qertgeneration.blogg.se - Coin flip sim

#COIN FLIP SIM CODE#

So what value do you expect for this proportion? We think it’s safe to say that most people would answer 1/2. So the proportion in question for this sequence is 1/2. For this sequence there are two flips which immediately followed heads, the second and the third, of which one (the second) was heads. What value do you expect for this proportion? (If there are no flips which immediately follow a H, i.e. the outcome is either TTTT or TTTH, discard the sequence and try again with four more flips.)įor example, the sequence HHTT means the the first and second flips are heads and the third and fourth flips are tails. For the recorded sequence, compute the proportion of the flips which immediately follow a H that result in H. Flip a fair coin four times and record the results in order.

7 Common Distributions of Continuous Random Variablesġ.6 Approximating probabilities - a brief introduction to simulation.

6.5 Comparison of Distributions of Counts.

6 Common Distributions of Discrete Random Variables.

5.6.5 Independent, uncorrelated, and something in between.5.6.2 Linearity of conditional expected value.5.6.1 Conditional expected value as a random variable.5.5.3 Variance of linear combinations of random variables.5.5 Expected values of linear combinations of random variables.5.2 “Law of the unconscious statistician” (LOTUS).4.8.2 Continuous random variables: Conditional probability density functions.4.8.1 Discrete random variables: Conditional probability mass functions.4.7.2 Joint probability density fuctions.4.6.2 Nonlinear transformations of random variables.4.6 Distributions of transformations of random variables.4.5.1 One ring spinner to rule them all?.

4.3 Continuous random variables: Probability density functions.

4.2 Discrete random variables: Probability mass functions.

4.1 Do not confuse a random variable with its distribution.

3.4 Conditional versus unconditional probability.

3 Rules of Probability and Conditional Probability.

2.14 A more interesting example: Matching problem.

2.13.2 Conditional distributions of continuous random variables.

2.13.1 Conditional distributions of discrete random variables.

2.11.2 Joint distributions of two continuous random variables.

2.11.1 Joint distributions of two discrete random variables.

2.10.2 Normal distributions and the empirical rule.

2.9.3 Averages of indicator random variables.

2.8.2 Simulating from a marginal distribution.

2.7.4 Conditioning is “slicing and renormalizing”.

2.7.2 Joint, conditional, and marginal probabilities.

2.7.1 Simulating conditional probabilities.

2.6.5 Beware a false sense of precision.

2.6.4 Approximating multiple probabilities.

2.6.2 Approximating probabilities: Simulation margin of error.

2.6.1 A few Symbulate commands for summarizing simulation output.

2.5.3 Computer simulation: Meeting problem.

2.5.2 Computer simulation: Dice rolling.

2.5.1 Tactile simulation: Boxes and spinners.

2.4.2 Some probability measures in the meeting problem.

2.4.1 Some probability measures for a four-sided die.

2 The Language of Probability and Simulation.

1.7 Why study coins, dice, cards, and spinners?.

1.6 Approximating probabilities - a brief introduction to simulation.

1.4 Proportional reasoning and tables of counts.

#COIN FLIP SIM CODE#

We’ll put our coin flipping code in here. Get an ||input:on button A pressed|| block from the ||input:Input|| drawer in the toolbox. We’ll use icon images to represent a heads or tails result. Let’s create a coin flipping program to simulate a real coin toss.