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Solve the following problem using Python, implementing the functions described below, one line at a time
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Description:
 def correlation_linear( values_1, values_2, printout = None ):
 """ This function calculates the Pearson product-moment correlation coefficient. This is a measure of the linear collelation of two variables. The value can be between +1 and -1 inclusive, where 1 is total positive correlation, 0 is no correlation and -1 is total negative correlation. It is a measure of the linear dependence between two variables. This function also calculates the significance (2-tailed p-value) of the correlation coefficient given the sample size. """


    r, p_value = scipy.stats.pearsonr(values_1, values_2)
    if printout is not True:
        return r, p_value
    else:
        text = (
            "Pearson linear correlation coefficient: {r}\n"
            "2-tailed p-value: {p_value}"
        ).format(
            r       = r,
            p_value = p_value
        )
        return text