'Fair coin 100 times' presentation slideshows

Chapter 8

Chapter 8. Inference for Proportions. Inference: using results from a random sample to draw conclusions about a population. In this chapter, you will use your understanding of sampling distributions developed in Chapter 7.

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Probability Theory: Counting in Terms of Proportions

Great Theoretical Ideas In Computer Science. Probability Theory: Counting in Terms of Proportions. Lecture 16. CS 15-251. Team A is better than Team B: In any one game, the odds are 6:5 that A will beat B. I.e., A beats B with probability 6/11.

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The Chi-Square Test

534 views • 28 slides

Review

Review. Probability Axioms Non-negativity P(A) ≥ 0 Additivity P(A U B) =P(A)+ P(B), if A and B are disjoint. Normalization P( Ω )=1 Independence of two events A and B P(A ∩ B)=P(A)P(B) Two coin tosses A={first toss is a head}, B={second toss is a head} Disjoint vs independent

331 views • 14 slides

Review

Review. Probability Axioms Non-negativity P(A) ? 0 Additivity P(A U B) =P(A)+ P(B), if A and B are disjoint. Normalization P( ? )=1 Independence of two events A and B P(A ? B)=P(A)P(B) Two coin tosses A={first toss is a head}, B={second toss is a head} Disjoint vs independent

267 views • 14 slides

What Does Random Look Like ? math.uh/~jmorgan/hollyer

What Does Random Look Like ? http://www.math.uh.edu/~jmorgan/hollyer. Suppose you perform the following experiment: Flip a fair coin 100 times. What do you expect to see?

120 views • 4 slides