This paper mainly focuses on statistical significance for the sale prices
of apartments in the Stockholm City Center. Linear regression model is
major models used in this case to study and examine result.
Linear regression can be used to fit a predictive model to a set of data
values as well as a structural interpretation which allows for hypotheses
testing.
There are 5 important assumptions we need to make concerning the
way in which the data are generated, such as ordinary least squares
estimation.
Also, it is important to examine how well the model represents the data
it is derived from and to what extent it is possible to use the model for
predictive purpose. This type of analysis is referred to as model
validation and may be done with different types of statistical tools, for
example, hypothesis testing.
Further, the author told us the method he used on narrowing down
large amounts of data in order to see relevant patterns and
relationships between variables.
Based on these basic theories, the author gathered the data set, used
regression model for his analysis then reached to a conclusion:
The result indicates support for the hypothesis that proximity to public
transport is positive for the price of an apartment.