Thus, the general purpose of multiple 
            regression  is to learn more about the 
            relationship between several independent 
            or predictor variables and a dependent or 
            output variable.
          Suppose that the Yield in a chemical 
            process depends on Temperature and the 
            Catalyst concentration, a multiple 
            regression that describe this 
            relationship is,
                 Y = b0+b1*X1+b2*X2+ €   →  (a)
                      Where   Y = Yield.
                      X1 = Temp:, X2 = Catalyst cont:.
                This is multiple linear regression model 
            with 2 regressors.
          The term linear is used because equation 
            (a) is a linear function of the unknown 
            parameters bi’s.
           
          Regression Models.
            Depending on nature of 
             relationship regression 
             models are two types.
          Linear regression model, 
             including
         a.Simple-linear regression (one 
             indep: var.)
         b.Multiple-linear regression.
          Non-Linear regression model, 
             including
         a.Polynomial regression.
         b.Exponential regression ,etc.
       Types of multiple 
       regression
        • There are three types of multiple regression, 
         each of which is designed to answer a different 
         question:
          – Standard multiple regression is used to 
           evaluate the relationships between a set of 
           independent variables and a dependent 
           variable.
          – Hierarchical, or sequential, regression is 
           used to examine the relationships between a 
           set of independent variables and a 
           dependent variable, after controlling for 
           the effects of some other independent 
           variables on the dependent variable.
          – Stepwise, or statistical, regression is 
           used to identify the subset of independent 
           variables that has the strongest 
           relationship to a dependent variable.
                                   MODEL
                   REGRESSI LINIER BERGANDA
          Model yg memperlihatkan hubungan antara satu variable 
          terikat  (dependent variable) dgn beberapa variabel bebas 
          (independent variables).
             Yi  = 0 + 1 X1i  + 2 X2i + … +  k Xki  + i 
           dimana:  i = 1, 2, 3, …. N (banyaknya pengamatan)
            ,   ,   , …,   adalah parameter yang nilainya 
            0   1   2      k 
           diduga melalui model:
                Yi  = b0 + b1 X1i  + b2 X2i + … +  bk Xki 
            0 dan 1 : parameter dari fungsi yg nilainya akan 
             diestimasi.
            Bersifat stochastik  untuk setiap nilai X terdapat 
             suatu distribusi probabilitas seluruh nilai Y atau Nilai 
             Y tidak dapat diprediksi secara pasti karena ada 
             faktor stochastik   yang memberikan sifat acak 
                               i
             pada Y.
            Adanaya variabel  disababkan karena:
                                 i
                 Ketidak-lengkapan teori
                 Perilaku manusia yang bersifat random  
                 Ketidak-sempurnaan spesifikasi model  
                 Kesalahan dalam agregasi
                 Kesalahan dalam pengukuran