{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Lab 2-1: Hypothesis Testing\n", "\n", "The Skykomish River in Washington state appears to have had a change in streamflow around the year 1975. \n", "* Test for statistical significance of the observed change in the mean annual flood. \n", "* Use a two-sample test, with alpha=0.05 (i.e. 95% confidence) and the z-distribution to define the rejection region.\n", "\n", "Why is it appropriate to use the z-distribution here? (consider the [Central Limit Theorem](https://en.wikipedia.org/wiki/Central_limit_theorem))\n", "\n", "---" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", "import pandas as pd\n", "import scipy.stats as stats\n", "import matplotlib.pyplot as plt\n", "%matplotlib inline" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/opt/conda/lib/python3.8/site-packages/openpyxl/worksheet/_reader.py:312: UserWarning: Unknown extension is not supported and will be removed\n", " warn(msg)\n" ] }, { "data": { "text/html": [ "
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date of peakwater yearpeak value (cfs)gage_ht (feet)
01928-10-0919291880010.55
11930-02-0519301580010.44
21931-01-2819313510014.08
\n", "
" ], "text/plain": [ " date of peak water year peak value (cfs) gage_ht (feet)\n", "0 1928-10-09 1929 18800 10.55\n", "1 1930-02-05 1930 15800 10.44\n", "2 1931-01-28 1931 35100 14.08" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Filepath to our excel file.\n", "skykomish_data_file = '../data/Skykomish_peak_flow_12134500_skykomish_river_near_gold_bar.xlsx'\n", "\n", "# Use pandas.read_excel() function to open this file.\n", "skykomish_data = pd.read_excel(skykomish_data_file)\n", "\n", "# Now we can see the dataset we loaded:\n", "skykomish_data.head(3)" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "image/png": 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", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# Plot peak streamflows per water year\n", "fig, ax = plt.subplots(figsize=(7,4))\n", "\n", "skykomish_data.plot(x='water year', y='peak value (cfs)', ax=ax)\n", "ax.set_ylabel('streamflow (cfs)');\n", "ax.set_title('Skykomish River, Annual Peak Streamflow, (Gold Bar, WA)');" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "---\n", "\n", "## Getting started with our hypothesis\n", "\n", "We are postulating that there was a change in peak flows around 1975. In other words, how likely is it that the mean of peak flows before 1975 comes from the same distribution as the mean of peak flows after 1975?\n", "\n", "To start, let's split the data in two:" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "# Divide the data into the early period (before 1975) and late period (after and including 1975). \n", "\n", "skykomish_before = skykomish_data[ skykomish_data['water year'] < 1975 ]\n", "skykomish_after = skykomish_data[ skykomish_data['water year'] >= 1975 ]" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# Plot our two time periods\n", "fig, ax = plt.subplots(figsize=(7,4))\n", "\n", "skykomish_before.plot(x='water year', y='peak value (cfs)', ax=ax, linestyle='-', label='pre-1975')\n", "skykomish_after.plot(x='water year', y='peak value (cfs)', ax=ax, linestyle='--', label='post-1975')\n", "\n", "ax.set_ylabel('streamflow (cfs)');\n", "ax.set_title('Skykomish River, Annual Peak Streamflow, (Gold Bar, WA)');" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**What does the distribution of streamflows in each period look like?**\n", "\n", "Plot a histogram for each period:" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "image/png": 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", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "fig, (ax1, ax2) = plt.subplots(nrows=1, ncols=2, figsize=(10,3))\n", "\n", "ax1.hist(skykomish_before['peak value (cfs)'], bins=10)\n", "ax1.set_xlim((1e4,1.4e5))\n", "ax1.set_xlabel('peak value (cfs)')\n", "ax1.set_title('Skykomish River, Annual Peak Streamflow\\nHistogram Before 1975')\n", "\n", "ax2.hist(skykomish_after['peak value (cfs)'], bins=10)\n", "ax2.set_xlim((1e4,1.4e5))\n", "ax2.set_xlabel('peak value (cfs)')\n", "ax2.set_title('Skykomish River, Annual Peak Streamflow\\nHistogram After 1975');\n", "\n", "plt.tight_layout()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "---\n", "\n", "## Cumulative distribution functions\n", "\n", "Visually compare the distributions of the data, before and after 1975, with theoretical distributions, and random numbers generated from theoretical distributions." ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [], "source": [ "### Method 1\n", "# This function requires that the input is a pandas dataframe, with column names, and an integer index\n", "# It returns a copy of the dataframe with an extra column added that has the Cunnane plotting positions\n", "def cunnane_quantile(df, column_name):\n", " '''This function will compute the Cunnane plotting position for the values in a column of a dataframe.\n", " It requres a pandas dataframe, and the column name of interest (a text string) as inputs.\n", " The output is a new dataframe, ranked (sorted) with an extra column with the plotting position.\n", " [Steven Pestana, spestana@uw.edu, Oct. 2020]'''\n", " \n", " # Rank all our values\n", " ranked_df = df.sort_values(by=[column_name]).reset_index()\n", " \n", " # Calculate the Cunnane plotting position\n", " ranked_df['cunnane_plotting_position'] = ((ranked_df.index + 1) - (2/5)) / (ranked_df[column_name].count() + (1/5))\n", " \n", " return ranked_df\n", "\n", "### Method 2\n", "# This function should be able to accept any one-dimensional numpy array or list, of numbers\n", "# It returns two numpy arrays, one of the sorted numbers, the other of the plotting position\n", "def cunnane_quantile_array(numbers):\n", " '''This function also computes the Cunnane plotting position given an array or list of numbers (rather than a pandas dataframe).\n", " It has two outputs, first the sorted numbers, second the Cunnane plotting position for each of those numbers.\n", " [Steven Pestana, spestana@uw.edu, Oct. 2020]'''\n", " \n", " # 1) sort the data, using the numpy sort function (np.sort())\n", " sorted_numbers = np.sort(numbers)\n", " \n", " # length of the list of numbers\n", " n = len(sorted_numbers) \n", " \n", " # make an empty array, of the same length. below we will add the plotting position values to this array\n", " cunnane_plotting_position = np.empty(n)\n", " \n", " # 2) compute the Cunnane plotting position for each number, using a for loop and the enumerate function\n", " for rank, number in enumerate(sorted_numbers):\n", " cunnane_plotting_position[rank] = ( (rank+1) - (2/5) ) / ( n + (1/5) )\n", " \n", " return sorted_numbers, cunnane_plotting_position" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [], "source": [ "# Use the cunnane quantile function for before 1975\n", "skykomish_before_b = cunnane_quantile(skykomish_before, 'peak value (cfs)')\n", "\n", "# Create theoretical normal CDF based on our sample values before 1975\n", "theoretical_cdf_b = stats.norm.cdf(skykomish_before_b['peak value (cfs)'].values,\n", " skykomish_before_b['peak value (cfs)'].mean(),\n", " skykomish_before_b['peak value (cfs)'].std(ddof=1))\n", "\n", "# Generate random numbers from a theoretical normal CDF based on our samples before 1975\n", "random_normal_b = np.random.normal(skykomish_before_b['peak value (cfs)'].mean(),\n", " skykomish_before_b['peak value (cfs)'].std(ddof=1),\n", " size=skykomish_before_b['peak value (cfs)'].count())\n", "\n", "# Compute the Cunnane plotting position for the random numbers\n", "random_sorted_b, random_quantiles_b = cunnane_quantile_array(random_normal_b)" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [], "source": [ "# Use the cunnane quantile function for after 1975\n", "skykomish_after_a = cunnane_quantile(skykomish_after, 'peak value (cfs)')\n", "\n", "# Create theoretical normal CDF based on our sample values before 1975\n", "theoretical_cdf_a = stats.norm.cdf(skykomish_after_a['peak value (cfs)'].values,\n", " skykomish_after_a['peak value (cfs)'].mean(),\n", " skykomish_after_a['peak value (cfs)'].std(ddof=1))\n", "\n", "# Generate random numbers from a theoretical normal CDF based on our samples before 1975\n", "random_normal_a = np.random.normal(skykomish_after_a['peak value (cfs)'].mean(),\n", " skykomish_after_a['peak value (cfs)'].std(ddof=1),\n", " size=skykomish_after_a['peak value (cfs)'].count())\n", "\n", "# Compute the Cunnane plotting position for the random numbers\n", "random_sorted_a, random_quantiles_a = cunnane_quantile_array(random_normal_a)" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Text(0.5, 1.0, 'Skykomish River, Annual Peak Streamflow CDF\\nAfter 1975')" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "fig, [ax1, ax2] = plt.subplots(nrows=1, ncols=2, figsize=(12,4))\n", "\n", "# Before 