Definitions POPULATION

Definitions POPULATION
(using basic Set Theory) An outcome is the result of an experiment on a population. Sample Space The set of all possible outcomes of an experiment.  = {Red, Orange, Yellow, Green, Blue} #( ) = 5 Venn Diagram Red Orange Yellow Consider the following experiment: Randomly select an individual from the population, and record its color. E Green Blue Event Any subset of  (including the empty set , and  itself). Outcome Red Orange Yellow Green Blue E = “Primary Color” = {Red, Yellow, Blue} #(E) = 3 ways Note that, even though the population may be considered infinite (i.e., “arbitrarily large”), the sample space need not be! However, they are often quite large, or indeed, can be infinite as well.

(using basic Set Theory) An outcome is the result of an experiment on a population. Sample Space The set of all possible outcomes of an experiment. Sample Space The set of all possible outcomes of an experiment.  = {Red, Orange, Yellow, Green, Blue} #( ) = 5 Venn Diagram Red Orange Yellow F Consider the following experiment: Randomly select an individual from the population, and record its color. Green Blue Event Any subset of  (including the empty set , and  itself). Outcome Red Orange Yellow Green Blue E = “Primary Color” = {Red, Yellow, Blue} #(E) = 3 ways F = “Hot Color” = {Red, Orange, Yellow} #(F) = 3 ways

(using basic Set Theory) An outcome is the result of an experiment on a population. Sample Space The set of all possible outcomes of an experiment. Sample Space The set of all possible outcomes of an experiment.  = {Red, Orange, Yellow, Green, Blue} #( ) = 5 Venn Diagram Red Orange Yellow F Consider the following experiment: Randomly select an individual from the population, and record its color. Green Blue Event Any subset of  (including the empty set , and  itself). Outcome Red Orange Yellow Green Blue E = “Primary Color” = {Red, Yellow, Blue} #(E) = 3 ways F = “Hot Color” = {Red, Orange, Yellow} #(F) = 3 ways Complement F C = “Not F” = “Cold Color” = {Green, Blue} #(FC) = 2 ways

(using basic Set Theory) An outcome is the result of an experiment on a population. Sample Space The set of all possible outcomes of an experiment. Sample Space The set of all possible outcomes of an experiment.  = {Red, Orange, Yellow, Green, Blue} #( ) = 5 Venn Diagram Red Orange Yellow F Consider the following experiment: Randomly select an individual from the population, and record its color. Green Blue Event Any subset of  (including the empty set , and  itself). Outcome Red Orange Yellow Green Blue E = “Primary Color” = {Red, Yellow, Blue} #(E) = 3 ways F = “Hot Color” = {Red, Orange, Yellow} #(F) = 3 ways “Cold Color” = {Green, Blue} “Not F” = Complement F C = #(FC) = 2 ways

(using basic Set Theory) An outcome is the result of an experiment on a population. Sample Space The set of all possible outcomes of an experiment. Sample Space The set of all possible outcomes of an experiment.  = {Red, Orange, Yellow, Green, Blue} #( ) = 5 Venn Diagram Red Orange Yellow F Consider the following experiment: Randomly select an individual from the population, and record its color. E Green Blue Event Any subset of  (including the empty set , and  itself). Outcome Red Orange Yellow Green Blue E = “Primary Color” = {Red, Yellow, Blue} #(E) = 3 ways F = “Hot Color” = {Red, Orange, Yellow} #(F) = 3 ways “Cold Color” = {Green, Blue} “Not F” = Complement F C = #(FC) = 2 ways Intersection E ⋂ F = “E and F” = {Red, Yellow}

(using basic Set Theory) An outcome is the result of an experiment on a population. Sample Space The set of all possible outcomes of an experiment. Sample Space The set of all possible outcomes of an experiment.  = {Red, Orange, Yellow, Green, Blue} #( ) = 5 Venn Diagram Red Orange Yellow F Consider the following experiment: Randomly select an individual from the population, and record its color. E Green Blue Event Any subset of  (including the empty set , and  itself). Outcome Red Orange Yellow Green Blue E = “Primary Color” = {Red, Yellow, Blue} #(E) = 3 ways F = “Hot Color” = {Red, Orange, Yellow} #(F) = 3 ways Complement F C = “Not F” = “Cold Color” = {Green, Blue} #(FC) = 2 ways Intersection E ⋂ F = {Red, Yellow} “E and F” = #(E ⋂ F) = 2

(using basic Set Theory) An outcome is the result of an experiment on a population. Sample Space The set of all possible outcomes of an experiment. Sample Space The set of all possible outcomes of an experiment.  = {Red, Orange, Yellow, Green, Blue} #( ) = 5 Venn Diagram A B Red Orange Yellow Consider the following experiment: Randomly select an individual from the population, and record its color. Green Blue Event Any subset of  (including the empty set , and  itself). Outcome Red Orange Yellow Green Blue If two events are disjoint, then… they cannot occur simultaneously, i.e., if one event does occur, the other does not occur. E = “Primary Color” = {Red, Yellow, Blue} #(E) = 3 ways F = “Hot Color” = {Red, Orange, Yellow} #(F) = 3 ways “Cold Color” = {Green, Blue} “Not F” = Complement F C = #(FC) = 2 ways Intersection E ⋂ F = {Red, Yellow} “E and F” = #(E ⋂ F) = 2 Note: A = {Red, Green} ⋂ B = {Orange, Blue} =  A and B are disjoint, or mutually exclusive events

