Worksheets경영 통계 이해하기
Total questions: 10
Worksheet time: 5mins
기초 통계에서 평균, 중앙값, 최빈값의 차이는 무엇인가요?
평균은 총합/개수, 중앙값은 정렬 후 중앙값, 최빈값은 가장 빈번한 값입니다.
평균은 정렬 후 첫 번째 값, 중앙값은 마지막 값, 최빈값은 두 번째 값입니다.
평균은 중앙값과 같고, 중앙값은 최빈값보다 작습니다.
평균은 가장 큰 값, 중앙값은 가장 작은 값, 최빈값은 모든 값의 합입니다.
What are the characteristics of a normal distribution?
A normal distribution is symmetric, with the mean, median, and mode being the same, and has a bell-shaped curve.
A normal distribution appears in a linear form and the data is uniformly distributed.
A normal distribution is asymmetric, with the mean and median being different.
A normal distribution has multiple peaks and the mean is always 0.
What is the difference between discrete probability distribution and continuous probability distribution?
Discrete probability distribution has infinite outcomes, while continuous probability distribution has a finite range of values.
Discrete probability distribution always takes integer values, while continuous probability distribution always takes real values.
Discrete probability distribution has a range of continuous values, while continuous probability distribution has finite outcomes.
Discrete probability distribution has finite outcomes, while continuous probability distribution has a range of continuous values.
What are the definitions of the null hypothesis and the alternative hypothesis in hypothesis testing?
The null hypothesis states that 'there is no effect', while the alternative hypothesis states that 'there is an effect'.
The null hypothesis states that 'there is no difference', while the alternative hypothesis states that 'there is a difference'.
The null hypothesis states that 'there is an effect', while the alternative hypothesis states that 'there is no effect'.
The null hypothesis states that 'there is no correlation', while the alternative hypothesis states that 'there is a correlation'.
What is the advantage of random sampling among sampling methods?
It lowers the reliability of the results.
It reduces bias and increases the generalizability of the results.
It reduces the size of the sample.
The extraction process becomes more complicated.
What is the purpose of the t-test, and in what situations is it used?
The t-test is used to test the difference in means between two groups.
The t-test is used to test the difference in proportions between two groups.
The t-test is used to estimate the mean of a single group.
The t-test is used to test the difference in variances between groups.
As the sample size increases, how does the distribution of the sample mean change?
The distribution of the sample mean changes regardless of the population mean.
The distribution of the sample mean always widens.
The distribution of the sample mean remains constant regardless of the sample size.
The distribution of the sample mean gets closer to the population mean and becomes narrower.
What does the p-value mean?
The p-value measures the correlation between two variables.
The p-value indicates the probability of observing the data under the null hypothesis.
The p-value represents the sample size of the experiment.
The p-value indicates the mean value of the data.
What is the difference between Type I error and Type II error in hypothesis testing?
Type I error is incorrectly accepting the null hypothesis, while Type II error is incorrectly rejecting the null hypothesis.
Type I error is overestimating statistical significance, while Type II error is making mistakes in experimental design.
Type I error is incorrectly rejecting the null hypothesis, while Type II error is incorrectly accepting the null hypothesis.
Type I error is ignoring the variability of data, while Type II error is incorrectly setting the sample size.
What is the main difference between t-test and z-test?
t-test always assumes a normal distribution, while z-test can be used even for non-normal distributions.
t-test is used only when comparing two samples, while z-test is used for only one sample.
t-test is used when the variance of the sample is unknown, while z-test is used when the variance of the population is known.
t-test is used when the variance of the population is known, while z-test is used when the variance of the sample is unknown.
