From 0c2a5736d603f11e3a0e27381eddbe844b8c40a2 Mon Sep 17 00:00:00 2001 From: "Ryan C. Cooper" Date: Tue, 28 Aug 2018 10:40:28 -0400 Subject: [PATCH] added ipynb link --- README.md | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/README.md b/README.md index 19026bd..57847f9 100644 --- a/README.md +++ b/README.md @@ -1,5 +1,7 @@ # Lab 0 - Statistics and the Student t-test +[Lab 0 notebook](./ME3263_lab-00.ipynb) + We use statistics to draw conclusions from limited data. No measurement is exact. Every measurement you make has two types of uncertainties, *systematic* and *random*. *Systematic* uncertainties come from faults in your assumptions or equipment. *Random* uncertainties are associated with unpredictable (or unforeseen at the time) experimental conditions. These can also be due to simplifications of your @@ -7,4 +9,4 @@ model. Here are some examples for caliper measurements: In theory, all uncertainies could be accounted for by factoring in all physics in your readings. In reality, there is a diminishing return on investment -for this practice. So we use some statistical insights to draw conclusions. \ No newline at end of file +for this practice. So we use some statistical insights to draw conclusions.