(using basic Set Theory) An outcome is the result of an experiment on a population. Sample Space The set of all possible outcomes of an experiment. Sample Space The set of all possible outcomes of an experiment.  = {Red, Orange, Yellow, Green, Blue} #( ) = 5 Venn Diagram Red Orange Yellow F Consider the following experiment: Randomly select an individual from the population, and record its color. E Green Blue Event Any subset of  (including the empty set , and  itself). Outcome Red Orange Yellow Green Blue E = “Primary Color” = {Red, Yellow, Blue} #(E) = 3 ways F = “Hot Color” = {Red, Orange, Yellow} #(F) = 3 ways “Cold Color” = {Green, Blue} “Not F” = Complement F C = #(FC) = 2 ways Intersection E ⋂ F = {Red, Yellow} “E and F” = #(E ⋂ F) = 2 Note: A = {Red, Green} ⋂ B = {Orange, Blue} =  A and B are disjoint, or mutually exclusive events “E or F” =

(using basic Set Theory) An outcome is the result of an experiment on a population. Sample Space The set of all possible outcomes of an experiment. Sample Space The set of all possible outcomes of an experiment.  = {Red, Orange, Yellow, Green, Blue} #( ) = 5 Venn Diagram Red Orange Yellow F Consider the following experiment: Randomly select an individual from the population, and record its color. E Green Blue Event Any subset of  (including the empty set , and  itself). Outcome Red Orange Yellow Green Blue E = “Primary Color” = {Red, Yellow, Blue} #(E) = 3 ways F = “Hot Color” = {Red, Orange, Yellow} #(F) = 3 ways “Cold Color” = {Green, Blue} “Not F” = Complement F C = #(FC) = 2 ways Intersection E ⋂ F = {Red, Yellow} “E and F” = #(E ⋂ F) = 2 Note: A = {Red, Green} ⋂ B = {Orange, Blue} =  A and B are disjoint, or mutually exclusive events Union E ⋃ F = “E or F” = {Red, Orange, Yellow, Blue} #(E ⋃ F) = 4

In general, for any two events A and B, there are 4 disjoint intersections:
A ⋂ Bc A\B A - B Ac ⋂ B B\A B - A A ⋂ B “A and B” “A only” “B only” Ac ⋂ Bc “Neither A nor B” DeMorgan’s Laws (A ⋃ B)c = Ac ⋂ Bc “Not (A or B)” = “Not A” and “Not B” = “Neither A nor B” (A ⋂ B)c = Ac ⋃ Bc “Not (A and B)” = “Not A” or “Not B” A B A B

What about probability of outcomes?
Definitions POPULATION (using basic Set Theory) An outcome is the result of an experiment on a population. Sample Space The set of all possible outcomes of an experiment. Sample Space The set of all possible outcomes of an experiment.  = {Red, Orange, Yellow, Green, Blue} #( ) = 5 Venn Diagram Red Orange Yellow F Consider the following experiment: Randomly select an individual from the population, and record its color. E Green Blue Event Any subset of  (including the empty set , and  itself). Outcome Red Orange Yellow Green Blue E = “Primary Color” = {Red, Yellow, Blue} #(E) = 3 ways What about probability of outcomes? F = “Hot Color” = {Red, Orange, Yellow} #(F) = 3 ways “Cold Color” = {Green, Blue} “Not F” = Complement F C = #(FC) = 2 ways Intersection E ⋂ F = {Red, Yellow} “E and F” = #(E ⋂ F) = 2 Note: A = {Red, Green} ⋂ B = {Orange, Blue} =  A and B are disjoint, or mutually exclusive events Union E ⋃ F = “E or F” = {Red, Orange, Yellow, Blue} #(E ⋃ F) = 4

POPULATION (Pie Chart)
Definitions POPULATION (using basic Set Theory) An outcome is the result of an experiment on a population. Sample Space The set of all possible outcomes of an experiment. Sample Space The set of all possible outcomes of an experiment.  = {Red, Orange, Yellow, Green, Blue} #( ) = 5 Venn Diagram Red Yellow Green Orange Blue E F Perform repeated trials of the following experiment: Randomly select an individual from the population, and record its color. Consider the following experiment: Randomly select an individual from the population, and record its color. Event Any subset of S (including the empty set , and S itself). # Red # trials Outcome Red Orange Yellow Green Blue Outcome Probability Red 0.20 Orange Yellow Green Blue 1.00 E = “Primary Color” = {Red, Yellow, Blue} #(E) = 3 ways “The probability of Red is equal to 0.20” F = “Hot Color” = {Red, Orange, Yellow} #(F) = 3 ways “Cold Color” = {Green, Blue} “Not F” = Complement F C = What happens to this “long run” relative frequency as # trials → ∞? P(Red) = 0.20 #(FC) = 2 ways …… But what does it mean?? Intersection E ⋂ F = {Red, Yellow} “E and F“ = #(E ⋂ F) = 2 Note: A = {Red, Green} ⋂ B = {Orange, Blue} =  … A and B are disjoint, or mutually exclusive events Union E ⋃ F = “E or F” = {Red, Orange, Yellow, Blue} # trials All probs are > 0, and sum = 1. #(E ⋃ F) = 4

