dissertation in pdf-format - Aalto-yliopisto
dissertation in pdf-format - Aalto-yliopisto
dissertation in pdf-format - Aalto-yliopisto
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38 T. Heimonen and M. Virtanen<br />
Appendix 1<br />
Table A1 Characteristics of different methods<br />
Statistical<br />
method<br />
Assumptions and<br />
modell<strong>in</strong>g criteria<br />
Evaluation<br />
criteria<br />
Challenges <strong>in</strong><br />
<strong>in</strong>terpretation<br />
Regression analysis (RA)<br />
Dependent L<strong>in</strong>earity The fit of the model Model specification<br />
variable<br />
Normality of<br />
Explanatory power Characteristics of<br />
distribution of error<br />
entrepreneurial<br />
term<br />
market:<br />
Log growth Homoskedasticy Confidence <strong>in</strong>terval Discont<strong>in</strong>uity and<br />
unstability<br />
Success Equality of variances Multicoll<strong>in</strong>earity Change and dynamism<br />
Least square<br />
S<strong>in</strong>gularity Operationalisation of<br />
modell<strong>in</strong>g<br />
variables<br />
Logistical regression (LRA)<br />
Dependent<br />
variable<br />
comb<strong>in</strong>ed<br />
growth and<br />
success<br />
Degrees of freedom Measurement<br />
Log l<strong>in</strong>earity A priori probability<br />
distribution<br />
Model specification<br />
The fit of the model Classification and<br />
estimation<br />
Normality of<br />
distribution of error<br />
term<br />
Equality of variances Classification<br />
statistics<br />
Power based on<br />
probabilities and odd<br />
ratios<br />
Operationalisation of<br />
variables<br />
HG +HS = 1 Maximum likelihood<br />
modell<strong>in</strong>g<br />
Cross validation Measurement<br />
NHG + NHS = 0 Degrees of freedom Interpretation of<br />
coefficients<br />
Discrim<strong>in</strong>ant analysis (DA)<br />
Dependent<br />
variable<br />
comb<strong>in</strong>ed<br />
growth and<br />
success<br />
Mult<strong>in</strong>ormal<br />
population<br />
Equality of variances<br />
of groups<br />
L<strong>in</strong>ear comb<strong>in</strong>ations<br />
are compared to<br />
crossmatrix of the data<br />
HG + HS = 1 The amount of<br />
discrim<strong>in</strong>ant function<br />
(the amount of<br />
groups – 1)<br />
A priori probability<br />
distribution<br />
Canonical<br />
discrim<strong>in</strong>ant<br />
function<br />
Classification<br />
statistics<br />
The gap between<br />
centroids<br />
Model specification<br />
Classification and<br />
estimation<br />
Power based on<br />
discrim<strong>in</strong>ant function<br />
Operationalisation of<br />
variables<br />
NHG + NHS = 0 Degrees of freedom Cross validation Measurement