<jupyter_start><jupyter_text>**Download** (right-click, save target as ...) this page as a jupyterlab notebook [Lab17-TH](http://54.243.252.9/engr-1330-webroot/8-Labs/Lab17/Lab17-TH.ipynb)___  Laboratory 17: "Reject it or Fail!" or a Lab on "Hypothesis Testing" LAST NAME, FIRST NAMER00000000ENGR 1330 Laboratory 17 - In-Lab ___<jupyter_code># Preamble script block to identify host, user, and kernel
import sys
! hostname
! whoami
print(sys.executable)
print(sys.version)
print(sys.version_info)<jupyter_output>atomickitty
sensei
/opt/jupyterhub/bin/python3
3.8.10 (default, Sep 28 2021, 16:10:42) 
[GCC 9.3.0]
sys.version_info(major=3, minor=8, micro=10, releaselevel='final', serial=0)<jupyter_text>ExerciseThe data below are the impact impact strength of packaging materials in foot-pounds of two branded boxes. Produce a histogram of the two series, and determine if there is evidence of a difference in mean strength between the two brands.  Use an appropriate hypothesis test to support your assertion at a level of significance of $\alpha = 0.10$. | Amazon Branded Boxes | Walmart Branded Boxes ||:---|:---|| 1.25| 0.89|| 1.16| 1.01|| 1.33| 0.97|| 1.15| 0.95|| 1.23| 0.94|| 1.20| 1.02|| 1.32| 0.98|| 1.28| 1.06|| 1.21| 0.98|| 1.14| 0.94|| 1.17| 1.02|| 1.34| 0.98| Deliverables:- Working scripts that produce perform the necessary tests- Narrative (or print blocks) that supply answer questions- CCMR citations for sources  (URL for outside sources is OK) Hints:- A suggested set of code cells is listed below- Add/remove cells as needed for your solution<jupyter_code># define lists and make into dataframe 
# describe lists/dataframe 
# histograms
# hypothesis tests are means same? (test for nomrality then t-test if normal; mann-whitney if non-normal)
# interpret findings (could be a markdown cell, or embed into code)<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.11397849462365592, 'fraction_numerical': 0.07311827956989247, 'mean_word_length': 4.708588957055214, 'pattern_counts': {'":': 0, '<': 8, '<?xml version=': 0, '>': 8, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 1, 'substrings_counts': 1, 'word_list_counts': {'cursed_substrings.json': 1, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
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