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Data from 14 cities were combined for a 20-year period, and the total 280 city-years included a total of 87 homicides. After finding the mean number of homicides per city-year, find the probability that a randomly selected city-year has the following numbers of homicides, then compare the actual results to those expected by using the Poisson probabilities: Homicides each city-year a. 0 b. 1 c. 2 d. 3 e. 4 Actual results 205 64 10 0 a. P(0) = (Round to four decimal places as needed.) b. P(1)= (Round to four decimal places as needed.) c. P(2)= (Round to four decimal places as needed.) d. P(3)= (Round to four decimal places as needed.) e. P(4)= (Round to four decimal places as needed.)

Data from 14 cities were combined for a 20-year period, and the total 280 city-years included a total of 87 homicides. After finding the mean number of homicides per city-year, find the probability that a randomly selected city-year has the following numbers of homicides, then compare the actual results to those expected by using the Poisson probabilities: Homicides each city-year a. 0 b. 1 c. 2 d. 3 e. 4 Actual results 205 64 10 0 a. P(0) = (Round to four decimal places as needed.) b. P(1)= (Round to four decimal places as needed.) c. P(2)= (Round to four decimal places as needed.) d. P(3)= (Round to four decimal places as needed.) e. P(4)= (Round to four decimal places as needed.)

MATLAB: An Introduction with Applications
MATLAB: An Introduction with Applications
6th Edition
ISBN:9781119256830
Author:Amos Gilat
Publisher:Amos Gilat
Chapter1: Starting With Matlab
Section: Chapter Questions
Problem 1P
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### Analyzing Homicide Data Using Poisson Probabilities **Data Overview:** Data from 14 cities were combined for a 20-year period, resulting in a total of 280 city-years and 87 homicides. We aim to determine the probability of a randomly selected city-year experiencing different numbers of homicides using Poisson probabilities. Moreover, we will compare these probabilities with the actual number of homicides observed. #### Event Probabilities: **Number of Homicides in each city-year:** - **a. 0 Homicides** * **Actual results:** 205 cases - **b. 1 Homicide** * **Actual results:** 64 cases - **c. 2 Homicides** * **Actual results:** 10 cases - **d. 3 Homicides** * **Actual results:** 1 case - **e. 4 Homicides** * **Actual results:** 0 cases #### Poisson Probability Calculations: To determine these probabilities, use the mean number of homicides per city-year and apply the Poisson probability formula. 1. **P(0) =** [Round to four decimal places as needed] 2. **P(1) =** [Round to four decimal places as needed] 3. **P(2) =** [Round to four decimal places as needed] 4. **P(3) =** [Round to four decimal places as needed] 5. **P(4) =** [Round to four decimal places as needed] By computing these probabilities, we can juxtapose the expected frequency of occurrences against the actual recorded data. This comparison aids in understanding if the distribution of homicide data adheres to Poisson expectations, or if there are deviations that might suggest other influencing factors.
Transcribed Image Text:### Analyzing Homicide Data Using Poisson Probabilities **Data Overview:** Data from 14 cities were combined for a 20-year period, resulting in a total of 280 city-years and 87 homicides. We aim to determine the probability of a randomly selected city-year experiencing different numbers of homicides using Poisson probabilities. Moreover, we will compare these probabilities with the actual number of homicides observed. #### Event Probabilities: **Number of Homicides in each city-year:** - **a. 0 Homicides** * **Actual results:** 205 cases - **b. 1 Homicide** * **Actual results:** 64 cases - **c. 2 Homicides** * **Actual results:** 10 cases - **d. 3 Homicides** * **Actual results:** 1 case - **e. 4 Homicides** * **Actual results:** 0 cases #### Poisson Probability Calculations: To determine these probabilities, use the mean number of homicides per city-year and apply the Poisson probability formula. 1. **P(0) =** [Round to four decimal places as needed] 2. **P(1) =** [Round to four decimal places as needed] 3. **P(2) =** [Round to four decimal places as needed] 4. **P(3) =** [Round to four decimal places as needed] 5. **P(4) =** [Round to four decimal places as needed] By computing these probabilities, we can juxtapose the expected frequency of occurrences against the actual recorded data. This comparison aids in understanding if the distribution of homicide data adheres to Poisson expectations, or if there are deviations that might suggest other influencing factors.
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