# R code for colors.r R = rep("Red", ) O = rep("Orange", ) Y = rep("Yellow", ) G = rep("Green", ) B = rep("Blue", ) pop = c(R, O, Y, G, B) plot.new() plot(0, 0, type = "n", axes = F, xlim = c(0, 300), ylim = c(0, 1), xlab = "n = # Trials", ylab = "#(Red) / n") axis(1) axis(2) box() lines(c(0, 300), c(.2, .2), lty = 2) i = 0 for (n in 1:300) { color = sample(pop, 1, replace = TRUE) if (color == "Red") i = i+1 relfreq = i/n points(n, relfreq, pch=19, cex = .5, col = "red") } Consider the following experiment: Randomly select an individual from the population, and record its color. Perform repeated trials of the following experiment: Randomly select an individual from the population, and record its color. Event Any subset of S (including the empty set , and S itself). Outcome Probability Red 0.20 Orange Yellow Green Blue 1.00 Outcome Red Orange Yellow Green Blue E = “Primary Color” = {Red, Yellow, Blue} #(E) = 3 ways “The probability of Red is equal to 0.20” F = “Hot Color” = {Red, Orange, Yellow} #(F) = 3 ways “Cold Color” = {Green, Blue} “Not F” = Complement F C = P(Red) = 0.20 #(FC) = 2 ways …… But what does it mean?? Intersection E ⋂ F = {Red, Yellow} “E and F“ = #(E ⋂ F) = 2 Note: A = {Red, Green} ⋂ B = {Orange, Blue} =  … A and B are disjoint, or mutually exclusive events Union E ⋃ F = “E or F” = {Red, Orange, Yellow, Blue} All probs are > 0, and sum = 1. #(E ⋃ F) = 4

Definitions (using basic Set Theory) An outcome is the result of an experiment on a population. Sample Space The set of all possible outcomes of an experiment. Sample Space The set of all possible outcomes of an experiment.  = {Red, Orange, Yellow, Green, Blue} #( ) = 5 Venn Diagram Red Yellow Green Orange Blue E F Perform repeated trials of the following experiment: Randomly select an individual from the population, and record its color. Consider the following experiment: Randomly select an individual from the population, and record its color. Event Any subset of S (including the empty set , and S itself). Outcome Probability Red 0.20 Orange Yellow Green Blue 1.00 Outcome Red Orange Yellow Green Blue E = “Primary Color” = {Red, Yellow, Blue} #(E) = 3 ways “The probability of Red is equal to 0.20” F = “Hot Color” = {Red, Orange, Yellow} #(F) = 3 ways “Cold Color” = {Green, Blue} “Not F” = Complement F C = P(Red) = 0.20 #(FC) = 2 ways …… But what does it mean?? Intersection E ⋂ F = {Red, Yellow} “E and F“ = #(E ⋂ F) = 2 Note: A = {Red, Green} ⋂ B = {Orange, Blue} =  A and B are disjoint, or mutually exclusive events Union E ⋃ F = “E or F” = {Red, Orange, Yellow, Blue} All probs are > 0, and sum = 1. #(E ⋃ F) = 4

Definitions (using basic Set Theory) An outcome is the result of an experiment on a population. Sample Space The set of all possible outcomes of an experiment. Sample Space The set of all possible outcomes of an experiment.  = {Red, Orange, Yellow, Green, Blue} #( ) = 5 Venn Diagram Red Yellow Green Orange Blue E F Perform repeated trials of the following experiment: Randomly select an individual from the population, and record its color. Event Any subset of S (including the empty set , and S itself). Outcome Probability Red 0.20 Orange Yellow Green Blue 1.00 E = “Primary Color” = {Red, Yellow, Blue} #(E) = 3 ways What about probability of events? F = “Hot Color” = {Red, Orange, Yellow} General Fact: #(F) = 3 ways “Cold Color” = {Green, Blue} “Not F” = Complement F C = For any event E, P(E) =  P(Outcomes in E). #(FC) = 2 ways Intersection E ⋂ F = {Red, Yellow} “E and F“ = #(E ⋂ F) = 2 Note: A = {Red, Green} ⋂ B = {Orange, Blue} =  A and B are disjoint, or mutually exclusive events BUT… Union E ⋃ F = “E or F“ = {Red, Orange, Yellow, Blue} All probs are > 0, and sum = 1. #(E ⋃ F) = 4

Definitions (using basic Set Theory) An outcome is the result of an experiment on a population. Sample Space The set of all possible outcomes of an experiment. Sample Space The set of all possible outcomes of an experiment.  = {Red, Orange, Yellow, Green, Blue} #( ) = 5 Venn Diagram Red Yellow Green Orange Blue E F Perform repeated trials of the following experiment: Randomly select an individual from the population, and record its color. Event Any subset of  (including the empty set , and  itself). Outcome Probability Red 0.20 Orange Yellow Green Blue 1.00 E = “Primary Color” = {Red, Yellow, Blue} P(E) = 3/5 = 0.6 #(E) = 3 ways F = “Hot Color” = {Red, Orange, Yellow} P(F) = 3/5 = 0.6 #(F) = 3 ways These outcomes are said to be “equally likely.” “Cold Color” = {Green, Blue} “Not F” = Complement F C = P(FC) = 2/5 = 0.4 #(FC) = 2 ways Intersection E ⋂ F = {Red, Yellow} “E and F” = #(E ⋂ F) = 2 P(E ⋂ F) = 0.4 Note: A = {Red, Green} ⋂ B = {Orange, Blue} =  P() = 0 A and B are disjoint, or mutually exclusive events Union E ⋃ F = “E or F“ = {Red, Orange, Yellow, Blue} All probs are > 0, and sum = 1. P(E ⋃ F) = 4/5 = 0.8 #(E ⋃ F) = 4

