Activity 3 Data

Section 1

2 3 4 5 6 7 8 9 10 11 12 W1 L1 P1 W L Rolls
2 4 2 3 7 9 5 1 0 2 1 11 7 18 15 20 96
2 2 4 5 4 5 9 2 0 2 1 7 5 24 17 19 105
0 2 0 5 4 11 5 4 2 2 1 13 3 20 20 16 119
1 0 3 6 9 5 5 1 1 1 4 6 5 25 15 21 125
0 1 2 3 4 2 8 4 4 5 3 7 4 25 18 18 121

Section 2

2 3 4 5 6 7 8 9 10 11 12 W1 L1 P1 W L Rolls
3 2 1 3 5 2 7 3 6 3 1 5 6 25 12 24 141
0 3 2 3 7 5 4 3 3 4 2 9 5 22 15 21 129
0 2 1 3 7 8 6 5 1 2 1 10 3 23 21 15 107
0 1 2 2 6 7 7 5 2 4 0 11 1 24 22 14 100
1 1 6 2 4 11 4 3 1 2 1 13 3 20 20 16 95
1 2 1 4 5 8 4 4 3 3 1 11 4 21 19 17 114
0 1 1 9 3 3 8 4 4 2 1 5 2 29 17 19 84
3 2 5 2 4 8 7 1 1 3 0 11 5 20 20 16 113
1 5 4 6 4 4 6 3 2 1 0 5 6 25 17 19 120
1 5 2 4 4 10 4 2 0 3 1 13 7 16 21 15 94
0 2 2 5 4 7 7 5 2 1 1 8 3 25 16 20 144

Totals

Sect 2 3 4 5 6 7 8 9 10 11 12 W1 L1 P1 W L Rolls
1 5 9 11 22 28 32 32 12 7 12 10 44 24 112 85 94 566
2 10 26 27 43 53 73 64 38 25 28 9 101 45 250 200 196 1,241
Total 15 35 38 65 81 105 96 50 32 40 19 145 69 362 285 290 1,807

Hypothesis Testing

Although hypothesis testing isn't covered until later, here are some things we'll be looking at later. Don't worry too much about what they mean for right now, but you may want to come back and look at them later.

Chi-Square Goodness of Fit Test

Probability theory tells us that the chance of getting a sum of 2 is 1/36, the chance of getting a sum of 3 is 2/36, and so on. The sum of the dice should have a nice triangular pattern if the theoretical values hold true. The expected frequencies are found by multiplying the total number of games by the chance of getting a 2, 3, 4, ..., or 12.

Sum 2 3 4 5 6 7 8 9 10 11 12 Total
Observed 15 35 38 65 81 105 96 50 32 40 19 576
Expected 16 32 48 64 80 96 80 64 48 32 16 576

Chi-sq = 17.45729167, df = 10, p-value = 0.064838

The p-value represents the probability of getting the results we actually got if the sum of the dice have the distribution they're supposed to have. A p-value of less than 0.05 would cause us to question whether or not the dice were behaving as they were expected to behave. This could happen if the dice weren't properly rolled or if some team made up their results.

Test About One Proportion

Another claim is that the probability of winning a game of craps is 244/495 and the probability of losing a game of craps is 251/495. We'll test the claim about the probabilty of winning a game of craps by comparing our results to the 244/495 and seeing whether or not our results are close enough that any differences could be do to chance alone or whether something unusual is going on (maybe our class is very lucky ... or unlucky ... at shooting craps. 244/495 is approximately 49.29% of the time.

Test of H0: p = 244/495 vs H1: p ≠ 244/495

x n sample p 95% C.I. Test
statistic
p-value
lower upper
285 575 0.495652 0.454786 0.536519 0.131 0.896093

The p-value is the chance of getting the results we obtained if the true chance of winning a game is 244/495 = 49.2323%. If the p-value is less than 0.05 (5%), then we consider our results to be too unusual to happen by chance alone decide that something must be going on.

Our winning rate of 49.57% appears to be close enough to the expected 49.29% to say that our results could have happened by chance alone. There doesn't appear to be anything unusual going on here.

Of course, if we did this again, we might get different results.

Data Dictionary

2-12
The number of times that a 2, 3, 4, ..., 12 was the initial roll for the game of craps
W1, L1, P1
The number of times that the initial roll was a win (W1), loss (L1), or point (P1)
W, L
The number of times the overall result was a win (W) or loss (L)
Rolls
The total number of rolls necessary to play 36 games of craps