What are some real-world implications of kurtosis in a dataset?
- Datasets with high kurtosis are easier to interpret
- High kurtosis can indicate a bias in data collection
- High kurtosis can indicate the presence of outliers
- Kurtosis does not have real-world implications
In real-world data analysis, kurtosis is used to identify the presence of outliers. High kurtosis in a dataset may signal an increase in tail risk. This is particularly relevant in fields like finance, where tail risk could translate into heavier losses than the normal distribution would predict.
What does the Wilcoxon Signed Rank Test compare in paired samples?
- Means
- Medians
- Modes
- Variance
The Wilcoxon Signed Rank Test compares the medians in paired samples.
What is the difference between correlation and causation?
- Causation implies correlation
- Correlation and causation are independent of each other
- Correlation implies causation
- Correlation means there is no causation
While correlation simply implies a relationship between two variables, causation goes a step further to explain that one variable actually causes the other to change. It's important to remember that correlation does not imply causation. However, if there is causation, there's likely to be correlation.
The correlation coefficient is denoted by the letter __.
- C
- P
- R
- S
The correlation coefficient is often denoted by the letter 'R'. In the case of Pearson's correlation, it's specifically denoted as 'r'. It measures the degree of relationship between two variables.
What is the measure of central tendency that divides a data set into two equal halves?
- Mean
- Median
- Mode
- Range
The median is the measure of central tendency that divides a data set into two equal halves. When the observations are ordered from smallest to largest, the median is the middle value, ensuring that 50% of the data falls below and 50% above the median value.
The larger the number of observations, the closer the sample mean will be to the population mean, according to the _________.
- Central Limit Theorem
- Law of Large Numbers
- Probability Rule
- Sampling Distribution
According to the Law of Large Numbers, the larger the number of observations, the closer the sample mean will be to the population mean. This law is a fundamental principle of probability and statistics that states that as the size of a sample is increased, the estimate of certain parameters obtained from the sample will tend to approach the true value for the population.
What happens if the Kruskal-Wallis Test results in a statistically significant H value?
- It means nothing
- It means the groups are different
- It means the groups are the same
- It means the test failed
A statistically significant H value in the Kruskal-Wallis Test suggests that at least one of the sample distributions is different from the others.
How is the Chi-square distribution related to the normal distribution?
- The Chi-square distribution is a special case of the normal distribution
- The Chi-square distribution is the distribution of the square of a standard normal random variable
- The Chi-square distribution is the distribution of the sum of two standard normal random variables
- The normal distribution is a special case of the Chi-square distribution
The Chi-square distribution is related to the normal distribution in that it is the distribution of the square of a standard normal random variable.
Quantitative data can be broken down into two types: ________ and ________.
- Continuous, Categorical
- Discrete, Continuous
- Nominal, Ordinal
- Ratio, Interval
Quantitative data can be broken down into two types: Discrete and Continuous. Discrete data can only take specific values (like whole numbers) while Continuous data can take any value (within a range).
A negative Spearman's rank correlation coefficient indicates a(n) ________ association between two variables.
- Direct
- Inverse
- Positive
- Strong
A negative Spearman's rank correlation coefficient indicates an inverse association between two variables. That is, as one variable increases, the other tends to decrease.