Impact of Particular Indicators of Urban Development of Cities in the Czech Republic on Average Road Traffic Intensity - Geoinformatics for Intelligent Transportation - page 84

positive/negative in fl uence to the road traf c intensity in that span. The final cal-

culation of the average values of correlation coef cients (one value for one indicator

of all cities unlike the sums mentioned above) was used to show what the depen-
dence of road traf

c intensity in all cities to indicators as one value for each
indicator. This value does not show variability of individual cities as it smoothens
them out by averaging.

4.2 Multiple Linear Regression

Multiple linear regression (MLR) is a multivariate statistical technique. It was
performed on software called SPSS (Statistical Package for the Social Sciences).
SPSS is a computer program used for statistical analysis and further for survey
authoring and deployment, data mining, text analytics, and collaboration and
deployment. MLR can model the linear relationship between a dependent variable
and more than one explanatory (independent) variable. The mathematical formula
applied to the explanatory variables to best explain or predict the dependent vari-
able is the following:

A dependent variable (ARTI) is the variable representing the process which
should be predicted or understood. Explanatory variables (attributes of population,
land use, etc.) are the variables used to model or to predict the dependent variable
values. The dependent variable is a function of

the explanatory variables.

Regression coef cients are values, one for each explanatory variable, that represent

the strength and the type of the relationship the explanatory variable has to the
dependent variable [ 12 ]. There are a few main assumptions of a regression analysis.

1. Independent variables should not be highly intercorrelated (the assumption of
the absence of multicollinearity). Multicollinearity leads to an unstable corre-
lation matrix and can produce unreliable regression estimates, signi cance

dence intervals.
2. There will not be outliers that could distort results.
3. The variables are related in a linear fashion. Since multiple regression is based
on Pearson

levels and con

cient, which is only sensitive to linear relation-
ships, gross departures from linearity will mean that important relationships will
remain undetected.
4. The variables are normally distributed [ 13 ].

' is correlation coef