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via python. It’s the   -number of accidents v hour of day  -number of accidents v time period  - relative distribution graph   With these graphs are there any statistical a

via python. It’s the   -number of accidents v hour of day  -number of accidents v time period  - relative distribution graph   With these graphs are there any statistical a

A First Course in Probability (10th Edition)
A First Course in Probability (10th Edition)
10th Edition
ISBN:9780134753119
Author:Sheldon Ross
Publisher:Sheldon Ross
Chapter1: Combinatorial Analysis
Section: Chapter Questions
Problem 1.1P: a. How many different 7-place license plates are possible if the first 2 places are for letters and...
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I am planning to investigate the effect of time of day on number of road accidents. i have linked the graphs I’ve currently produced via python. It’s the  

-number of accidents v hour of day 

-number of accidents v time period 

- relative distribution graph  

With these graphs are there any statistical and numerical results (e.g correlation, pvalues etc ) that I can calculate? 

=[9]: 30- 25 8 Percentage of Accidents 8 5 0 Relative Distribution of Accidents across Time Segments Morning (Peak) 7AM - 10AM ah. Late Morning Afternoon (Off-peak) school&work Evening (Off-peak) 10AM - 12PM 12PM - 2:59 PM 3PM - 7PM Time Segments Late Night 7PM - 11:59 PM 12AM - 4:59 AM
Transcribed Image Text:=[9]: 30- 25 8 Percentage of Accidents 8 5 0 Relative Distribution of Accidents across Time Segments Morning (Peak) 7AM - 10AM ah. Late Morning Afternoon (Off-peak) school&work Evening (Off-peak) 10AM - 12PM 12PM - 2:59 PM 3PM - 7PM Time Segments Late Night 7PM - 11:59 PM 12AM - 4:59 AM
]: Accident Count 160000 140000 120000 100000 80000 60000 40000 20000 0 Accident Count by Hour of the Day 10.0 Hour of the Day 14.0 15.0 16.0 17.0 18.0 19.0 20.0 21.0 22.0 23.0
Transcribed Image Text:]: Accident Count 160000 140000 120000 100000 80000 60000 40000 20000 0 Accident Count by Hour of the Day 10.0 Hour of the Day 14.0 15.0 16.0 17.0 18.0 19.0 20.0 21.0 22.0 23.0
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i linked an image from chatgpt that explains the steps i need to go through to determine if the number of accidents in the "High Traffic" time segment is significantly higher than any other time segment can you read it and let me know if it is the correct steps to take

To use permutation testing to determine if the number of accidents in the "High Traffic" time segment is significantly higher than any other time segment, you can follow these steps: 1. Define the null hypothesis: The null hypothesis states that there is no difference in the number of accidents between the "High Traffic" time segment and any other time segment. 2. Calculate the observed test statistic: Calculate the number of accidents in the "High Traffic" time segment. This will be your observed test statistic. 3. Pool the accident counts: Combine the accident counts from all time segments except the "High Traffic" segment into a single pool. 4. Perform permutations: Randomly shuffle the accident counts within the pooled data, maintaining the original counts for the "High Traffic" segment. This random reshuffling breaks any association between the time segments and the accident counts. 5. Calculate the test statistic for each permutation: For each permutation, calculate a test statistic that represents the maximum difference in the number of accidents between the "High Traffic" segment and any other time segment. This could be the absolute difference, the ratio, or any other appropriate measure. 6. Repeat steps 4 and 5 multiple times: Repeat the shuffling and test statistic calculation process a sufficient number of times (e.g., 1000) to obtain a distribution of test statistics under the null hypothesis. 7. Compare the observed test statistic: Compare the observed test statistic with the distribution of test statistics obtained from the permutations. Calculate the proportion of permuted test statistics that are equal to or greater than the observed test statistic. This will give you the p-value. 8. Interpret the results: If the p-value is small (below a predetermined significance level, e.g., 0.05), it suggests that the observed number of accidents in the "High Traffic" segment is significantly higher than what would be expected by chance under the null hypothesis. Conversely, if the p-value is large, there is no strong evidence to suggest a significant difference between the "High Traffic" segment and other time segments. By performing permutation testing, you can assess the statistical significance of the observed difference in the number of accidents and determine if the "High Traffic" time segment has a significantly higher accident rate compared to other time segments.
Transcribed Image Text:To use permutation testing to determine if the number of accidents in the "High Traffic" time segment is significantly higher than any other time segment, you can follow these steps: 1. Define the null hypothesis: The null hypothesis states that there is no difference in the number of accidents between the "High Traffic" time segment and any other time segment. 2. Calculate the observed test statistic: Calculate the number of accidents in the "High Traffic" time segment. This will be your observed test statistic. 3. Pool the accident counts: Combine the accident counts from all time segments except the "High Traffic" segment into a single pool. 4. Perform permutations: Randomly shuffle the accident counts within the pooled data, maintaining the original counts for the "High Traffic" segment. This random reshuffling breaks any association between the time segments and the accident counts. 5. Calculate the test statistic for each permutation: For each permutation, calculate a test statistic that represents the maximum difference in the number of accidents between the "High Traffic" segment and any other time segment. This could be the absolute difference, the ratio, or any other appropriate measure. 6. Repeat steps 4 and 5 multiple times: Repeat the shuffling and test statistic calculation process a sufficient number of times (e.g., 1000) to obtain a distribution of test statistics under the null hypothesis. 7. Compare the observed test statistic: Compare the observed test statistic with the distribution of test statistics obtained from the permutations. Calculate the proportion of permuted test statistics that are equal to or greater than the observed test statistic. This will give you the p-value. 8. Interpret the results: If the p-value is small (below a predetermined significance level, e.g., 0.05), it suggests that the observed number of accidents in the "High Traffic" segment is significantly higher than what would be expected by chance under the null hypothesis. Conversely, if the p-value is large, there is no strong evidence to suggest a significant difference between the "High Traffic" segment and other time segments. By performing permutation testing, you can assess the statistical significance of the observed difference in the number of accidents and determine if the "High Traffic" time segment has a significantly higher accident rate compared to other time segments.
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