hypothesis_testing_exercise.ipynb

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   "source": [
    "# import numpy\n",
    "import numpy as np"
   ]
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    "# people weights \n",
    "weights = [94.93428306,  82.23471398, 97.95377076, 115.46059713, 80.31693251,  80.31726086, 116.58425631, \n",
    "           100.34869458,  75.61051228, 95.85120087, 75.73164614, 75.68540493, 89.83924543,  46.73439511,  \n",
    "           50.50164335,  73.75424942,  64.74337759,  91.28494665, 66.83951849, 56.75392597, 114.31297538, \n",
    "           80.48447399,  86.35056409,  56.50503628, 74.11234551,  66.1092259 ,  53.49006423,  68.75698018,\n",
    "           58.9936131 ,  62.0830625 ,  58.98293388,  83.52278185, 64.86502775,  54.42289071,  73.22544912,  \n",
    "           52.7915635 ,67.08863595,  45.40329876,  51.71813951,  66.96861236, 72.3846658 ,  66.71368281,  \n",
    "           63.84351718,  61.98896304, 50.2147801 ,  57.80155792,  60.39361229,  75.57122226, 68.4361829 , 47.36959845]"
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   "source": [
    "#### Set the significance level (alpha) to 0.05"
   ]
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    "#### Create function `evaluate_test` which prints a conclusion of hypothesis test based on p-value and alpha\n",
    "\n",
    "PARAMS:\n",
    "- p (float) - p-value from test\n",
    "- alpha (float) - significance level\n"
   ]
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    "#### Import Shapiro-Wilk Test to test if weights are normally distributed\n",
    "\n",
    "- H0 = weights are normally distributed\n",
    "- HA = weights are not normally distributed\n",
    "- https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.shapiro.html"
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   "source": [
    "#### Use function `evaluate_test` to make conclusion if weights are normally distributed"
   ]
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    "#### Test the hypothesis that mean of weights is equal to 72\n",
    "\n",
    "- use one sample t-test\n",
    "- H0: mean = 72\n",
    "- HA: mean != 72\n",
    "- note that we don't know the population standard deviation"
   ]
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   "source": [
    "#### Use function `evaluate_test` to make conclusion if the mean of the heights is 72"
   ]
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    "# salaries in the first company\n",
    "salaries_company_A = [ 62779.75930907,  67487.49834604,  78998.91885801,  92801.06354333,\n",
    "        94917.76195759,  85409.43843246,  65536.36510309,  97608.88920408,\n",
    "        79613.1791369 ,  74035.25988438,  72698.71057961,  57170.2204782 ,\n",
    "        96496.56571672,  78123.01652012,  69617.56847376,  89109.14505065,\n",
    "        91809.98342107,  54010.91167324, 103259.7319888 , 113319.79557154,\n",
    "        81529.81385057,  83590.49251746, 115902.53443622,  63608.1666576 ,\n",
    "        72175.25765417,  88719.32305603,  97215.1090373 ,  80570.98830349,\n",
    "        67796.25874935,  99321.80738101]\n",
    "\n",
    "# salaries in the second company\n",
    "salaries_company_B = [ 89845.96793876,  90027.93042629, 108596.08141043, 120113.67952031,\n",
    "        94794.04532001,  99565.51332692, 110927.06162603,  85471.82457925,\n",
    "        79030.8553638 ,  82644.84718934,  71592.66608011,  68244.23637394,\n",
    "       134420.97566401,  72106.76757987,  95429.7573215 ,  88285.90615416,\n",
    "       110973.4078626 ,  92323.32822085, 117740.37152488,  87412.61048855,\n",
    "        94906.53993793, 105017.39597368,  93983.46012639, 100538.051311  ,\n",
    "        95673.65143504,  61727.33698247, 105311.27474286, 113551.6401474 ,\n",
    "        87408.82036567,  85895.00912077]"
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    "#### Test the hypothesis that mean of salaries in both companies are equal\n",
    "- use t-test\n",
    "- H0: salaries are the same\n",
    "- HA: salaries are not the same\n"
   ]
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   "source": [
    "#### Use function `evaluate_test` to make conclusion if the salaries are equal in both comapnies"
   ]
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