You have found that your dataset has a high degree of multicollinearity. What steps would you consider to rectify this issue?
- Add more data points
- Increase the model bias
- Increase the model complexity
- Use Principal Component Analysis (PCA)
One way to rectify multicollinearity is to use Principal Component Analysis (PCA). PCA transforms the original variables into a new set of uncorrelated variables, thereby removing multicollinearity.
Which of the following best describes qualitative data?
- Data that can be categorized
- Data that can be ordered
- Data that can take any value
- Data that is numerical in nature
Qualitative data refers to non-numerical information that can be categorized based on traits and characteristics. It captures information that cannot be simply expressed in numbers.
In the context of EDA, what does the concept of "data wrangling" entail?
- Calculating descriptive statistics for the dataset
- Cleaning, transforming, and reshaping raw data
- Training and validating a machine learning model
- Visualizing the data using charts and graphs
In the context of EDA, "data wrangling" involves cleaning, transforming, and reshaping raw data. This could include dealing with missing or inconsistent data, transforming variables, or restructuring data frames for easier analysis.
Which library would you typically use for creating 3D plots in Python?
- Matplotlib
- Pandas
- Plotly
- Seaborn
Matplotlib has a toolkit 'mplot3d' which is used for creating 3D plots. It provides functions for plotting in three dimensions, making it versatile for a variety of 3D plots.
You have a dataset that follows a Uniform Distribution. You are asked to transform this data so it follows a Normal Distribution. How would you approach this task?
- By adding a constant to each value in the dataset
- By applying the Central Limit Theorem
- By normalizing the dataset using min-max normalization
- By squaring each value in the dataset
A Uniform Distribution can be approximated to a Normal Distribution by the application of the Central Limit Theorem, which states that the sum of a large number of independent and identically distributed variables, irrespective of their shape, tends towards a Normal Distribution.
What does MAR signify in data analysis related to missing data?
- Missed At Random
- Missing And Regular
- Missing At Random
- Missing At Range
In data analysis, MAR signifies Missing At Random. This indicates that the missingness is not random, but that it is also not related to the missing data, only the observed data.
How can one ensure that the chosen data visualization technique doesn't introduce bias in the interpretation of the results?
- By choosing colorful visuals
- By considering the data's context and choosing appropriate scales and ranges
- By only using one type of visualization technique
- By using complex visualization techniques
To avoid introducing bias in interpretation, it's crucial to consider the context of the data and choose appropriate scales and ranges for visualization. Misrepresentative scaling can distort the data's perception. It is also important to use a suitable type of visualization for the data and question at hand. For example, a pie chart would be inappropriate for showing trends over time.
How does multicollinearity affect feature selection?
- It affects the accuracy of the model
- It causes unstable parameter estimates
- It makes the model less interpretable
- It results in high variance of the model
Multicollinearity, which refers to the high correlation between predictor variables, can affect feature selection by causing unstable estimates of the parameters. This instability can lead to strange and unreliable predictions, making the feature selection process less accurate.
Modified Z-score is a more robust estimator in the presence of _______.
- normally distributed data
- outliers
- skewed data
- uniformly distributed data
The modified Z-score is more robust in the presence of outliers, making it better suited to datasets with many extreme values.
What type of data is Spearman's correlation most suitable for?
- Categorical data
- Continuous, normally distributed data
- Nominal data
- Ordinal data
Spearman's correlation is most suitable for ordinal data. It assesses how well the relationship between two variables can be described using a monotonic function. Because it's based on ranks, it can be used with ordinal data, where the order is important but not the difference between values.