Data Analysis Quiz: Questions And Answers

Pearson is for categorical data; Spearman is for numerical data

Pearson assumes linear relationships; Spearman assesses monotonic relationships

Pearson is non-parametric; Spearman is parametric

Pearson handles outliers better than Spearman

By transforming features into a lower-dimensional space

By assigning equal importance to all features

By evaluating the correlation between features

By measuring the entropy of each feature

Assessing the correlation between clusters

Creating a hierarchy of clusters based on similarities

Identifying outliers in clustered data

Measuring the similarity within clusters

Assessing multicollinearity in regression models

Evaluating the distribution of residuals

Summarizing the performance of a classification model

Identifying outliers in a dataset

Identifying outliers in time series data

Handling missing values in time series data

Forecasting future values by giving more weight to recent observations

Assessing the autocorrelation between time series and lagged values

Reducing model complexity

Combining predictions from multiple models

Handling outliers by giving less weight to extreme values

Ensuring that features contribute equally to a model

Identifying outliers in a dataset

Assessing the spread of data

Evaluating the correlation between predictor variables

Handling missing values in regression models

The range of values within which a population parameter is estimated to lie

The average of sample values

The proportion of data falling within a specified range

The standard error of the mean

Converting numerical features into categorical bins

Removing outliers from a dataset

Transforming features into a lower-dimensional space

Filling missing values in a dataset

The ratio of true positives to false positives

The improvement in predictive performance compared to a random model

The increase in model complexity

The impact of outliers on model predictions