Multiple Linear Regression

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Multiple Linear Regression predicts one output from multiple inputs. This tutorial review three methods in Python to perform a simple regression problem with 2 dependent variables and 1 independent variables. The output from the statsmodels summary is reviewed to highlight the statistical information.
  1. APMonitor.com
  2. Gekko
  3. Regression
  4. t-statistic
  5. confidence
  6. parameter
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Автор

great videos sir, very informative. Please implement techniques like independent component analysis, kurtosis, RNN etc.

shahkhan
Автор

Good day APMonitor, I am new to python, less than a month and your videos have been a great help. Thank you!. I am trying to run a multiple linear regression with 4 independent variables. However, i am trying to put some constraints where all independent variables sum up to 1 with each of the beta values greater than or equals to zero. I have watched this video as well as the constrained linear regression video, but not able to crack it . (please see below)

m = GEKKO(remote=False); m.options.IMODE=2
c = m.Array(m.FV, 5); c[0].STATUS=1; c[1].STATUS=1; c[2].STATUS=1; c[3].STATUS=1; c[4].STATUS=1 #create new constants, first is the slope, second is the intercept
c[1].lower>=0 #putting constraint on the intercept
c[2].lower>=0
c[3].lower>=0
c[4].lower>=0
xd = m.Param(X0_array, X1_array, X2_array, X3_array); yd = m.Param(Y_array); yp = m.Var() #your X and Y data as well as your predicted Y

m.Minimize((yd-yp)**2)
m.solve(disp=False)
c = [c[0].value[0], c[1].value[1], [c[2].value[2], c[3].value[3], c[4].value[4], c[5].value[5]

any pointers you have is greatly appreciated.

bigwolls