fiona has 85 labeled discs, which are numbered from 1 to 85. If a disc is selected at random what is the probability that the disc number: a. ends with 5 b. can be multiplied by 3 c. can be multiplied by 6 d. is number 65 e. is not a multiple of 5 f. is a multiple of 4 or 3 g. is a multiple of 2 and 6 h. is number 1? Click here for the solution Complementary Events The probability of complementary events refers to the probability associated with events not occurring. For example, if P (A) = 0.25, then the probability of A not occurring is the probability associated with all other events in S occurring less the probability of A occurring. This means that P ( A ') = 1 P (A) (3) where A' refers to `not A' In other words, the probability of `not A' is equal to one minus the probability of A. Exercise 7: Probability (Solution on p. 16.) If you throw two dice, one red and one blue, what is the probability that at least one of them will be a six? Exercise 8: Probability (Solution on p. 16.) A bag contains three red balls, ve white balls, two green balls and four blue balls: 1. Calculate the probability that a red ball will be drawn from the bag. 2. Calculate the probability that a ball which is not red will be drawn 8.1 Interpretation of Probability Values The probability of an event is generally represented as a real number between 0 and 1, inclusive. An impossible event has a probability of exactly 0, and a certain event has a probability of 1, but the converses are not always true: probability 0 events are not always impossible, nor probability 1 events certain. The rather subtle distinction between "certain" and "probability 1" is treated at greater length in the article on "almost surely". Most probabilities that occur in practice are numbers between 0 and 1, indicating the event's position on the continuum between impossibility and certainty. The closer an event's probability is to 1, the more likely it is to occur. For example, if two mutually exclusive events are assumed equally probable, such as a ipped or spun coin landing heads-up or tails-up, we can express the probability of each event as "1 in 2", or, equivalently, "50%" or "1/2". Probabilities are equivalently expressed as odds, which is the ratio of the probability of one event to the probability of all other events. The odds of heads-up, for the tossed/spun coin, are (1/2)/(1-1/2), which is equal to 1/1. This is expressed as "1 to 1 odds" and often written "1:1"

13 OpenStax-CNX module: m Thuli has a bag containing ve orange, three purple and seven pink blocks. The bag is shaken and a block is withdrawn. The colour of the block is noted and the block is replaced. a. What is the sample space for this experiment? b. What is the set describing the event of drawing a pink block, P? c. Write down a set, O or B, to represent the event of drawing either a orange or a purple block. d. Draw a Venn diagram to show the above information. Click here for the solution

14 OpenStax-CNX module: m Solutions to Exercises in this Module Solution to Exercise (p. 4) Step 1. Drawing a prime number: P = {2; 3; 5; 7} Drawing an even number: E = {2; 4; 6; 8} Step 2. Figure 4 Step 3. The union of P and E is the set of all elements in P or in E (or in both). P or E = 2, 3, 4, 5, 6, 7, 8. P or E is also written P E. Step 4. The intersection of P and E is the set of all elements in both P and E. P and E = 2. P and E is also written as P E. Step 5. We use n (S) to refer to the number of elements in a set S, n (X) for the number of elements in X, etc. n (S) = 9 n (P ) = 4 n (E) = 4 n (P E) = 7 n (P E) = 2 (4) Solution to Exercise (p. 6) Step 1. The probability of this event is = 1 4.

15 OpenStax-CNX module: m Step = Step 3. This example is slightly more complicated. We cannot simply add together the number of number of outcomes for each event separately ( = 17) as this inadvertently counts one of the outcomes twice (the king of clubs). The correct answer is Solution to Exercise (p. 7) Step 1. There are two unique outcomes: H and T. Step 2. Outcome H 44 T 56 Table 4 Frequency Step 3. The statistical experiment of tossing the coin was performed 100 times. Therefore, there were 100 trials, in total. Step 4. frequency of outcome Probability of H = number of trials 44 = 100 = 0, 44 frequency of outcome Relative Frequency of T = number of trials 56 = 100 = 0, 56 The relative frequency of the coin landing heads-up is 0,44 and the relative frequency of the coin landing tails-up is 0,56. Solution to Exercise (p. 7) Step 1. There are two unique outcomes: H and T. Step 2. There are two possible outcomes. Step 3. number of favourable outcomes Relative Frequency of H = total number of outcomes 1 = 2 = 0, 5 number of favourable outcomes Relative Frequency of T = total number of outcomes 1 = 2 = 0, 5 The probability of an evenly weighted coin landing on either face is 0, 5. Solution to Exercise (p. 8) Step 1. P(S)=n(E)/n(S)=52/52=1. because all cards are black or red! Solution to Exercise (p. 8) (4) (4)

16 OpenStax-CNX module: m Step 1. Step 2. P (club ace) = P (club) + P (ace) P (club ace) (4) = ( 1 4 ) = = 52 4 = 13 Notice how we have used P (C A) = P (C) + P (A) P (C A). Solution to Exercise (p. 10) Step 1. To solve that kind of question, work out the probability that there will be no six. Step 2. The probability that the red dice will not be a six is 5/6, and that the blue one will not be a six is also 5/6. Step 3. So the probability that neither will be a six is 5/6 5/6 = 25/36. Step 4. So the probability that at least one will be a six is 1 25/36 = 11/36. Solution to Exercise (p. 10) Step 1. Let R be the event that a red ball is drawn: P(R)-n(R)/n(S)=3/14 R and R' are complementary events Step 2. P(R') = 1 - P(R) = 1-3/14 = 11/14 Step 3. Alternately P(R') = P(B) + P(W) + P(G) P(R') = 4/14 + 5/14 + 2/14 = 11/14 (4)

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