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Interactive_Confidence_Interval.ipynb

@@ -276,7 +276,7 @@
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@@ -290,7 +290,7 @@
     {
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       "application/vnd.jupyter.widget-view+json": {
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        "version_major": 2,
        "version_minor": 0
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@@ -323,7 +323,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -512,13 +512,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "4ace97f0d4664bd8be4a79b6863a26e9",
+       "model_id": "39edf33b4a264afba6312a4e60669125",
        "version_major": 2,
        "version_minor": 0
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@@ -532,12 +532,12 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "82d4710de9ad49729a1d5ebc2b3636ac",
+       "model_id": "50c1837b597440a0882842f2e6f7e1b9",
        "version_major": 2,
        "version_minor": 0
       },
       "text/plain": [
-       "Output(outputs=({'output_type': 'display_data', 'data': {'text/plain': '<Figure size 640x480 with 4 Axes>', 'i…"
+       "Output()"
       ]
      },
      "metadata": {},
@@ -573,7 +573,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -595,7 +595,7 @@
     "uib = widgets.HBox([n,smean,sstdev,alpha],)                            # basic widget formatting           \n",
     "uib3 = widgets.VBox([l2,uib],)\n",
     "\n",
-    "def ci_make(n,smean,sstdev,alpha):                                      # function to take parameters, make sample and plot\n",
+    "def ci_make(n,smean,sstdev,alpha):                                     # function to take parameters, make sample and plot\n",
     "    dof = n-1 \n",
     "    tscore = np.round(-1*stats.t.ppf(alpha/2.0, df=dof),2)\n",
     "    se = np.round(sstdev/(np.sqrt(n)),2)\n",
@@ -619,10 +619,10 @@
     "    plt.annotate(r'$CI_{\\mu} \\rightarrow \\overline{x} \\pm t_{\\left(\\frac{\\alpha}{2},dof\\right)} \\times \\frac{s_{x}}{\\sqrt{n}}$',[6.3,1.2+vset],size=12)\n",
     "    # plt.annotate(r'$CI_{\\mu} = $' + str(smean) + r'$ \\pm $' + str(tscore) + r' $\\times$ ',[6.3,1.1],size=12)\n",
     "    plt.annotate(r'$\\overline{x} = $' + str(smean),[6.4,1.35+vset],color='red'); plt.plot([6.8,7.0],[1.33+vset,1.27+vset],color='red')\n",
-    "    plt.annotate(r'$\\alpha = $' + str(alpha),[7.4,1.355+vset],color='red'); plt.plot([7.8,7.75],[1.33+vset,1.26+vset],color='red')\n",
-    "    plt.annotate(r'$s_{x} = $' + str(sstdev),[8.6,1.355+vset],color='red'); plt.plot([8.85,8.75],[1.33+vset,1.27+vset],color='red')\n",
+    "    plt.annotate(r'$\\alpha = $' + str(alpha),[7.3,1.355+vset],color='red'); plt.plot([7.75,7.6],[1.33+vset,1.26+vset],color='red')\n",
+    "    plt.annotate(r'$s_{x} = $' + str(sstdev),[8.4,1.355+vset],color='red'); plt.plot([8.55,8.45],[1.33+vset,1.27+vset],color='red')\n",
     "    plt.annotate(r'$dof = $' + str(dof),[7.4,1.05+vset],color='red'); plt.plot([7.9,7.8],[1.15+vset,1.10+vset],color='red')\n",
-    "    plt.annotate(r'$n = $' + str(n),[8.8,1.05+vset],color='red'); plt.plot([8.8,8.9],[1.15+vset,1.10+vset],color='red')\n",
+    "    plt.annotate(r'$n = $' + str(n),[8.5,1.05+vset],color='red'); plt.plot([8.55,8.65],[1.15+vset,1.10+vset],color='red')\n",
     "    \n",
     "    vset = +0.2\n",
     "    plt.plot([8.7,9.1],[1.115+vset,1.115+vset],lw=0.5,color='black')\n",
@@ -634,7 +634,7 @@
     "    plt.annotate(r'$CI_{\\mu} \\rightarrow $' + str(smean) + r'$ \\pm $' + str(tscore) + r' $\\times$ ' + str(se),[6.3,0.95+vset],size=12)\n",
     "    plt.annotate('statistic',[7.1,0.65+vset],rotation=270.0,ha='left',color='red'); plt.plot([7.23,7.23],[0.86+vset,0.93+vset],color='red')\n",
     "    plt.annotate('-score',[7.8,0.7+vset],rotation=270.0,ha='left',color='red'); plt.plot([7.93,7.93],[0.86+vset,0.93+vset],color='red')\n",
-    "    plt.annotate('standard error',[8.8,0.48+vset],rotation=270.0,ha='left',color='red'); plt.plot([8.93,8.93],[0.86+vset,0.93+vset],color='red')\n",
+    "    plt.annotate('standard error',[8.6,0.48+vset],rotation=270.0,ha='left',color='red'); plt.plot([8.73,8.73],[0.86+vset,0.93+vset],color='red')\n",
     "    \n",
     "    plt.annotate(r'$CI_{\\mu} \\rightarrow [$' + str(lower_CI) + ' , ' + str(upper_CI) + r'$]$',[6.3,0.5+vset],size=12)\n",
     "    \n",
@@ -642,7 +642,6 @@
