README.md

# Introduction to Sensors and Data Analysis
## ME 3263 Fall 2020

[ME 3263 Lab Report Rubric](./ME3263-grading_rubric.pdf)

Labs 0 and 1 have a 3-page limit and 2-figure limit. Labs 2-6 have a 5-page
limit and 4-figure limit. You can add additional pages and figures in an
Appendix. The Appendix will not be graded, but you can use it to refer
to data, methods, or diagrams that are relevant. 

The report is scored 0-100. Over 70 is passing. Late submissions receive 10
point penalty per day. 

# Repository for laboratory notebooks
*To access notebooks and interactive lab material, sign into github.uconn.edu,
then follow the link to the class server.*

# [ugmelab.uconn.edu](https://ugmelab.uconn.edu) 
# ME3263 Introduction to Sensors and Data Analysis (Fall 2018)

# Lab #0 - Introduction to the Student t-test

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
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. 

# Labs 1-6 coming soon
check [HuskyCT](lms.uconn.edu), [Piazza](piazza.com/uconn/fall2020/me3263/home), and
[ME3263 repo](https://github.uconn.edu/rcc02007/me3263_labs) for updates!