1975\n", "# Empirical CDF\n", "ax1.plot(skykomish_before_b['peak value (cfs)'], skykomish_before_b['cunnane_plotting_position'], color='k', label='Empirical CDF')\n", "# Theorectical Normal CDF\n", "ax1.plot(skykomish_before_b['peak value (cfs)'], theoretical_cdf_b, label='Theoretical Normal CDF')\n", "# Random numbers CDF from a theoretical normal distribution\n", "ax1.plot(random_sorted_b,random_quantiles_b,'-', label='Random Numbers CDF')\n", "# Add legend and labels\n", "ax1.legend()\n", "ax1.set_ylabel('Cumulative Frequency')\n", "ax1.set_xlabel('Peak Flow (cfs)')\n", "ax1.set_title('Skykomish River, Annual Peak Streamflow CDF\\nBefore 1975')\n", "\n", "# After 1975\n", "# Empirical CDF\n", "ax2.plot(skykomish_after_a['peak value (cfs)'], skykomish_after_a['cunnane_plotting_position'], color='k', label='Empirical CDF')\n", "# Theorectical Normal CDF\n", "ax2.plot(skykomish_after_a['peak value (cfs)'], theoretical_cdf_a, label='Theoretical Normal CDF')\n", "# Random numbers CDF from a theoretical normal distribution\n", "ax2.plot(random_sorted_a, random_quantiles_a,'-', label='Random Numbers CDF')\n", "# Add legend and labels\n", "ax2.legend()\n", "ax2.set_ylabel('Cumulative Frequency')\n", "ax2.set_xlabel('Peak Flow (cfs)')\n", "ax2.set_title('Skykomish River, Annual Peak Streamflow CDF\\nAfter 1975')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Does the streamflow data look normally distributed? Maybe try changing the above code to compare the empirical CDFs against theoretical lognormal distributions. (Remember to transform the mean and standard deviations into \"log space\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "---\n", "## Two-Sample Z-Test\n", "\n", "#### Returning to our question: We are postulating (making a hypothesis) that there was a change in the mean flood statistics after 1975, and we want to test whether this is true. Where do we start?\n", "\n", "First we can formally state our null hypothesis, and our alternative hypothesis. We are also told to use a two sample test, and to set $\\alpha$ at 5%.\n", "\n", "Our **null hypothesis** is that the peak flows of the early period ($\\bar{X}_1$) are drawn from the same distribution as the peak flows of the later period ($\\bar{X}_2$) (therefore the distributions means of the two time periods are equal):\n", "\n", "$H_0: \\bar{X}_1 = \\bar{X}_2$\n", "\n", "Our **alternative hypothesis** is that the mean of the distribution for the later period is greater than that of the early period:\n", "\n", "$H_1: \\bar{X}_2 > \\bar{X}_1$\n", "\n", "We can also state these as:\n", "\n", "$H_0: \\bar{X}_1 - \\bar{X}_2 = \\mu_0$\n", "\n", "$H_1: \\bar{X}_1 - \\bar{X}_2 < \\mu_0$\n", "\n", "Where $\\mu_0$ is the hypothesized difference between the population means, and in this case $\\mu_0 = \\mu_1 - \\mu_2 = 0$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Note that I have written a \"[one-sided](https://en.wikipedia.org/wiki/One-_and_two-tailed_tests)\" test here because we are testing only for a change in one direction (an increase). We think that either the mean flood increased or it didn't change; we do not think the mean flood decreased:\n", "* This might be chosen because we have some physical reason to think it increased (e.g. higher elevations in the watershed now get rainfall where it used to mostly get snow because of our warming climate).\n", "* Or this might be chosen because we have some practical reason for the test to matter in this particular direction (e.g. we will change flood zoning downstream and/or how we operate a reservoir if the mean flood has increased, but won't make a change if it decreased)." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### But which test should we use? Is the z-distribution valid?\n", "\n", "We are using the [z-test](https://en.wikipedia.org/wiki/Z-test), which uses the standard normal distribution. From our work above, we know that our data are likely not neccesarily normally distributed. However, the [central limit theorem](https://en.wikipedia.org/wiki/Central_limit_theorem) tells us that, \"*If a sample of n values is extracted at random from a population with mean μ and standard deviation σ, and n > 30, then the means of these samples are approximately normally distributed*\"\n", "\n", "We calculate our z-score as: $\\displaystyle Z = \\frac{ (\\bar{X}_2 - \\bar{X}_1) - \\mu _{0} } { s_{1,2} }$\n", "\n", "Where $s_{1,2}$ is the \"pooled standard deviation\", $s_1$, $s_2$ and $n_1$, $n_2$ are the two standard deviations and sample sizes respectively.