Definitions (using basic Set Theory) An outcome is the result of an experiment on a population. Sample Space The set of all possible outcomes of an experiment.  = {Red, Orange, Yellow, Green, Blue}. P( ) = 1 Venn Diagram Red Yellow Green Orange Blue E F Perform repeated trials of the following experiment: Randomly select an individual from the population, and record its color. Event Any subset of  (including the empty set , and  itself). Outcome Probability Red 0.20 Orange Yellow Green Blue 1.00 Outcome Probability Red 0.10 Orange 0.15 Yellow 0.20 Green 0.25 Blue 0.30 1.00 E = “Primary Color” = {Red, Yellow, Blue} P(E) = 3/5 = 0.6 F = “Hot Color” = {Red, Orange, Yellow} P(F) = 3/5 = 0.6 These outcomes are said to be “equally likely.” These outcomes are NOT “equally likely.” “Cold Color” = {Green, Blue} “Not F” = Complement F C = P(FC) = 2/5 = 0.4 Intersection E ⋂ F = {Red, Yellow} “E and F” = P(E ⋂ F) = 0.4 Note: A = {Red, Green} ⋂ B = {Orange, Blue} =  P() = 0 A and B are disjoint, or mutually exclusive events Union E ⋃ F = “E or F“ = {Red, Orange, Yellow, Blue} All probs are > 0, and sum = 1. P(E ⋃ F) = 4/5 = 0.8

Definitions (using basic Set Theory) An outcome is the result of an experiment on a population. Sample Space The set of all possible outcomes of an experiment. Sample Space The set of all possible outcomes of an experiment.  = {Red, Orange, Yellow, Green, Blue} P( ) = 1 Venn Diagram Red Yellow Green Orange Blue E F Perform repeated trials of the following experiment: Randomly select an individual from the population, and record its color. Event Any subset of  (including the empty set , and  itself). Outcome Probability Red 0.10 Orange 0.15 Yellow 0.20 Green 0.25 Blue 0.30 1.00 E = “Primary Color” = {Red, Yellow, Blue} P(E) = 0.60 F = “Hot Color” = {Red, Orange, Yellow} “Cold Color” = {Green, Blue} “Not F” = Complement F C = Intersection E ⋂ F = {Red, Yellow} “E and F” = Note: A = {Red, Green} ⋂ B = {Orange, Blue} =  P() = 0 A and B are disjoint, or mutually exclusive events Union E ⋃ F = “E or F“ = {Red, Orange, Yellow, Blue} All probs are > 0, and sum = 1.

Definitions (using basic Set Theory) An outcome is the result of an experiment on a population. Sample Space The set of all possible outcomes of an experiment. Sample Space The set of all possible outcomes of an experiment.  = {Red, Orange, Yellow, Green, Blue} P( ) = 1 Venn Diagram Red Yellow Green Orange Blue E F Perform repeated trials of the following experiment: Randomly select an individual from the population, and record its color. Event Any subset of  (including the empty set , and  itself). Outcome Probability Red 0.10 Orange 0.15 Yellow 0.20 Green 0.25 Blue 0.30 1.00 E = “Primary Color” = {Red, Yellow, Blue} P(E) = 0.60 F = “Hot Color” = {Red, Orange, Yellow} P(F) = 0.45 “Cold Color” = {Green, Blue} “Not F” = Complement F C = Intersection E ⋂ F = {Red, Yellow} “E and F” = Note: A = {Red, Green} ⋂ B = {Orange, Blue} =  P() = 0 A and B are disjoint, or mutually exclusive events Union E ⋃ F = “E or F“ = {Red, Orange, Yellow, Blue} All probs are > 0, and sum = 1.

Definitions (using basic Set Theory) An outcome is the result of an experiment on a population. Sample Space The set of all possible outcomes of an experiment. Sample Space The set of all possible outcomes of an experiment.  = {Red, Orange, Yellow, Green, Blue} P( ) = 1 Venn Diagram Red Yellow Green Orange Blue E F Perform repeated trials of the following experiment: Randomly select an individual from the population, and record its color. Event Any subset of  (including the empty set , and  itself). Outcome Probability Red 0.10 Orange 0.15 Yellow 0.20 Green 0.25 Blue 0.30 1.00 E = “Primary Color” = {Red, Yellow, Blue} P(E) = 0.60 F = “Hot Color” = {Red, Orange, Yellow} P(F) = 0.45 “Cold Color” = {Green, Blue} “Not F” = Complement F C = P(FC) = 1 – P(F) = 0.55 Intersection E ⋂ F = {Red, Yellow} “E and F” = Note: A = {Red, Green} ⋂ B = {Orange, Blue} =  P() = 0 A and B are disjoint, or mutually exclusive events Union E ⋃ F = “E or F“ = {Red, Orange, Yellow, Blue} All probs are > 0, and sum = 1.

