LESSON 1

Statistics

• Study of collection, analysis, interpretation, presentation, and organization of data.

• Applications range from small to large scale.

Probability

• Denotes the likelihood of the outcome of random events.

• Example: Probability of getting heads when flipping a coin depends on possible outcomes.

Random Experiment

• Defined as a process to draw outcomes with randomness.

• Possible outcomes can be determined but not specific; they have defined probabilities.

Random Variables

• Function assigning numerical values to possible outcomes of a random experiment.

• Commonly denoted by upper case letters (X, Y).

• Can be discrete or continuous.

Discrete Random Variables

• Takes distinct, listable values; may have finite or infinite number of possible values.

• Example: Rolling a die results in possible values {1, 2, 3, 4, 5, 6}.

Sample Space

• Set of all possible outcomes of an experiment.

Key Differences

• Discrete Random Variable: Values obtained by counting; examples: number of students, test questions.

• Continuous Random Variable: Values obtained by measuring; examples: height, time, amount of rainfall.

Classification Activity

• Example outcomes classified as discrete or continuous:

  • Defective computers: Discrete

  • Weight of newborns: Continuous

  • Number of siblings: Discrete

  • Time needed for a test: Continuous

  • Number of dropouts: Discrete

  • Speed of a car: Continuous

  • Number of female athletes: Discrete

  • Amount of sugar in coffee: Continuous

  • Number of LOTTO players: Discrete