Lesson Notes By Weeks and Term v4 - SHS 1
MAKING PREDICTIONS WITH DATA
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MAKING PREDICTIONS WITH DATA
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Subject: Additional Mathematics
Class: SHS 1
Term: 2nd Term
Week: 20
Grade code: 1.4.2.LI.6
Strand code: 4
Sub-strand code: 2
Content standard code: 1.4.2.CS.1
Indicator code: 1.4.2.LI.6
Theme: HANDLING DATA
Subtheme: MAKING PREDICTIONS WITH DATA
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This lesson introduces the fascinating world of probability, which is the mathematics of chance. We are surrounded by uncertainty every day. Will it rain in Accra tomorrow? What are the chances that the Black Stars will win their next match? Will I be the one to answer the next question in class? Probability helps us measure and predict these chances. By understanding the difference between what *should* happen in theory and what *actually* happens in real life, we can make smarter decisions, from playing games like Ludo to planning a business. This lesson will be hands-on, allowing us to discover these principles ourselves through simple, fun experiments.
This topic revolves around two main types of probability. Before we explore them, let's understand the basic language we will be using. A. Basic Probability Terminology
Think about rolling a Ludo die. This simple action helps us understand all the key terms. Experiment: An activity or process with an uncertain result. *Example:* Rolling a single Ludo die. Trial: A single performance of an experiment. *Example:* Rolling the die *once*. Outcome: A possible result of a single trial. *Example:* When you roll a die, the possible outcomes are 1, 2, 3, 4, 5, or 6. Sample Space (S): The set of *all* possible outcomes of an experiment. We usually write this in curly braces `{}`. *Example:* For rolling a die, the sample space is S = {1, 2, 3, 4, 5, 6}. *Example:* For tossing a 1 Cedi coin, the sample space is S = {Head, Tail}. Event (E): A specific outcome or a set of outcomes that we are interested in. *Example:* The event of rolling an even number on a die. The outcomes for this event are {2, 4, 6}. *Example:* The event of getting a Head when tossing a coin. The outcome is {Head}. B. Theoretical Probability
This is the probability that we *expect* to happen based on logic and mathematics. It tells us what should happen in a perfect, ideal world where everything is perfectly fair.
Formula: `Theoretical Probability of an event (P(E)) = (Number of favourable outcomes) / (Total number of possible outcomes)`