Definitions (using basic Set Theory) An outcome is the result of an experiment on a population. Sample Space The set of all possible outcomes of an experiment. Sample Space The set of all possible outcomes of an experiment.  = {Red, Orange, Yellow, Green, Blue} P( ) = 1 Venn Diagram Red Yellow Green Orange Blue E F Perform repeated trials of the following experiment: Randomly select an individual from the population, and record its color. Event Any subset of  (including the empty set , and  itself). Outcome Probability Red 0.10 Orange 0.15 Yellow 0.20 Green 0.25 Blue 0.30 1.00 E = “Primary Color” = {Red, Yellow, Blue} P(E) = 0.60 F = “Hot Color” = {Red, Orange, Yellow} P(F) = 0.45 “Cold Color” = {Green, Blue} “Not F” = Complement F C = P(FC) = 1 – P(F) = 0.55 Intersection E ⋂ F = {Red, Yellow} “E and F” = P(E ⋂ F) = 0.3 Note: A = {Red, Green} ⋂ B = {Orange, Blue} =  P() = 0 A and B are disjoint, or mutually exclusive events Union E ⋃ F = “E or F“ = {Red, Orange, Yellow, Blue} All probs are > 0, and sum = 1.

Definitions (using basic Set Theory) An outcome is the result of an experiment on a population. Sample Space The set of all possible outcomes of an experiment. Sample Space The set of all possible outcomes of an experiment.  = {Red, Orange, Yellow, Green, Blue} P( ) = 1 Venn Diagram Red Yellow Green Orange Blue E F Perform repeated trials of the following experiment: Randomly select an individual from the population, and record its color. Event Any subset of  (including the empty set , and  itself). Outcome Probability Red 0.10 Orange 0.15 Yellow 0.20 Green 0.25 Blue 0.30 1.00 E = “Primary Color” = {Red, Yellow, Blue} P(E) = 0.60 F = “Hot Color” = {Red, Orange, Yellow} P(F) = 0.45 “Cold Color” = {Green, Blue} “Not F” = Complement F C = P(FC) = 1 – P(F) = 0.55 Intersection E ⋂ F = {Red, Yellow} “E and F” = P(E ⋂ F) = 0.3 Note: A = {Red, Green} ⋂ B = {Orange, Blue} =  P() = 0 A and B are disjoint, or mutually exclusive events Union E ⋃ F = “E or F“ = {Red, Orange, Yellow, Blue} All probs are > 0, and sum = 1. P(E ⋃ F) = 0.75

Definitions (using basic Set Theory) An outcome is the result of an experiment on a population. Sample Space The set of all possible outcomes of an experiment. Sample Space The set of all possible outcomes of an experiment.  = {Red, Orange, Yellow, Green, Blue} P( ) = 1 Venn Diagram Red Yellow Green Orange Blue E F Perform repeated trials of the following experiment: Randomly select an individual from the population, and record its color. Event Any subset of  (including the empty set , and  itself). Outcome Probability Red 0.10 Orange 0.15 Yellow 0.20 Green 0.25 Blue 0.30 1.00 E = “Primary Color” = {Red, Yellow, Blue} P(E) = 0.60 F = “Hot Color” = {Red, Orange, Yellow} P(F) = 0.45 “Cold Color” = {Green, Blue} “Not F” = Complement F C = P(FC) = 1 – P(F) = 0.55 Intersection E ⋂ F = {Red, Yellow} “E and F” = P(E ⋂ F) = 0.3 Note: A = {Red, Green} ⋂ B = {Orange, Blue} =  P() = 0 A and B are disjoint, or mutually exclusive events Union E ⋃ F = “E or F“ = {Red, Orange, Yellow, Blue} All probs are > 0, and sum = 1. P(E ⋃ F) = P(E ⋃ F) = 0.75

Definitions (using basic Set Theory) An outcome is the result of an experiment on a population. Sample Space The set of all possible outcomes of an experiment. Sample Space The set of all possible outcomes of an experiment.  = {Red, Orange, Yellow, Green, Blue} P( ) = 1 Venn Diagram Red Yellow Green Orange Blue E F Perform repeated trials of the following experiment: Randomly select an individual from the population, and record its color. Event Any subset of  (including the empty set , and  itself). Outcome Probability Red 0.10 Orange 0.15 Yellow 0.20 Green 0.25 Blue 0.30 1.00 E = “Primary Color” = {Red, Yellow, Blue} P(E) = 0.60 F = “Hot Color” = {Red, Orange, Yellow} P(F) = 0.45 “Cold Color” = {Green, Blue} “Not F” = Complement F C = P(FC) = 1 – P(F) = 0.55 Intersection E ⋂ F = {Red, Yellow} “E and F” = P(E ⋂ F) = 0.3 Note: A = {Red, Green} ⋂ B = {Orange, Blue} =  P() = 0 A and B are disjoint, or mutually exclusive events Union E ⋃ F = “E or F“ = {Red, Orange, Yellow, Blue} All probs are > 0, and sum = 1. P(E ⋃ F) = P(E ⋃ F) = P(E) P(E ⋃ F) = 0.75

Definitions (using basic Set Theory) An outcome is the result of an experiment on a population. Sample Space The set of all possible outcomes of an experiment. Sample Space The set of all possible outcomes of an experiment.  = {Red, Orange, Yellow, Green, Blue} P( ) = 1 Venn Diagram Red Yellow Green Orange Blue E F Perform repeated trials of the following experiment: Randomly select an individual from the population, and record its color. Event Any subset of  (including the empty set , and  itself). Outcome Probability Red 0.10 Orange 0.15 Yellow 0.20 Green 0.25 Blue 0.30 1.00 E = “Primary Color” = {Red, Yellow, Blue} P(E) = 0.60 F = “Hot Color” = {Red, Orange, Yellow} P(F) = 0.45 “Cold Color” = {Green, Blue} “Not F” = Complement F C = P(FC) = 1 – P(F) = 0.55 Intersection E ⋂ F = {Red, Yellow} “E and F” = P(E ⋂ F) = 0.3 Note: A = {Red, Green} ⋂ B = {Orange, Blue} =  P() = 0 A and B are disjoint, or mutually exclusive events Union E ⋃ F = “E or F“ = {Red, Orange, Yellow, Blue} All probs are > 0, and sum = 1. P(E ⋃ F) = P(E) P(E ⋃ F) = P(E) + P(F) P(E ⋃ F) = 0.75

