A ___________ skewness indicates that the data distribution is skewed to the left.
- Any of these
- Negative
- Positive
- Zero
Negative skewness refers to a distribution where the left tail is longer or fatter than the right tail. In such distributions, the majority of the values (including the median and the mode) tend to be greater than the mean.
What are the 'dots' appearing outside the 'whiskers' of a box plot typically indicating?
- The mean of the data
- The median of the data
- The mode of the data
- The outliers in the data
In a box plot, 'dots' that appear outside the 'whiskers' are typically indicating the outliers in the data.
You've applied the IQR method on your dataset and found no outliers. However, you suspect there may be some. What could be your next steps?
- All of these
- Increase the IQR threshold
- Inspect the data visually
- Use a different outlier detection method
When the IQR method fails to detect suspected outliers, it may be useful to try a different approach, such as increasing the IQR threshold, using a different method, or visualizing the data.
What is the effect of redundant features on a machine learning model?
- All of the above
- They can lead to overfitting
- They can reduce the interpretability of the model
- They can slow down the learning process
Redundant features can lead to overfitting, slow down the learning process, and reduce the interpretability of the model.
You are required to visualize the density of data on a single continuous variable. Which type of plot would you use and why?
- Scatter plot
- Line graph
- Kernel Density Plot
- Bar graph
A Kernel Density Plot is the best option to visualize the density of data on a single continuous variable. This plot provides a smooth curve that gives a clear idea about the density of the distribution.
What is the key purpose of Predictive Modeling in data analysis?
- To confirm a pre-existing hypothesis
- To generate future data
- To make predictions based on the data
- To understand the underlying structure of the data
The key purpose of predictive modeling is to make predictions based on the data. Using various statistical techniques and machine learning algorithms, predictive modeling enables analysts to predict future outcomes based on historical data.
What are the limitations of using a modified Z-score for outlier detection?
- It assumes data is normally distributed
- It cannot handle missing values
- It is sensitive to extreme values
- It uses median instead of mean, which may not always be appropriate
A limitation of the modified Z-score is that it uses the median and MAD instead of the mean and standard deviation, which may not always be appropriate, especially for normally distributed data.
A data analyst needs to demonstrate the occurrence of outliers in a dataset using a plot. Which plot type would you recommend and why?
- Bar graph
- Box plot
- Line graph
- Scatter plot
The Box plot is ideal for demonstrating outliers in a dataset. The 'whiskers' in a box plot represent the range for the bulk of the data, and any data point that falls outside of this range is visually represented as an outlier.
The process of combining highly correlated variables into one is called _________.
- Data Aggregation
- Principal Component Analysis (PCA)
- Standardization
- Variance Inflation
When dealing with multicollinearity, one approach is to combine the correlated variables into one using a technique such as Principal Component Analysis (PCA). PCA creates new uncorrelated variables that capture the information of the original variables.
The ______ of a scatter plot may indicate the presence of outliers in the dataset.
- correlation
- scatter
- slope
- trend line
In a scatter plot, the scattering or spread of data points can help identify outliers. Points that are distant from the main concentration of data can indicate potential outliers.