For example, you could use multiple regre… 0000048156 00000 n 0000006545 00000 n For multiple regression analysis the principal assumption is: 1. \$��!\$qL�Q��E^����`l�=��K-�nխ�������g�v���)�� B����Hܞt���S����}='l�&����~�C��vߓ'�~��s��>�q�m{6Ol��)����v�cwx�Ko�1�h���'� �A�.|l��iA���. R2-- squared multiple correlation tells how much of the Y variability is “accounted for,”. Answer one of your choice: A, B, C, or D A. 0000005607 00000 n The dependent and independent variables show a linear relationship between the slope and the intercept. 0≤R2≤1. The value of the residual (error) is not correlated across all observations. 0000002532 00000 n 0000004419 00000 n 0000007869 00000 n (b) How do we test a binary predictor for significance? 0000011964 00000 n 0000008912 00000 n It is used when the dependent variable is binary(0/1, True/False, Yes/No) in nature. There are two main advantages to analyzing data using a multiple regression model. 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