Exam Prep

The exam will have Stuff from test 1-3 + mean + SD Chapter 4-8

25 multiple - permutations, combinations, counting subsets, probabilities - probability (independant, mutually exclusive) - counting - permutations - combinations - counting subsets. one where you can have an empty subset, one where you cant have an empty subset. minus one if you have an empty subset - terminology. - independant or dependant - mututally exclusive or non-mutually exclusive - multiply or add - how to get dependant event probabilities - z-scores - standard deviation - mean, median and mode

5 extended response- counting, permutations, combinations, probability 1. expected value for a hypergeometric distribution 2. uniform continuous probability distribution. the one that’s the rectangle 3. normal distribution question. pay close attention to the variable 4. binomial distribution. know how to get n,p,q values 5. confidence intervals

all formulas are provided on the exam. z-score tables too.

NONE:

• NO BINOMIAL ERROR CORRECTION
• NO ONE VARIABLE OR 2 VARIABLE STATISTICS
• NO GEOMETRIC DISTRIBUTION
• NO PERCENTILES

Problem Solving

Used from chapter 4-7:

• Indirect Reasoning
• Cases
• Box Occupant Based Thinking

Combinatorics(Chapter 4)

• Counting
  • Types of counting are:
  • Fundamental Counting Principle
  • Rule of Sum
• Cases
  • Permutations
    • Permutations with Identical Items
  • Pascal Triangle
    • Pascal Routes

Combinations & Binomial Theorem(Chapter 5)

• Venn Diagrams
• Combinations
  • Problem Solving with Combinations
  • Combination Subset Formula
  • Combination Subset Formula Identical Items
• Combination Permutation Word Problem Flowchart
• Binomial Theorem ← simple

Probability(Chapter 6)

• Probability
• Events
  • Inclusive Exclusive Probabilities
• Odds
• Probability Using Counting techniques
• Dependent Events
  • Conditional Probability
• Independent Events

Probability Distributions(Chapter 7)

• Probability Distributions
• Comparing and Selecting Discrete Probability Distributions

Normal Distributions(Chapter 8)

• Continuous Probability Distributions

Terminology:

Random Variable: A discrete or continuous variable Discrete: Integers or values that are logically finite Continuous: Decimals or values that you MUST approximate Bernoulli Trial: Trials where the outcome is win or lose. binary states Mutually Exclusive: Sets do not overlap Non-Mutually Exclusive: Sets overlap

Probability Distributions

Uniform:

• P(X) = p
• E(X) = just the mean Binomial:
• P(X) = ${}_n{C}_x{p}^x{q}^{n-x}$
• E(X) = np Geometric:
• Use it to find:
  • Time for something to show up
  • Probability of showing up at specific time
• P(X) = q^x * p
• E(X) = q/p Hyper geometric:
• Use it when you have dependent variables
• P(X) = current case / all possible cases
• P(X) = $\frac{{}_a{C}_x{\ast }_{n-a}{C}_{r-x}}{{}_n{C}_r}$
• $E(X)=\frac{ra}{n}$