You have a data set with a large number of outliers. Which measure of dispersion should you use to best describe the data set, and why?
- Interquartile range (IQR) because it is robust to outliers
- Range because it covers all values
- Standard deviation because it gives the average spread
- Variance because it squares the differences
When dealing with a large number of outliers in a data set, the "Interquartile range (IQR)" is the most suitable measure of dispersion. This is because it measures the statistical spread between the 25th and 75th percentiles, thus excluding outliers.
A teacher is analyzing test scores and finds that the distribution is bimodal, with one peak at 70 and another at 90. Which measure of central tendency might not be the best choice in this situation, and why?
- Mean, because it doesn't reflect the peaks
- Median, because it doesn't reflect the bimodality
- Mode, because there are two peaks
- None, because all are suitable
The "Mean" might not be the best choice in this situation because it does not reflect the two peaks. The mean would give a single central value, which does not accurately represent the two distinct groups in a bimodal distribution.
You are given a dataset where the salaries of a company are reported. The CEO's salary is significantly higher than the rest of the employees. Which measure of central tendency would give a more representative measure of the typical salary?
- Mean
- Median
- Mode
- None would be representative
The "Median" would be a more representative measure of the typical salary. Because the CEO's salary is an outlier and would significantly skew the mean, the median provides a more accurate central measure by considering the middle value in the sorted data.
Which of the following graphs can help identify outliers in a univariate dataset?
- Bar Chart
- Box Plot
- Line Graph
- Pie Chart
A box plot is a type of graph that can help identify outliers in a univariate dataset.
How does the Spearman's correlation handle ties compared to Kendall's Tau?
- It doesn't handle ties
- It handles ties better than Kendall's Tau
- It handles ties worse than Kendall's Tau
- The method of handling ties is the same
Spearman's correlation coefficient handles ties worse than Kendall's Tau. While both are rank correlation coefficients, Kendall's Tau is better at handling ties. Ties are handled in Spearman's correlation by assigning each tied group the mean of the ranks they would have received if they weren't tied.
In the context of outlier detection, a Z-score above or below _______ is typically considered as an outlier.
- 1.5
- 2
- 2.5
- 3
A data point with a Z-score above 3 or below -3 is usually considered an outlier. However, this threshold can vary depending on the context.
Even after concluding, it's crucial to '______' effectively in the EDA process, as this step is where your findings are shared and potentially acted upon.
- communicate
- conclude
- question
- wrangle
Even after concluding, it's crucial to 'communicate' effectively in the EDA process, as this step is where your findings are shared and potentially acted upon. Communication is not only about presenting the findings, but also about making sure that they are understood and can be acted upon.
If you want to represent both the distribution and density of data, a _____ plot is a good choice.
- Bar
- Line
- Scatter
- Violin
A Violin plot is a good choice to represent both the distribution and density of data. It combines aspects of both a box plot and a density plot, giving a fuller picture of the distribution.
What are the potential downsides of removing outliers from your dataset?
- It always improves the quality of the dataset
- It might discard important information
- It might introduce noise into the dataset
- nan
Removing outliers might discard potentially important information that could significantly influence the analysis results.
In a correlation matrix, the value -1 signifies a perfect _____ correlation between two variables.
- negative
- neutral
- positive
- random
In a correlation matrix, a value of -1 signifies a perfect negative correlation between two variables. This means that as one variable increases, the other decreases proportionally, and vice versa.