Week 1 - Statistic Basics

The average of a dataset, calculated as the sum of all values divided by the number of values.

The middle value in an ordered dataset, dividing it into two equal halves.

The most frequently occurring value in a dataset.

A measure of the spread or variability of data around the mean.

The average of the squared deviations of each data point from the mean.

The difference between the maximum and minimum values in a dataset.

The range of the middle 50% of data, calculated as the difference between the first and third quartiles.

The asymmetry of a data distribution.

• Positive skewness: A tail on the right.
• Negative skewness: A tail on the left.

The "peakedness" of a distribution compared to a normal distribution.

• High kurtosis: Many extreme values.
• Low kurtosis: Flatter distribution.

A value that divides the dataset into 100 equal parts. Example: The 90th percentile includes 90% of the data below it.

Values dividing the dataset into four equal parts:

• Q1 (25th percentile), Q2 (Median, 50th percentile), Q3 (75th percentile).

visualizing the frequency distribution of data.

Visualizing data spread, medians, quartiles, and identifying outliers.

Exploring relationships between two variables.

Comparing group means or other aggregate values.

Displaying proportional contributions of categories to the whole.

A data point significantly deviating from the rest of the dataset, potentially affecting results.

A standardized value showing how many standard deviations a data point is from the mean.

Applying mathematical functions (e.g., logarithmic or square root) to modify data distribution.

Changing or grouping variable values to create new categories.

1. Skewness and kurtosis tests: Check distribution symmetry and peakedness.
2. Q-Q plots: Compare actual vs. expected values for normal distribution.