What is the difference between skewness and kurtosis?
- Skewness measures asymmetry, kurtosis measures variability.
- Skewness measures center, kurtosis measures spread.
- Skewness measures spread, kurtosis measures center.
- Skewness measures symmetry, kurtosis measures tailedness.
The difference between skewness and kurtosis is that skewness measures the asymmetry of a data distribution around its mean, whereas kurtosis measures the "tailedness" of a data distribution. So, skewness is about the symmetry, and kurtosis is about the tails of the distribution.
While analyzing a dataset using a box plot, you notice that there are several data points plotted as circles. What might these circles represent?
- Data within the interquartile range
- Data within the whiskers
- Median values
- Outliers
In a box plot, data points plotted as circles often represent outliers.
What is the key difference between 'removal' and 'transformation' of outliers?
- Removal changes the data distribution, while transformation does not
- Removal deals with extreme values, while transformation does not
- Removal discards outliers, while transformation modifies their values
- Removal is a type of data cleaning, while transformation is not
The key difference between 'removal' and 'transformation' of outliers is that removal discards outliers from the dataset, while transformation modifies the values of outliers to reduce their impact.
What is an outlier in the context of Exploratory Data Analysis?
- A data point that falls outside of the normal range
- A data point that is a duplicate
- A data point that is missing
- A frequently occurring data point
In statistics, an outlier is an observation that lies an abnormal distance from other values in a random sample from a population. In simple terms, an outlier is a value that is significantly different from other similar values.
You're working with a data set where a few observations are vastly different from the rest. Which method, Z-score or IQR, would be more robust to use for outlier detection?
- Either would work equally well
- IQR
- Neither would be effective
- Z-score
The IQR method is more robust than Z-score for outlier detection in this scenario, as Z-scores can be significantly affected by extreme values.
What is the underlying JavaScript library that Plotly uses to render its graphics?
- D3.js
- Node.js
- React.js
- jQuery
Plotly uses D3.js (Data-Driven Documents) under the hood to render its graphics. D3.js is a JavaScript library for producing dynamic and interactive data visualizations in web browsers.
Replacing missing values with the median of the existing values is known as _____ imputation.
- Mean
- Median
- Mode
- Pairwise
Replacing missing values with the median of the existing values is known as 'median' imputation. This technique is useful for skewed distributions as the median is less affected by outliers than the mean.
In a survey about income levels, some individuals chose not to disclose their earnings. How would you categorize this missing data?
- MAR
- MCAR
- NMAR
- Not missing data
This would also be NMAR (Not Missing at Random) because the missingness (income level) depends on the value of the unobserved data itself (i.e., people with higher or lower incomes may be more likely to omit this information).
_____ data can only take certain values with gaps between them.
- Continuous
- Discrete
- Nominal
- Ordinal
Discrete data can only take certain values (usually integers) and there are gaps between the values.
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.