def main(events):
data = get_event_data(events)
if len(data) == 0:
sys.exit("ERROR: Unable to retrieve data from Mixpanel")
matrix = event_data_to_matrix(data, events)
response_name = events.pop(0)
response_data = matrix[:,0]
predictors_data = matrix[:,1:]
predictors_names = events
model = ols(response_data, predictors_data, response_name, predictors_names)
model.summary()
## Generate Equation
# Gather list of coefficients so we can build our formula
coeff_dict = dict(zip(model.x_varnm, model.b))
equation = "%s = %.5f" % (response_name, coeff_dict['const']) # Start with constant coefficient
for name in predictors_names:
val = coeff_dict[name]
equation += " + %.5f(%s)" % (val, name)
print "Regression equation for response variable '%s'" % response_name
print
print equation
def main(events):
data = get_event_data(events)
matrix = event_data_to_matrix(data, events)
print "Correlation coefficients"
for event1, event2 in list_to_pairs(events):
data1 = matrix[:,events.index(event1)]
data2 = matrix[:,events.index(event2)]
coeff = corrcoef(data1, data2)[0][1]
print "%s\tx\t%s:\t%f" % (event1, event2, coeff)
