Descriptive Statistics and Covariance and Correlation

It helps in cleaning and visualizing data

It enables predictive modeling

It allows inference from sample data to the population

All of the above

Nominal

Ordinal

Interval

Ratio

2

3

5

7

σ² = ∑(x - μ)² / N

σ = ∑(x - μ)² / N

σ = ∑(x - μ) / N

σ² = ∑(x - μ) / N

Median

Mode

Mean

None of these

The difference between the largest and smallest values

The spread of data around the mean

The middle value of a dataset

The most frequently occurring value

To summarize and categorize data

To calculate the mean of a dataset

To measure variability in data

To determine correlation between variables

Covariance measures both direction and strength, while correlation only measures direction

Covariance depends on the scale of variables, whereas correlation is standardized

Covariance is always positive, while correlation can be negative

Covariance is used only for categorical data, while correlation is used for numerical data

It only measures relationships between two variables

The diagonal elements represent the covariance between different variables

It is a square matrix where diagonal elements represent variance and off-diagonal elements represent covariance

It is used to find non-linear relationships in a dataset

When the relationship between variables is strictly linear

When the data contains outliers or follows a non-normal distribution

When the data is already standardized

When analyzing categorical data