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.
Regularization techniques like Ridge and Lasso can indirectly perform feature selection by assigning a _______ coefficient to irrelevant features.
- Negative
- Non-zero
- Positive
- Zero
Regularization techniques like Ridge and Lasso can indirectly perform feature selection by assigning a zero coefficient to irrelevant features. This is achieved by adding a penalty term to the loss function that encourages smaller or zero coefficients, effectively removing the irrelevant features from the model.
What is a correlation matrix and what is its primary purpose in Exploratory Data Analysis?
- A graphical representation of the correlation between variables
- A representation of missing values in the data
- A representation of the data distribution
- A visual representation of data clusters
A correlation matrix is a tabular data representing the correlations between pairs of variables. Each cell in the table shows the correlation between two variables. It's primary use in EDA is to understand the linear relationship between the variables.
How can histograms be used to detect outliers?
- Outliers are represented by bars that are far away from others
- Outliers are represented by the shortest bars
- Outliers are represented by the tallest bars
- Outliers cannot be detected with histograms
In a histogram, outliers can often be represented by bars that are noticeably separated from the rest of the data distribution.
You are required to create a complex statistical plot to identify and present possible correlations between multiple variables in your dataset. Which Python library would be the most appropriate for this task?
- Bokeh
- Matplotlib
- Plotly
- Seaborn
Seaborn is best suited for creating complex statistical plots. It provides high-level, attractive statistical plots and integrates well with pandas DataFrames, allowing direct use of column names for the axes and other arguments.