Many track runners believe that they have a better chance of winning if they start in the inside lane that is closest to the field. For the data below, the lane closest to the field is Lane 1, the next lane is Lane 2, and so on until the outermost lane, Lane 6. The data lists the number of wins for track runners in the different starting positions. Calculate the chi- square test statistic x² to test the claim that the number of wins is uniformly distributed across the different starting positions. The results are based on 240 wins. Starting Position 1 4 5 6 2 3 Number of Wins 32 36 45 44 33 50 O 12.592 9.326 6.750 15.541

MATLAB: An Introduction with Applications
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Provide an appropriate response.
Many track runners believe that they have a
better chance of winning if they start in the
inside lane that is closest to the field. For the
data below, the lane closest to the field is Lane
1, the next lane is Lane 2, and so on until the
outermost lane, Lane 6. The data lists the
number of wins for track runners in the
different starting positions. Calculate the chi-
square test statistic x² to test the claim that the
number of wins is uniformly distributed across
the different starting positions. The results are
based on 240 wins.
Starting Position 1 2 3 4 5 6
Number of Wins 32 36 45 44 33 50
12.592
9.326
6.750
15.541
Transcribed Image Text:Provide an appropriate response. Many track runners believe that they have a better chance of winning if they start in the inside lane that is closest to the field. For the data below, the lane closest to the field is Lane 1, the next lane is Lane 2, and so on until the outermost lane, Lane 6. The data lists the number of wins for track runners in the different starting positions. Calculate the chi- square test statistic x² to test the claim that the number of wins is uniformly distributed across the different starting positions. The results are based on 240 wins. Starting Position 1 2 3 4 5 6 Number of Wins 32 36 45 44 33 50 12.592 9.326 6.750 15.541
Calculate x² from contingency table.
Use the sample data below to calculate the chi-
squared test statistic for testing whether car
color affects the likelihood of being in an
accident. Use a significance level of 0.01.
Red Blue White
Car has been in
accident
Car has not been
in accident
6.635
0.4287
0.050
0.579
O 9.210
28 33
23 22
36
30
Transcribed Image Text:Calculate x² from contingency table. Use the sample data below to calculate the chi- squared test statistic for testing whether car color affects the likelihood of being in an accident. Use a significance level of 0.01. Red Blue White Car has been in accident Car has not been in accident 6.635 0.4287 0.050 0.579 O 9.210 28 33 23 22 36 30
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