diff --git a/statistic.py b/statistic.py
new file mode 100644
index 0000000..a662901
--- /dev/null
+++ b/statistic.py
@@ -0,0 +1,58 @@
+import math
+import itertools
+from numpy.polynomial.chebyshev import chebfit, chebval
+
+
+def med_dev(vals):
+    med = sum(vals) / len(vals)
+    dev = ((sum(abs(med - i) ** 2.0 for i in vals) / len(vals)) ** 0.5)
+    return med, dev
+
+
+def round_deviation(med_dev):
+    med, dev = med_dev
+
+    if dev < 1E-7:
+        return med_dev
+
+    dev_div = 10.0 ** (math.floor(math.log10(dev)) - 1)
+    dev = int(dev / dev_div) * dev_div
+    med = int(med / dev_div) * dev_div
+    return (type(med_dev[0])(med),
+            type(med_dev[1])(dev))
+
+
+def groupby_globally(data, key_func):
+    grouped = {}
+    grouped_iter = itertools.groupby(data, key_func)
+
+    for (bs, cache_tp, act, conc), curr_data_it in grouped_iter:
+        key = (bs, cache_tp, act, conc)
+        grouped.setdefault(key, []).extend(curr_data_it)
+
+    return grouped
+
+
+def approximate_curve(x, y, xnew, curved_coef):
+    """returns ynew - y values of some curve approximation"""
+    return chebval(xnew, chebfit(x, y, curved_coef))
+
+
+def approximate_line(x, y, xnew, relative_dist=False):
+    """returns ynew - y values of linear approximation"""
+
+
+def difference(y, ynew):
+    """returns average and maximum relative and
+       absolute differences between y and ynew"""
+
+
+def calculate_distribution_properties(data):
+    """chi, etc"""
+
+
+def minimal_measurement_amount(data, max_diff, req_probability):
+    """
+    should returns amount of measurements to get results (avg and deviation)
+    with error less, that max_diff in at least req_probability% cases
+    """