     "    plt.annotate('upper interval = ' + str(upper_CI),[upper_CI-0.1,stats.t.pdf(upper_CI,df = dof, loc = smean, scale = sstdev/np.sqrt(n))+0.05],rotation=90.0)\n",
     "    plt.annotate('$\\overline{x} = $' + str(smean),[smean-0.1,stats.t.pdf(smean,df = dof, loc = smean, scale = sstdev/np.sqrt(n))+0.05],rotation=90.0)\n",
     "    \n",
-    "    \n",
     "    #plt.annotate(r'$F^{-1}_x($' + str(np.round(P,2)) + '$)$ = ' + str(np.round(x,2)),xy=[x+0.003,0.08],rotation=270.0)\n",
     "    \n",
     "    plt.xlim([0,10]); plt.ylim([0,2.0]); plt.ylabel('Density'); plt.xlabel('Value'); plt.title('Analytical Confidence Interval for Population Mean')\n",
@@ -667,13 +666,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "5bec43cb4b8747fb9198f055c6e433db",
+       "model_id": "f61e4ff5284b4419b7fa2c0ec0060a7c",
        "version_major": 2,
        "version_minor": 0
       },
@@ -687,12 +686,12 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "2bf29a2376a54692bf6e8339c9db7d2c",
+       "model_id": "daee34a89c1b446fa4a8ccfa9fca8e6a",
        "version_major": 2,
        "version_minor": 0
       },
       "text/plain": [
-       "Output(outputs=({'output_type': 'display_data', 'data': {'text/plain': '<Figure size 640x480 with 1 Axes>', 'i…"
+       "Output()"
       ]
      },
      "metadata": {},
@@ -737,20 +736,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 418,
+   "execution_count": 25,
    "metadata": {},
    "outputs": [],
    "source": [
     "# interactive calculation of the sample set (control of source parametric distribution and number of samples)\n",
     "l10 = widgets.Text(value='                                       Confidence Interval for Proportion, Analytical Method Demonstrated, Michael Pyrcz, Professor, The University of Texas at Austin',layout=Layout(width='950px', height='30px'))\n",
     "\n",
-    "n = widgets.IntSlider(min=1, max = 100, value = 30, step = 1, description = '$n$',orientation='horizontal',layout=Layout(width='400px', height='20px'),continuous_update=False)\n",
+    "n = widgets.IntSlider(min=1, max = 100, value = 30, step = 1, description = '$n$',orientation='horizontal',layout=Layout(width='300px', height='20px'),continuous_update=False)\n",
     "n.style.handle_color = 'red'\n",
     "\n",
-    "sprop = widgets.FloatSlider(min=0.1, max = 0.9, value = 0.4, step = 0.1, description = '$\\hat{p}$',orientation='horizontal',layout=Layout(width='400px', height='20px'),continuous_update=False)\n",
+    "sprop = widgets.FloatSlider(min=0.1, max = 0.9, value = 0.4, step = 0.1, description = '$\\hat{p}$',orientation='horizontal',layout=Layout(width='300px', height='20px'),continuous_update=False)\n",
     "sprop.style.handle_color = 'green'\n",
     "\n",
-    "alpha = widgets.FloatSlider(min=0.01, max = 0.40, value = 0.05, step = 0.01, description = r'$\\alpha$',orientation='horizontal',layout=Layout(width='400px', height='20px'),continuous_update=False)\n",
+    "alpha = widgets.FloatSlider(min=0.01, max = 0.40, value = 0.05, step = 0.01, description = r'$\\alpha$',orientation='horizontal',layout=Layout(width='300px', height='20px'),continuous_update=False)\n",
     "alpha.style.handle_color = 'gray'\n",
     " \n",
     "uib10 = widgets.HBox([n,sprop,alpha],)                                  # basic widget formatting           \n",
@@ -763,7 +762,7 @@
     "    numerator = np.round(np.sqrt(sprop*(1.0-sprop)),2)\n",
     "    sprop = np.round(sprop,2)\n",
     "    alpha = np.round(alpha,2)\n",
-    "    xval = np.linspace(0,10,1000)\n",
+    "    xval = np.linspace(0,10,10000)\n",
     "    pdf = stats.t.pdf(xval,df = dof, loc = sprop, scale = se)\n",
     "    lower_CI = np.round(sprop - tscore*se,2)\n",
     "    upper_CI = np.round(sprop + tscore*se,2)\n",
@@ -808,13 +807,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 419,
+   "execution_count": 26,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "0b8c4e925e9b4ec3b406f4cafe633a85",
+       "model_id": "67d934e205cc4006b8aea89d963abd48",
        "version_major": 2,
        "version_minor": 0
       },
@@ -828,12 +827,12 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "52851cebf33f4ce88e1daad37de0d676",
+       "model_id": "30757e28dac24c398bc005725e7197e9",
        "version_major": 2,
        "version_minor": 0
       },
       "text/plain": [
-       "Output(outputs=({'output_type': 'display_data', 'data': {'text/plain': '<Figure size 640x480 with 1 Axes>', 'i…"
+       "Output()"
       ]
      },
      "metadata": {},