\n", "\n", "$s_{1,2} = \\displaystyle\\sqrt{ \\displaystyle\\frac{s^2_1}{n_1} + \\displaystyle\\frac{s^2_2}{n_2} }$\n", "\n", "#### Remember, the means are normally distributed even if the data themselves are not normally distributed." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "So what does the \"**Null Distribution** look like?\n", "\n", "And what do the \"rejection regions\" look like?" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "data": { "image/png": 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", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "fig, ax = plt.subplots(figsize=(10,7))\n", "\n", "# Create a null pdf\n", "x = np.linspace(-4, 4, num=160)\n", "ax.plot(x, stats.norm.pdf(x, 0, 1), label='Normal Distribution PDF')\n", "\n", "# Plot the null cdf\n", "ax.plot(x, stats.norm.cdf(x, 0, 1), linestyle='--', label='Normal Distribution CDF')\n", "\n", "# Plot the region that z_test would have to fall in in order for us to reject the null hypothesis\n", "conf = 0.95\n", "z_alpha = stats.norm.ppf(conf)\n", "shade = np.linspace(z_alpha, 4, 10)\n", "ax.fill_between(shade, stats.norm.pdf(shade, 0, 1) , color='k', alpha=0.5, label='reject region\\nfor alpha={}'.format(np.round(1-conf,2)))\n", "# Plot a line at z_alpha\n", "plt.axvline(z_alpha, color='black', label='$z_{a}$')\n", "# Plot a line at our 95% confidence\n", "plt.axhline(conf, color='black', linestyle='--', label='Confidence = {}%'.format(conf*100))\n", "\n", "\n", "# Add labels\n", "ax.set_ylim((0,1))\n", "plt.xlabel('Z', fontsize=15)\n", "plt.ylabel('PDF', fontsize=15)\n", "ax.legend(loc='center left', fontsize=14);" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "46\n", "45\n" ] } ], "source": [ "# Check that we have a large enough sample size (n>30)\n", "\n", "n = len(skykomish_before['peak value (cfs)'])\n", "print(n)\n", "\n", "m = len(skykomish_after['peak value (cfs)'])\n", "print(m)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Both are larger than 30, so we can go ahead and calculate the z-score for our test:" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [], "source": [ "# We want our alpha to be 0.05\n", "alpha = 0.05\n", "# This gives us a confidence of 0.95\n", "conf = 1 - alpha" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Next, determine which value in the z-distribution corresponds to our 0.95 confidence in the CDF" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "z_alpha = 1.6448536269514722\n" ] } ], "source": [ "z_alpha = stats.norm.ppf(conf)\n", "print(\"z_alpha = {}\".format(z_alpha)) " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Compute the pooled standard deviation, $s_{1,2} = \\displaystyle\\sqrt{ \\displaystyle\\frac{s^2_1}{n_1} + \\displaystyle\\frac{s^2_2}{n_2} }$" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [], "source": [ "# Compute the pooled standard deviaiton\n", "pooled_sd = np.sqrt( skykomish_before['peak value (cfs)'].std(ddof=1)**2 / n + skykomish_after['peak value (cfs)'].std(ddof=1)**2 / m )" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Finally, compute our z-score as $\\displaystyle Z = \\frac{ (\\bar{X}_2 - \\bar{X}_1) - \\mu _{0} } { s_{1,2} }$" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "z-score = 2.87\n" ] } ], "source": [ "# hypothesizing no change\n", "mu_0 = 0\n", "\n", "# compute z-score\n", "zscore = (skykomish_after['peak value (cfs)'].mean() - skykomish_before['peak value (cfs)'].mean() - mu_0)/pooled_sd\n", "\n", "print(\"z-score = {}\".format( np.round(zscore,2) )) " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can also compute a p-value from this z-score by looking it up on the standard normal distribution CDF" ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "p = 0.002\n" ] } ], "source": [ "pvalue = 1 - stats.norm.cdf(zscore)\n", "print(\"p = {}\".format( np.round(pvalue,3) ))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Plot the z-distribution, our z-score test result, and the $z_\\alpha$ that corresponds with our 95% confidence interval." ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [ { "data": { "image/png": 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", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# Create z values between -4 and 4 to look at the middle portion of the z-distribution around 0\n", "# Scale our values by the pooled standard deviation (otherwise we'd be in generic z-distribution space)\n", "z = np.linspace(-4, 4, num=160) * pooled_sd\n", "\n", "# Create the plot\n", "plt.figure(figsize=(10,7))\n", "# Plot the z-distribution here\n", "plt.plot(z, stats.norm.pdf(z, 0, pooled_sd), label='Null PDF: ($\\overline{X}_2 - \\overline{X}_1$) = 0')\n", "\n", "# Plot a line at our z-alpha value and shade the rejection region\n", "plt.axvline(z_alpha*pooled_sd, color='black', linestyle='-', label='$z_{a}$')\n", "shade = np.linspace(z_alpha*pooled_sd, np.max(z), 10)\n", "plt.fill_between(shade, stats.norm.pdf(shade, 0, pooled_sd) , color='k', alpha=0.5, label='rejection region\\nfor alpha={}'.format(np.round(1-conf,2)))\n", "\n", "\n", "plt.axvline(zscore*pooled_sd, color='red', linestyle='-', label='z-test')\n", "plt.xlabel('($\\overline{X}_2 - \\overline{X}_1$) [cfs]', fontsize=15)\n", "plt.ylabel('PDF', fontsize=15)\n", "plt.ticklabel_format(axis='x', style='sci', scilimits=(0,0))\n", "plt.ticklabel_format(axis='y', style='sci', scilimits=(0,0))\n", "plt.ylim(0, 9e-5)\n", "plt.legend(loc='best', fontsize=15);" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Questions:\n", "\n", "What do these test results mean? \n", "\n", "What does it mean that our z-test value (red line) fell within our \"rejection region\" of the null hypothesis PDF?\n", "\n", "What does our p-value tell us?" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "---\n", "\n", "## Test a different hypothesis\n", "\n", "How would the estimate of p change if our **null hypothesis** is now that the difference in the means is equal to 20% of the mean of the before-1975 period, and the alternative hypothesis is that a change more than 20% of the before-1975 period mean has occurred?\n", "\n", "Therefore\n", "\n", "$H_0: \\bar{X}_1 - \\bar{X}_2 = 0.2 \\cdot \\bar{X}_1$\n", "\n", "$H_1: \\bar{X}_1 - \\bar{X}_2 < 0.2 \\cdot \\bar{X}_1$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Compute our z-score and p-value:" ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "z-score = 1.12\n", "p = 0.132\n" ] } ], "source": [ "# hypothesizing a 20% change\n", "mu_0 = 0.2 * skykomish_before['peak value (cfs)'].mean()\n", "\n", "# compute z-score\n", "zscore = (skykomish_after['peak value (cfs)'].mean() - skykomish_before['peak value (cfs)'].mean() - mu_0)/pooled_sd\n", "print(\"z-score = {}\".format( np.round(zscore,2) )) \n", "\n", "pvalue = 1 - stats.norm.cdf(zscore)\n", "print(\"p = {}\".format( np.round(pvalue,3) ))" ] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# Create z values between -4 and 4 to look at the middle portion of the z-distribution around 0\n", "z = np.linspace(-4, 4, num=160)\n", "# Scale our values by the pooled standard deviation (otherwise we'd be in generic z-distribution space)\n", "z = [i * pooled_sd for i in z]\n", "\n", "# Create the plot\n", "plt.figure(figsize=(10,7))\n", "# Plot the z-distribution here\n", "plt.plot(z, stats.norm.pdf(z, 0, pooled_sd), label='Null PDF: ($\\overline{X}_2 - \\overline{X}_1$) = 0.2*$\\overline{X}_1$')\n", "\n", "# Plot a line at our z-alpha value and shade the rejection region\n", "plt.axvline(z_alpha*pooled_sd, color='black', linestyle='-', label='$z_{a}$')\n", "shade = np.linspace(z_alpha*pooled_sd, np.max(z), 10)\n", "plt.fill_between(shade, stats.norm.pdf(shade, 0, pooled_sd) , color='k', alpha=0.5, label='rejection region\\nfor alpha={}'.format(np.round(1-conf,2)))\n", "\n", "plt.axvline(zscore*pooled_sd, color='red', linestyle='-', label='z-test')\n", "plt.xlabel('($\\overline{X}_2 - \\overline{X}_1$) = 0.2*$\\overline{X}_1$ [cfs]', fontsize=15)\n", "plt.ylabel('PDF', fontsize=15)\n", "plt.ticklabel_format(axis='x', style='sci', scilimits=(0,0))\n", "plt.ticklabel_format(axis='y', style='sci', scilimits=(0,0))\n", "plt.ylim(0, 9e-5)\n", "plt.legend(loc='upper left', fontsize=15);" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### So what happened with this second test?\n", "\n", "We created a more stringent test.\n", "\n", "For example, say we will only recommend constructing taller levees along the river if the mean flood (defined by the annual peak flow) increased by more than 20%. While we can report that we are 95% sure that there is a change greater than 0 (our first test); we are **not** 95% sure that the change is greater than 20% of the early period mean." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.10.10" } }, "nbformat": 4, "nbformat_minor": 4 }