Predicting The Demand For Shared Bicycles In Seoul By Multiple Linear Regression
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Keywords:
Linear Regression, Shared Bicycles Correlation, Analysis MulticollinearityAbstract
The study used a multiple linear regression model to model the demand for shared bicycles and
related factors in Seoul for the year 2020. Data analysis was performed to find out the influencing factors
that affect the demand for shared bicycles in Seoul. Correlation analysis was carried out to check the
relationship between all variables and identify the multicollinearity problem in the data. After fitting
multiple linear regression, it was found that the demand for shared bicycles in Seoul was significantly
affected by hour of the day, temperature, humidity, visibility, solar radiation and rainfall. Among these
variables, it was found out that solar radiation is the most important factor.
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