Imagine you are examining a correlation matrix and find that two variables have a correlation coefficient close to -1. What does this imply about the relationship between these two variables?
- Their relationship is random
- They are unrelated
- They have a strong negative relationship
- They have a weak positive relationship
A correlation coefficient close to -1 implies that the two variables have a strong negative relationship. This means that as one variable increases, the other decreases and vice versa.
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.
Consider you are using a correlation matrix to understand the relationship between multiple features. You come across a correlation coefficient of -0.85 between two features. What does this indicate?
- A strong negative linear relationship
- A strong positive linear relationship
- A weak positive linear relationship
- No relationship
A correlation coefficient of -0.85 indicates a strong negative linear relationship between two features. This means as one feature increases, the other decreases.
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.