Small edits

StephenElston · StephenElston · commit a7543a00d18e · 2018-07-08T18:20:15.000-07:00
diff --git a/Module04/04-05-Sampling Distributions.ipynb b/Module04/04-05-Sampling Distributions.ipynb
@@ -465,7 +465,7 @@
     "\n",
     "\\begin{equation}\\mu_{x} = \\mu_{\\bar{x}}\\end{equation}\n",
     "\n",
-    "In this case, the full population mean is unknown (unless we weigh every bag in the world!), but we do have the mean of the full set of sample observations we collected (**x&#772;**), and if we check the values generated by Python for the sample mean and the sampling mean, they're more or less the same: around 3.2.\n",
+    "In this case, the full population mean is unknown (unless we weigh every bag in the world!), but we do have the mean of the full set of sample observations we collected (**x&#772;**), and if we check the values generated by R for the sample mean and the sampling mean, they're more or less the same: around 3.2.\n",
     "\n",
     "To find the standard deviation of the sample mean, which is technically the *standard error*, we can use this formula:\n",
     "\n",
@@ -512,7 +512,7 @@
     "\\begin{equation}\\lim_{n \\to \\infty} \\frac{\\sigma}{\\sqrt{n}} = 0\\end{equation}\n",
     "\n",
     "\n",
-    "In Python, you can use the *scipy.stats.**norm.interval*** function to calculate a confidence interval for a normal distribution. Run the following code to recreate the sampling distribution for bag searches with the same parameters, and display the 95% confidence interval for the mean (again, this may take some time to run):"
+    "Run the following code to recreate the sampling distribution for bag searches with the same parameters, and display the 95% confidence interval for the mean (again, this may take some time to run):"
    ]
   },
   {