Definitions (using basic Set Theory) An outcome is the result of an experiment on a population. Sample Space The set of all possible outcomes of an experiment.  = {Red, Orange, Yellow, Green, Blue} P( ) = 1 Venn Diagram Red Yellow Green Orange Blue E F Perform repeated trials of the following experiment: Randomly select an individual from the population, and record its color. Event Any subset of  (including the empty set , and  itself). Outcome Probability Red 0.10 Orange 0.15 Yellow 0.20 Green 0.25 Blue 0.30 1.00 E = “Primary Color” = {Red, Yellow, Blue} P(E) = 0.60 F = “Hot Color” = {Red, Orange, Yellow} P(F) = 0.45 “Cold Color” = {Green, Blue} “Not F” = Complement F C = P(FC) = 1 – P(F) = 0.55 Intersection E ⋂ F = {Red, Yellow} “E and F” = P(E ⋂ F) = 0.3 Note: A = {Red, Green} ⋂ B = {Orange, Blue} =  P() = 0 A and B are disjoint, or mutually exclusive events Union E ⋃ F = “E or F“ = {Red, Orange, Yellow, Blue} All probs are > 0, and sum = 1. P(E ⋃ F) = P(E) + P(F) – P(E ⋂ F) P(E ⋃ F) = P(E) + P(F) P(E ⋃ F) = 0.75

Definitions (using basic Set Theory) An outcome is the result of an experiment on a population. Sample Space The set of all possible outcomes of an experiment.  = {Red, Orange, Yellow, Green, Blue} P( ) = 1 Venn Diagram Red Yellow Green Orange Blue E F Perform repeated trials of the following experiment: Randomly select an individual from the population, and record its color. Event Any subset of  (including the empty set , and  itself). Outcome Probability Red 0.10 Orange 0.15 Yellow 0.20 Green 0.25 Blue 0.30 1.00 E = “Primary Color” = {Red, Yellow, Blue} P(E) = 0.60 F = “Hot Color” = {Red, Orange, Yellow} P(F) = 0.45 “Cold Color” = {Green, Blue} “Not F” = Complement F C = P(FC) = 1 – P(F) = 0.55 Intersection E ⋂ F = {Red, Yellow} “E and F” = P(E ⋂ F) = 0.3 Note: A = {Red, Green} ⋂ B = {Orange, Blue} =  P() = 0 A and B are disjoint, or mutually exclusive events Union E ⋃ F = “E or F“ = {Red, Orange, Yellow, Blue} All probs are > 0, and sum = 1. P(E ⋃ F) = P(E) + P(F) – P(E ⋂ F) = – 0.30 P(E ⋃ F) = P(E) + P(F) – P(E ⋂ F) P(E ⋃ F) = 0.75

Definitions (using basic Set Theory) An outcome is the result of an experiment on a population. Sample Space The set of all possible outcomes of an experiment.  = {Red, Orange, Yellow, Green, Blue} P( ) = 1 Venn Diagram Red Yellow Green Orange Blue E F Perform repeated trials of the following experiment: Randomly select an individual from the population, and record its color. Event Any subset of  (including the empty set , and  itself). Outcome Probability Red 0.10 Orange 0.15 Yellow 0.20 Green 0.25 Blue 0.30 1.00 E = “Primary Color” = {Red, Yellow, Blue} P(E) = 0.60 F = “Hot Color” = {Red, Orange, Yellow} P(F) = 0.45 “Cold Color” = {Green, Blue} “Not F” = Complement F C = P(FC) = 1 – P(F) = 0.55 Intersection E ⋂ F = {Red, Yellow} “E and F” = P(E ⋂ F) = 0.3 Note: A = {Red, Green} ⋂ B = {Orange, Blue} =  P() = 0 A and B are disjoint, or mutually exclusive events Union E ⋃ F = “E or F“ = {Red, Orange, Yellow, Blue} All probs are > 0, and sum = 1. P(E ⋃ F) = P(E) + P(F) – P(E ⋂ F) = – 0.30 P(E ⋃ F) = 0.75

In general, for any two events A and B, there are 4 disjoint intersections:
If events E and F are disjoint, A B A ⋂ B A ⋂ Bc A\B Ac ⋂ B B\A “A only” “B only” Ac ⋂ Bc “Neither A nor B” “A and B” then P(E ⋂ F) = 0. So… P(E ⋃ F) = P(E) + P(F) F E Probability Table B BC A P(A ⋂ B) P(A⋂ BC) P(A) AC P(AC ⋂ B) P(AC ⋂ BC) P(AC) P(B) P(BC) 1.0 P(E ⋃ F) = P(E) + P(F) – P(E ⋂ F)

Definitions (using basic Set Theory) An outcome is the result of an experiment on a population. Sample Space The set of all possible outcomes of an experiment.  = {Red, Orange, Yellow, Green, Blue} P( ) = 1 Venn Diagram Red Yellow Green Orange Blue E F Perform repeated trials of the following experiment: Randomly select an individual from the population, and record its color. Event Any subset of  (including the empty set , and  itself). Outcome Probability Red 0.10 Orange 0.15 Yellow 0.20 Green 0.25 Blue 0.30 1.00 E = “Primary Color” = {Red, Yellow, Blue} P(E) = 0.60 F = “Hot Color” = {Red, Orange, Yellow} P(F) = 0.45 Probability Table E EC F P(E ⋂ F) P(EC ⋂ F) P(F) FC P(E ⋂ FC) P(EC ⋂ FC) P(FC) P(E) P(EC) 1.0 All probs are > 0, and sum = 1.

