Question

In: Math

Each observation in a random sample of 106 bicycle accidents resulting in death was classified according...

Each observation in a random sample of 106 bicycle accidents resulting in death was classified according to the day of the week on which the accident occurred. Data consistent with information are given in the following table. Based on these data, is it reasonable to conclude that the proportion of accidents is not the same for all days of the week? Use α = 0.05. (Round your answer to two decimal places.)

Day of Week Frequency
Sunday 17
Monday 13
Tuesday 13
Wednesday 15
Thursday 17
Friday 18
Saturday 13


χ2 =  

P-value interval

p < 0.001

0.001 ≤ p < 0.01    

0.01 ≤ p < 0.05

0.05 ≤ p < 0.10

p ≥ 0.10


The proportion of accidents is  ---Select--- the same, not the same for all days.

Solutions

Expert Solution

We are assuming that the proportion of accidents is same on all days. Out of 106 accidents all days there will be (106/7)

Same number of accidents. Since there are 7 days we divide by seven.

Day of Week Frequency Expected (Ei) (106/7)
Sunday 17 15.143 0.228
Monday 13 15.143 0.303
Tuesday 13 15.143 0.303
Wednesday 15 15.143 0.001
Thursday 17 15.143 0.228
Friday 18 15.143 0.539
Saturday 13 15.143 0.303

Null Hypothesis: All days proportion of accidents is same.

Alternative: All days proportion of accidents is not same.

Test Statistic :

Test Statistic = 1.906

p - value =

df = n - 1 = 6

p - value =

= 0.928

P-value interval

p < 0.001

0.001 ≤ p < 0.01    

0.01 ≤ p < 0.05

0.05 ≤ p < 0.10

p ≥ 0.10

Since p-value > 0.05

We don't reject the null hypothesis

The proportion of accidents is  the same for all days.


Related Solutions

Each observation in a random sample of 102 bicycle accidents resulting in death was classified according...
Each observation in a random sample of 102 bicycle accidents resulting in death was classified according to the day of the week on which the accident occurred. Data consistent with information are given in the following table. Based on these data, is it reasonable to conclude that the proportion of accidents is not the same for all days of the week? Use α = 0.05. (Round your answer to two decimal places.) Day of Week Frequency Sunday 15 Monday 12...
Each observation in a random sample of 101 bicycle accidents resulting in death was classified according...
Each observation in a random sample of 101 bicycle accidents resulting in death was classified according to the day of the week on which the accident occurred. Data consistent with information are given in the following table. Based on these data, is it reasonable to conclude that the proportion of accidents is not the same for all days of the week? Use ? = .05. (Use 2 decimal places.) Day of Week Frequency Sunday 13 Monday 12 Tuesday 12 Wednesday...
A report classified fatal bicycle accidents according to the month in which the accident occurred, resulting...
A report classified fatal bicycle accidents according to the month in which the accident occurred, resulting in the accompanying table. Month Number of Accidents January 40 February 30 March 45 April 59 May 76 June 72 July 100 August 87 September 66 October 64 November 40 December 38 (a) Use the given data to test the null hypothesis H0: p1 = 1/12, p2= 1/12, .., p12= 1/12, where p1 is the proportion of fatal bicycle accidents that occur in January,...
A particular report classified 717 fatal bicycle accidents according to the month in which the accident...
A particular report classified 717 fatal bicycle accidents according to the month in which the accident occurred, resulting in the accompanying table. Month Number of Accidents January 37 February 31 March 44 April 59 May 79 June 75 July 97 August 83 September 63 October 66 November 43 December 40 (a) Use the given data to test the null hypothesis H0: p1 = 1/12, p2 = 1/12, ... , p12 = 1/12, where p1 is the proportion of fatal bicycle...
A particular report from 2004 classified 721 fatal bicycle accidents according to the month in which...
A particular report from 2004 classified 721 fatal bicycle accidents according to the month in which the accident occurred, resulting in the accompanying table. Month Number of Month Accidents January 37 February 33 March 44 April 60 May 78 June 73 July 97 August 85 September 63 October 67 November 43 December 41 (a) Use the given data to test the null hypothesis H0: ?1 = 1/12, ?2 = 1/12, . . . , ?12 = 1/12, where ?1 is...
A particular report included the following table classifying 716 fatal bicycle accidents according to time of...
A particular report included the following table classifying 716 fatal bicycle accidents according to time of day the accident occurred. Time of Day Number of Accidents Midnight to 3 a.m. 36 3 a.m. to 6 a.m. 28 6 a.m. to 9 a.m. 65 9 a.m. to Noon 78 Noon to 3 p.m. 99 3 p.m. to 6 p.m. 128 6 p.m. to 9 p.m. 166 9 p.m. to Midnight 116 (a) Assume it is reasonable to regard the 716 bicycle...
Last year's records of auto accidents occurring on a given section of highway were classified according...
Last year's records of auto accidents occurring on a given section of highway were classified according to whether the resulting damage was $1,000 or more and to whether a physical injury resulted from the accident. The data follows.      Under $1,000     $1,000 or More Number of Accidents 39 40 Number Involving Injuries     10 23 (a) Estimate the true proportion of accidents involving injuries when the damage was $1,000 or more for similar sections of highway. (Round your answer to three...
A single observation of a random variable (that is, a sample of size n = 1)...
A single observation of a random variable (that is, a sample of size n = 1) having a geometric distribution is used to test the null hypothesis θ = θ0 against the alternative hypothesis θ = θ1 for θ1 < θ0. The null hypothesis is rejected if the observed value of the random variable is greater than or equal to some positive integer k. Find expressions for the probabilities of type I and type II errors.
X is a random variable following Poisson distribution. X1 is an observation (random sample point) of...
X is a random variable following Poisson distribution. X1 is an observation (random sample point) of X. (1.1) Please find probability distribution of X and X1. Make sure to define related parameter properly. (1.2) Please give the probability distribution of a random sample with sample size of n that consists of X1, X2, ..., Xn as its observations. (1.3) Please give an approximate distribution of the sample mean in question 1.2(say, called Y) when sample size is 100 with detailed...
For a random sample of 50 measurements of the breaking strength of cotton threads resulting in...
For a random sample of 50 measurements of the breaking strength of cotton threads resulting in a mean of 210 grams and standard deviation of 18 grams. A. Calculate an 80% confidence interval for the true mean breaking strength of cotton threads. B. Is your interval statistically valid? Explain. C. Without recalculating, would a 90% confidence result in a wider or narrower interval? Explain. D. A co-worker offers the following partial interpretation of your interval: We are confident that 80%...
ADVERTISEMENT
ADVERTISEMENT
ADVERTISEMENT