Coefficient of Determination(R Squared)(How to determine goodness of fit)?

# quote from 'introduction to computation and programming     # using Python, revised, MIT press import pylabdef getTrajectoryData(fileName):    dataFile = open(fileName, 'r')    distances = []    heights1, heights2, heights3, heights4 = [], [], [], []    discardHeader = dataFile.readline()    for line in dataFile:        d, h1, h2, h3, h4 = line.split()        distances.append(float(d))        heights1.append(float(h1))        heights2.append(float(h2))        heights3.append(float(h3))        heights4.append(float(h4))    dataFile.close()    return (distances, [heights1, heights2, heights3, heights4])    def processTrajectories(fileName):    distances, heights = getTrajectoryData(fileName)    numTrials = len(heights) #heights is a 'list of lists'    distances = pylab.array(distances)    #Get array containing mean height at each distances    totHeights = pylab.array([0]*len(distances))    for h in heights:        totHeights = totHeights + pylab.array(h)    meanHeights = totHeights/len(heights)    pylab.title('Trajectory fo Projectile (Mean of '\                + str(numTrials) + ' Trials)')    pylab.xlabel('Inches from Launch Point')    pylab.ylabel('Inches Above Launch Point')    pylab.plot(distances, meanHeights, 'bo')        #linear fit    a,b = pylab.polyfit(distances, meanHeights, 1)    altitudes = a*distances + b    pylab.plot(distances, altitudes, 'b', label = 'Linear Fit')    print 'RSquare of linear fit =', rSquared(meanHeights, altitudes)        #quadratic fit    a,b,c = pylab.polyfit(distances, meanHeights, 2)    altitudes = a*(distances**2) + b*distances + c    pylab.plot(distances, altitudes, 'b:', label = 'Quadratic Fit')    print 'RSquare of quadratic fit =', rSquared(meanHeights, altitudes)        pylab.legend()def rSquared(measured, predicted):    """Assumes measured a one-dimensional array of measured values               predicted a one-dimensional array of predicted values       Returns coefficient of determination"""    #square error(cost function)    estimateError = ((predicted - measured)**2).sum()    meanOfMeasured = measured.sum()/float(len(measured))    variability = ((measured - meanOfMeasured)**2).sum()    return 1 - estimateError/variability

%run "C:\Users\Administrator\test.py"
RSquare of linear fit = 0.0177433205441
RSquare of quadratic fit = 0.985765369287