A brief teaching note on descriptive statistics and inferential statistics: for Housing Studies students (information from perplexity.ai)
My question 1: "Briefly describe the two types of statistics, namely descriptive statistics and inferential statistics in the context of quantitative research in housing studies."
The reply: "In the context of quantitative research in housing studies, descriptive statistics are used to summarize and describe the main features of the collected housing data. They provide measures such as the mean, median, mode, range, variance, and standard deviation to characterize central tendencies, variability, and the overall distribution of key housing variables like size, price, or quality. Descriptive statistics help researchers to organize and visualize housing data clearly, focusing solely on the sample data without generalizing beyond it.
Inferential statistics, conversely, allow housing researchers to make conclusions or predictions about a larger housing population based on a sample dataset. They involve hypothesis testing, regression analysis, and confidence intervals to infer relationships and patterns, such as how certain factors influence housing prices or preferences across broader regions. Inferential statistics extend the understanding gained from sample data to draw broader generalizations and test research hypotheses relevant to housing studies.
Thus, descriptive statistics provide a foundational summary of housing data characteristics, while inferential statistics enable predictions and conclusions about the wider housing context from which the samples are drawn.
Examples of visualizations for descriptive housing data include:
Histograms: These show the distribution of housing variables like prices, highlighting common price ranges and skewness in the data.
Box Plots: Useful for visualizing the spread and quartiles of housing prices or sizes, as well as identifying outliers.
Scatter Plots: To explore relationships between housing variables, such as area versus price.
Heatmaps: Display correlations between multiple housing variables, such as price, number of bedrooms, and location factors.
Maps: Both static and interactive maps visualize spatial data patterns like housing density, tenure, or price variations across different neighborhoods.
Data Dashboards and Story Maps: These integrate various charts, maps, and narrative elements to provide comprehensive and engaging presentations of housing data insights.
Parallel Coordinates Plots: Useful for high-dimensional housing data, showing multiple variables and their relationships across different suburbs or census areas.
These visualizations help researchers and stakeholders understand the characteristics, patterns, and distributions in housing data effectively."
My question 2: "Please provide 3 illustrative examples of these two types of statistics usage in Housing Studies investigation (e.g. Housing Studies dissertation projects and assignments)."
The reply: "Here are three illustrative examples of using descriptive and inferential statistics in Housing Studies investigations, such as dissertation projects or assignments:
Descriptive statistics example: A dissertation examining the relationship between house size and housing satisfaction uses descriptive statistics to summarize key variables like mean rooms per person, frequency of reported space shortage, and distribution of housing satisfaction scores. These descriptive summaries provide a clear snapshot of the housing conditions and resident satisfaction levels in the sample.
Inferential statistics example: A study on the effect of average income on demand for indoor living space in municipalities applies multiple regression analysis (inferential statistics) to examine the statistical relationship, controlling for population density and housing prices. This method allows the researcher to infer how income changes impact living space demand beyond the sample, with hypothesis testing for significance.
Mixed example: A housing relocation study uses descriptive statistics for initial summaries of changes in residential satisfaction and travel satisfaction after moving, then employs structural equation modeling (inferential statistics) to test hypotheses about the causal relationships between residential environment changes, motivations, socioeconomic factors, and life satisfaction outcomes. This approach blends description with inference for deeper analysis.
These examples show how descriptive statistics provide essential summary insights, while inferential statistics enable hypothesis testing and population-level conclusions in housing research contexts. Both are vital to rigorous quantitative analysis in Housing Studies."
A brief video on this topic of the 2 types of statistics.
A collection of blog notes on using chatgpt for research purpose.
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