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Course Description: An introduction to multiple regression techniques with focus on economic applications.
Correlations among these microstructural variables and hardness were determined using multiple regression techniques.
The results showed that the neural networks provided more accurate predictions than did multiple regression techniques.
Baseline to follow-up change was analyzed using multiple regression techniques.
Moreover, multiple regression techniques were employed and their results were compared with those of neural networks.
Multiple regression techniques were used to identify the relationship between space heating load and behavioural variables.
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QAP is a multiple regression technique able to regress a valued matrix on other matrixes and does not assume independence of observations [ 21].
Data treatment was essentially based on the multiple regression technique.
The multiple regression technique indicated the robustness of this loyalty transformation model (adjusted R2= 0.50).
The data obtained were analysed employing multiple regression technique to generate suitable mathematical models.
Empirical data was collected from 58 GVTs and analyzed using a Hierarchical Multiple Regression technique.
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