Definitions (using basic Set Theory) An outcome is the result of an experiment on a population. Sample Space The set of all possible outcomes of an experiment.  = {Red, Orange, Yellow, Green, Blue} P( ) = 1 Venn Diagram Red Yellow Green Orange Blue E F Perform repeated trials of the following experiment: Randomly select an individual from the population, and record its color. Event Any subset of  (including the empty set , and  itself). Outcome Probability Red 0.10 Orange 0.15 Yellow 0.20 Green 0.25 Blue 0.30 1.00 E = “Primary Color” = {Red, Yellow, Blue} P(E) = 0.60 F = “Hot Color” = {Red, Orange, Yellow} P(F) = 0.45 Probability Table E EC F 0.30 0.15 0.45 FC 0.25 0.55 0.60 0.40 1.0 All probs are > 0, and sum = 1.

~ Summary of Basic Properties of Probability ~ Population Hypothesis  Experiment  Sample space  of possible outcomes  Event E ⊆   Probability P(E) = ? Def: P(E) = “limiting value” of as experiment is repeated indefinitely. P(E) =  P(outcomes) = always a number between 0 and 1. (That is, 0 ≤ P(E) ≤ 1.) If AND ONLY IF all outcomes in  are equally likely, then P(E) = If E and F are any two events, then so are the following:  EC E E F Event Description Notation Terminology Probab Not E “E does not occur.” complement of E 1 – P(E) E and F “Both E and F occur simultaneously.” E ⋂ F intersection of E and F - E or F “Either E occurs, or F occurs (or both).” E ⋃ F union of E and F P(E) + P(F) – P(E ⋂ F) “If E occurs, then F occurs.” E ⊆ F E is a subset of F P(E ⋂ F) F E

Definitions (using basic Set Theory) An outcome is the result of an experiment on a population. Sample Space The set of all possible outcomes of an experiment.  = {Red, Orange, Yellow, Green, Blue} P( ) = 1 Venn Diagram 0.15 0.30 0.25 E F Perform repeated trials of the following experiment: Randomly select an individual from the population, and record its color. Event Any subset of  (including the empty set , and  itself). Outcome Probability Red 0.10 Orange 0.15 Yellow 0.20 Green 0.25 Blue 0.30 1.00 E = “Primary Color” = {Red, Yellow, Blue} P(E) = 0.60 F = “Hot Color” = {Red, Orange, Yellow} P(F) = 0.45 Probability Table E EC F 0.30 0.15 0.45 FC 0.25 0.55 0.60 0.40 1.0 All probs are > 0, and sum = 1.

What percentage receives T1 only? (w/o T2)
Example: Two treatments exist for a certain disease, which can either be taken separately or in combination. Suppose: 70% of patient population receives T1 50% of patient population receives T2 30% of patient population receives both T1 and T2 T T2 T1 ⋂ T2c T1c ⋂ T2 (w/ or w/o T2) T1 ⋂ T2 (w/ or w/o T1) T1c ⋂ T2c P(T1) = 0.7 0.7 P(T2) = 0.5 0.5 P(T1 ⋂ T2) = 0.3 0.3 0.3 Row marginal sums T1 T1c T2 T2c 1.0 What percentage receives T1 only? (w/o T2) P(T1 ⋂ T2c) = 0.7 – 0.3 = 0.4…. i.e., 40% 0.4 0.4 0.5 What percentage receives T2 only? (w/o T1) P(T1c ⋂ T2) = 0.5 – 0.3 = 0.2…. i.e., 20% 0.2 0.2 0.3 Column marginal sums What percentage receives neither T1 nor T2? P(T1c ⋂ T2c) = 1 – ( ) = 0.1…. i.e., 10% 0.1 0.1

In general, for three events A, B, and C…
A ⋂ C B ⋂ C C

A B C In general, for three events A, B, and C… A ⋂ Bc ⋂ Cc A ⋂ B ⋂ Cc
Ac ⋂ B ⋂ Cc “A only” “B only” A ⋂ B ⋂ C A ⋂ Bc ⋂ C Ac ⋂ B ⋂ C Ac ⋂ Bc ⋂ Cc Ac ⋂ Bc ⋂ C “Neither A nor B nor C” “C only” C

 A B C In general, for three events A, B, and C…
A ⋂ Bc ⋂ Cc A ⋂ B ⋂ Cc Ac ⋂ B ⋂ Cc “A only” “B only”  A ⋂ B ⋂ C A ⋂ Bc ⋂ C Ac ⋂ B ⋂ C Ac ⋂ Bc ⋂ Cc Ac ⋂ Bc ⋂ C “Neither A nor B nor C” “C only” “All three events occur” C

   A B C In general, for three events A, B, and C…
A ⋂ Bc ⋂ Cc A ⋂ B ⋂ Cc Ac ⋂ B ⋂ Cc “A only” “B only” A ⋂ B ⋂ C   A ⋂ Bc ⋂ C Ac ⋂ B ⋂ C Ac ⋂ Bc ⋂ Cc Ac ⋂ Bc ⋂ C “Neither A nor B nor C” “C only” “Exactly two events occur” C

