Dr. Jayanthi Sankaran
Well-Known Member
Hi David,
In the following question 209.1:
209.1 Nine (9) companies among a random sample of 60 companies defaulted. The companies were each in the same highly speculative credit rating category; statistically, they represent a random sample from the population of CCC-rated companies. The rating agency contends that the historical (population) default rate for this category is 10.0%, in contrast to the 15.0% default rate observed in the sample. Is there statistical evidence, with any high confidence, that the true default rate is different than 10.0%; i.e. if the null hypothesis is that the true default rate is 10.0%, can we reject the null?
a) No, the t-statistic is 0.39
b) No, the t-statistic is 1.08
c) Yes, the t-statistic is 1.74
d) Yes, the t-statistic is 23.53
The answer is 209.1 (b): No, the t-statistic is only 1.08. For a large sample, the distribution is normally approximated, such that at a 5.0% two-tailed significance, we reject if the abs(t-statistic) exceeds 1.96
The standard error = SQRT((15%*85%)/60) = 0.0460098; please note: if you used SQRT(10%*90%/60) for the standard error, that is not wrong, but also would not change the conclusion as the t-statistic is 1.29
The t-statistic = (15%-10%)/0.0460098 = 1.08. The two-sided p-value is 27.8%, but as the t statistic is well below 2.0, we cannot reject.
We don't really need the lookup table or a calculator: the t - statistic tells us that the observed sample mean is only 1.08 standard deviations (standard errors) away from the hypothesized population mean.
A two-tailed 90% confidence interval implies 1.64 standard errors, so this (72.8%) is much less confident than even 90%
FWIW: From the 'Cumulative Probabilities of the Standard Normal Distribution Table'
Z(1.08) = 0.8599
And so, 2*(1 - .8599) = 28.02%
Area under curve = 100 - 28.02 = 71.98% ~ 72.0%
A very trivial question - how do you get an area of 72.8%, instead?
My question is (1) how do you determine the p-value of 27.8%?
Thanks
Jayanthi
In the following question 209.1:
209.1 Nine (9) companies among a random sample of 60 companies defaulted. The companies were each in the same highly speculative credit rating category; statistically, they represent a random sample from the population of CCC-rated companies. The rating agency contends that the historical (population) default rate for this category is 10.0%, in contrast to the 15.0% default rate observed in the sample. Is there statistical evidence, with any high confidence, that the true default rate is different than 10.0%; i.e. if the null hypothesis is that the true default rate is 10.0%, can we reject the null?
a) No, the t-statistic is 0.39
b) No, the t-statistic is 1.08
c) Yes, the t-statistic is 1.74
d) Yes, the t-statistic is 23.53
The answer is 209.1 (b): No, the t-statistic is only 1.08. For a large sample, the distribution is normally approximated, such that at a 5.0% two-tailed significance, we reject if the abs(t-statistic) exceeds 1.96
The standard error = SQRT((15%*85%)/60) = 0.0460098; please note: if you used SQRT(10%*90%/60) for the standard error, that is not wrong, but also would not change the conclusion as the t-statistic is 1.29
The t-statistic = (15%-10%)/0.0460098 = 1.08. The two-sided p-value is 27.8%, but as the t statistic is well below 2.0, we cannot reject.
We don't really need the lookup table or a calculator: the t - statistic tells us that the observed sample mean is only 1.08 standard deviations (standard errors) away from the hypothesized population mean.
A two-tailed 90% confidence interval implies 1.64 standard errors, so this (72.8%) is much less confident than even 90%
FWIW: From the 'Cumulative Probabilities of the Standard Normal Distribution Table'
Z(1.08) = 0.8599
And so, 2*(1 - .8599) = 28.02%
Area under curve = 100 - 28.02 = 71.98% ~ 72.0%
A very trivial question - how do you get an area of 72.8%, instead?
My question is (1) how do you determine the p-value of 27.8%?
Thanks
Jayanthi