Ordinary least squares regression giving incorrect prediction
I am using statsmodels OLS to match a series of dots in a line:
import statsmodels.api as sm Y = [1, 2, 3, 4, 5, 6, 7, 8, 9, 11, 12, 13, 14, 15] X = [[73.759999999999991], [73.844999999999999], [73.560000000000002], [73.209999999999994], [72.944999999999993], [73.430000000000007], [72.950000000000003], [73.219999999999999], [72.609999999999999], [74.840000000000003], [73.079999999999998], [74.125], [74.75], [74.760000000000005]] ols = sm.OLS(Y, X) r = ols.fit() preds = r.predict() print preds
And I am getting the following results:
[ 7.88819844 7.89728869 7.86680961 7.82937917 7.80103898 7.85290687
7.8015737 7.83044861 7.76521269 8.00369809 7.81547643 7.92723304
7.99407312 7.99514256]
This is about 10 times. What am I doing wrong? I tried to add a constant that just makes the values 1000 times bigger. I'm not very good at statistics, so maybe I need to do something with the data?
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1 answer
I think you switched your answer and your predictor as suggested by Michael Mayer in his comment. If you plot the prediction data from your model, you get something like this:
import statsmodels.api as sm import numpy as np import matplotlib.pyplot as plt Y = np.array([1,2,3,4,5,6,7,8,9,11,12,13,14,15]) X = np.array([ 73.76 , 73.845, 73.56 , 73.21 , 72.945, 73.43 , 72.95 , 73.22 , 72.61 , 74.84 , 73.08 , 74.125, 74.75 , 74.76 ]) Design = np.column_stack((np.ones(14), X)) ols = sm.OLS(Y, Design).fit() preds = ols.predict() plt.plot(X, Y, 'ko') plt.plot(X, preds, 'k-') plt.show()
If you switch X and Y, which I think you want, you get:
Design2 = np.column_stack((np.ones(14), Y)) ols2 = sm.OLS(X, Design2).fit() preds2 = ols2.predict() print preds2 [ 73.1386399 73.21305699 73.28747409 73.36189119 73.43630829 73.51072539 73.58514249 73.65955959 73.73397668 73.88281088 73.95722798 74.03164508 74.10606218 74.18047927] plt.plot(Y, X, 'ko') plt.plot(Y, preds2, 'k-') plt.show()
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