    A B C In general, for three events A, B, and C…
A ⋂ Bc ⋂ Cc A ⋂ B ⋂ Cc Ac ⋂ B ⋂ Cc “A only” “B only”  A ⋂ B ⋂ C   A ⋂ Bc ⋂ C Ac ⋂ B ⋂ C Ac ⋂ Bc ⋂ Cc Ac ⋂ Bc ⋂ C “Neither A nor B nor C” “C only” “At least two events occur” C

   A B C In general, for three events A, B, and C…
A ⋂ Bc ⋂ Cc A ⋂ B ⋂ Cc Ac ⋂ B ⋂ Cc “A only” “B only” A ⋂ B ⋂ C A ⋂ Bc ⋂ C Ac ⋂ B ⋂ C  Ac ⋂ Bc ⋂ Cc Ac ⋂ Bc ⋂ C “Neither A nor B nor C” “C only” “Exactly one event occurs” C

       A B C In general, for three events A, B, and C…
A ⋂ Bc ⋂ Cc A ⋂ B ⋂ Cc Ac ⋂ B ⋂ Cc “A only” “B only”  A ⋂ B ⋂ C   A ⋂ Bc ⋂ C Ac ⋂ B ⋂ C  Ac ⋂ Bc ⋂ Cc Ac ⋂ Bc ⋂ C “Neither A nor B nor C” “C only” “At least one event occurs” C

       A B C In general, for three events A, B, and C… 1 ‒ P( )
A ⋂ Bc ⋂ Cc A ⋂ B ⋂ Cc Ac ⋂ B ⋂ Cc “A only” “B only”  A ⋂ B ⋂ C   A ⋂ Bc ⋂ C Ac ⋂ B ⋂ C  1 ‒ P( ) Ac ⋂ Bc ⋂ Cc Ac ⋂ Bc ⋂ C 1 ‒ P( ) Neither A nor B nor C “C only” P(At least one event occurs) =  P(outcomes) “At least one event occurs” C

A ⋂ C B ⋂ C

“Inclusion-Exclusion Principle”
In general, for three events A, B, and C… A C B A ⋂ B A ⋂ C B ⋂ C “Inclusion-Exclusion Principle”

“Inclusion-Exclusion Principle”
In general, for three events A, B, and C… In general, for three “pairwise disjoint” events A, B, and C… A C B “Inclusion-Exclusion Principle”

“Countable Additivity” “Inclusion-Exclusion Principle”
In general, for “countably many” pairwise disjoint events A1, A2, A3 ,… In general, for three events A, B, and C… “Countable Additivity” “Inclusion-Exclusion Principle”

is known as the “power set” of Ω.
Definitions POPULATION (using basic Set Theory) An outcome is the result of an experiment on a population. Sample Space The set of all possible outcomes of an experiment.  = {Red, Orange, Yellow, Green, Blue} #( ) = 5 Venn Diagram Red Orange Yellow Consider the following experiment: Randomly select an individual from the population, and record its color. Green Blue Event Any subset of  (including the empty set , and  itself). 1 5 What are all the events of the sample space? That is, all the subsets of the sample space? 10  10 5 1 is known as the “power set” of Ω. #( ) = ?

~ Three Ingredients of a “Probability Model” ~
Sample space (of an experiment) = set of all possible outcomes Sigma-field of events = Set of all subsets (i.e., “power set”) of Ω Claim: If #(Ω) = n, then #( ) = 2n. (Note: Subtle mathematical difficulties arise if infinite.) Proof 1: Each outcome ωi has two possibilities for inclusion in a subset (i.e., “in” or “out”), for i = 1,2,3,…,n. Thus, there are 2  2  2  …  2 = 2n such subsets. QED Ex: Ω ={R, O, Y, G, B} Note: Infinitely many outcomes are possible as well, but assume n finite for simplicity.

Sample space (of an experiment) = set of all possible outcomes Sigma-field of events = Set of all subsets (i.e., “power set”) of Ω Claim: If #(Ω) = n, then #( ) = 2n. (Note: Subtle mathematical difficulties arise if infinite.) Proof 1: Each outcome ωi has two possibilities for inclusion in a subset (i.e., “in” or “out”), for i = 1,2,3,…,n. Thus, there are 2  2  2  …  2 = 2n such subsets. QED Proof 2: The power set consists of all subsets of size k = 0,1,2,…,n. That is,  There is = 1 subset of size k = 0, namely, the empty set .  There are = n subsets of size k = 1: the singletons {ω1}, {ω2}, …, {ωn}. … etc…  There is = 1 subset of size k = n, namely Ω itself. Therefore, the total number of subsets is via the Binomial Theorem for (x + y)n, with x = 1, y = 1. QED Ex: Ω ={R, O, Y, G, B} Note: Infinitely many outcomes are possible as well, but assume n finite for simplicity.

Sample space (of an experiment) = set of all possible outcomes Sigma-field of events = Set of all subsets (i.e., “power set”) of Ω Ex: Ω ={R, O, Y, G, B} Note: Infinitely many outcomes are possible as well, but assume n finite for simplicity. Probability measure P, a mapping from to the interval [0, 1], with that satisfies countable additivity, i.e., (provided convergence holds). Def: The “ordered triple” (, , P) is called a “probability space.”

Definitions POPULATION

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