A brief note on some basic terms in descriptive statistics: for Housing Studies students

A brief note on some basic terms in descriptive statistics: for Housing Studies students (information from perplexity.ai)

My question: "Briefly explain the basic terms of statistics of mean, median, mode, skewness, standard deviation and variance in the context of Housing Studies."

The reply: "In Housing Studies, basic statistical terms are used to analyze and interpret housing data, helping to understand housing patterns and resident experiences:

• Mean: The average value of a dataset, such as average housing expense or living area per head, calculated by summing all values and dividing by the number of observations. It reflects the typical value but can be affected by extreme values (outliers).​

• Median: The middle value when the data are ordered from smallest to largest. For example, the median housing cost represents the midpoint at which half the residents pay less and half pay more. Unlike the mean, it is less sensitive to extreme values and is useful when data are skewed.​

• Mode: The most frequently occurring value in a dataset, such as the most common housing satisfaction score or housing type. It can identify popular or typical categories in housing data.​

• Skewness: A measure of the asymmetry of the data distribution. In housing studies, skewness can indicate whether variables like housing prices or satisfaction scores lean more towards higher or lower values, affecting the mean, median, and mode relationship. For instance, a right (positive) skew means higher values are more spread out.​

• Standard Deviation: It quantifies the extent of variation or dispersion of housing-related data points from the mean. A low standard deviation means values are close to the mean (e.g., similar housing costs), while a high standard deviation indicates more variability among residents or housing units.​

• Variance: The average of the squared differences from the mean, another measure of data spread used in housing studies to understand variability in metrics like income, rent, or living density.​

These statistics help housing researchers describe and interpret data patterns, assess inequalities or resource distribution, and support evidence-based housing policies and management decisions."

** also study a video on introducing these basic terms; a specific video on skewness.

A collection of blog notes on using chatgpt for research purpose.