def correlation():
"Check correlation between actual object LOC and hours"
# according [HUMPHREY95] p.513 & p.151:
# - when 0.9 <= r2 : the relationship is considered predictive
# - when 0.7 <= r2 < 0.9 : there is a strong correlation
# - when 0.5 <= r2 < 0.7 : there is an adequate correlation (use with caution)
# - when r2 < 0.5 : not reliable for planning purposes
actual_loc, hours = get_projects_metrics()
r = calc_correlation(actual_loc, hours)
r2 = r ** 2
if 0.9 <= r2:
corr = "high (predictive)"
elif 0.7 <= r2 < 0.9:
corr = "strong (planning)"
elif 0.5 <= r2 < 0.7:
corr = "adequate (use with care)"
elif r2 < 0.5:
corr = "weak (not reliable)"
return {"loc": actual_loc, "hours": hours, "r2": r ** 2, "correlation": corr}
def correlation():
"Check correlation between actual object LOC and hours"
# according [HUMPHREY95] p.513 & p.151:
# - when 0.9 <= r2 : the relationship is considered predictive
# - when 0.7 <= r2 < 0.9 : there is a strong correlation
# - when 0.5 <= r2 < 0.7 : there is an adequate correlation (use with caution)
# - when r2 < 0.5 : not reliable for planning purposes
actual_loc, hours = get_projects_metrics()
r = calc_correlation(actual_loc, hours)
r2 = r**2
if 0.9 <= r2:
corr = 'high (predictive)'
elif 0.7 <= r2 < 0.9:
corr = 'strong (planning)'
elif 0.5 <= r2 < 0.7:
corr = 'adequate (use with care)'
elif r2 < 0.5:
corr = 'weak (not reliable)'
return {'loc': actual_loc, 'hours': hours, 'r2': r**2, 'correlation': corr}
def correlation():
r = calc_correlation(x_values, y_values)
return {'r2': r**2, 'ok': round(r**2, 4)==0.9107}
