MGSC 291 EXAM 3 STUDY GUIDE 2026
COMPLETE QUESTIONS WITH
CORRECT DETAILED ANSWERS ||
100% GUARANTEED PASS
<RECENT VERSION>
1. Uncertainty quantification - ANSWER ✔ tools needed to be able to quantify
the uncertainty in our point estimates
2. goal with statistical analysis: not to eliminate uncertainty, but to REDUCE
and QUANTIFY it
3. Frequentist approach - ANSWER ✔ Repeatedly drawing samples of data
and counting the frequency with which an event happens
4. Frequency - ANSWER ✔ mean of the sample
5. Sampling variability - ANSWER ✔ Frequency values(different sample
means) are calculated, and varies from sample to sample
6. Sampling distribution of sample means - ANSWER ✔ The distribution of all
possible sample means(frequencies) of size n from the same population
,7. How is the frequentist approach different from bootstrapping? - ANSWER
✔ Different from bootstrapping because bootstrapping resamples the
original sample of data
8. SHAPE of the sampling distribution of a sample mean - ANSWER ✔
normal
(if we take large samples OR we sample from a normal distribution)
9. CENTER of the sampling distribution of a sample mean - ANSWER ✔
(greek mu symbol) --- our average or our mean
Parameter = mu ; (true mean of the population) the value is fixed but
unknown
10.percentile bootstrap confidence interval - ANSWER ✔
quantile(betaBoot$t,c(0.025,0.975))
95% percentile bootstrap confidence interval
11.bootstrap estimating the size of bias - ANSWER ✔ take the differences
between the bootstrap sample estimates from the original sample estimate
12.bootstrap(t) - original(t0)
ex:
size of bias> priceErrors<-priceBoot$t-priceBoot$t0
ex:
estimating bias! > mean(priceErrors)
, 13.bias-adjusted estimated - ANSWER ✔ ex: R
➢ priceBoot$t0 - mean(priceErrors)
14.original(t0) - estimated bias
15.bias-corrected confidence interval - ANSWER ✔ ex: R
➢ quantile(2*priceBoot$t0 - priceBoot$t,c(0.025,0.975))
16.2*original(t0) - bootstrap(t), in 95% CI
17.out of sample(OOS) - ANSWER ✔ data used to TEST the model's
PERFORMANCE
18.test data: new data we calculate the OOS performance on
19.Sample standard deviation - ANSWER ✔ Point estimate for the population
standard deviation
20.A population has a uniform distribution. A random sample of n = 250
observations is taken from this population. What is the approximate shape of
the sampling distribution of the sample mean? - ANSWER ✔ Normal
21.A population has a uniform distribution. A random sample of n = 2
observations is taken from this population. What is the approximate shape of
the sampling distribution of the sample mean? - ANSWER ✔ Uniform
COMPLETE QUESTIONS WITH
CORRECT DETAILED ANSWERS ||
100% GUARANTEED PASS
<RECENT VERSION>
1. Uncertainty quantification - ANSWER ✔ tools needed to be able to quantify
the uncertainty in our point estimates
2. goal with statistical analysis: not to eliminate uncertainty, but to REDUCE
and QUANTIFY it
3. Frequentist approach - ANSWER ✔ Repeatedly drawing samples of data
and counting the frequency with which an event happens
4. Frequency - ANSWER ✔ mean of the sample
5. Sampling variability - ANSWER ✔ Frequency values(different sample
means) are calculated, and varies from sample to sample
6. Sampling distribution of sample means - ANSWER ✔ The distribution of all
possible sample means(frequencies) of size n from the same population
,7. How is the frequentist approach different from bootstrapping? - ANSWER
✔ Different from bootstrapping because bootstrapping resamples the
original sample of data
8. SHAPE of the sampling distribution of a sample mean - ANSWER ✔
normal
(if we take large samples OR we sample from a normal distribution)
9. CENTER of the sampling distribution of a sample mean - ANSWER ✔
(greek mu symbol) --- our average or our mean
Parameter = mu ; (true mean of the population) the value is fixed but
unknown
10.percentile bootstrap confidence interval - ANSWER ✔
quantile(betaBoot$t,c(0.025,0.975))
95% percentile bootstrap confidence interval
11.bootstrap estimating the size of bias - ANSWER ✔ take the differences
between the bootstrap sample estimates from the original sample estimate
12.bootstrap(t) - original(t0)
ex:
size of bias> priceErrors<-priceBoot$t-priceBoot$t0
ex:
estimating bias! > mean(priceErrors)
, 13.bias-adjusted estimated - ANSWER ✔ ex: R
➢ priceBoot$t0 - mean(priceErrors)
14.original(t0) - estimated bias
15.bias-corrected confidence interval - ANSWER ✔ ex: R
➢ quantile(2*priceBoot$t0 - priceBoot$t,c(0.025,0.975))
16.2*original(t0) - bootstrap(t), in 95% CI
17.out of sample(OOS) - ANSWER ✔ data used to TEST the model's
PERFORMANCE
18.test data: new data we calculate the OOS performance on
19.Sample standard deviation - ANSWER ✔ Point estimate for the population
standard deviation
20.A population has a uniform distribution. A random sample of n = 250
observations is taken from this population. What is the approximate shape of
the sampling distribution of the sample mean? - ANSWER ✔ Normal
21.A population has a uniform distribution. A random sample of n = 2
observations is taken from this population. What is the approximate shape of
the sampling distribution of the sample mean? - ANSWER ✔ Uniform
