Table of Contents

• 1: Do those shoes fit?
• 2: The 3-day work week
• 3: The day is saved ... or not
• 4: What you observe vs. expect
• 5: The Chi-Square Equation
• 6: Give it a try
• Detour
• 7: What's a p-value?
• 8: What's a Lookup Table?
• 9: What's a "df"?
• Completing the Chi-Square Test
• 10: Interpreting a chi-square
• 11: The answer, finally
• 12: Summary
• 13: What about 90% M/F sickdays?
• 14: What about 50% M/F sickdays?
• Chi-Square in Biology
• 15: Testing for a dihybrid ratio
• 16: Review and Words of Wisdom

Play it again, Sam!

What if 90% of the sickdays were on M/F?

	observed ("o")	expected ("e")	(o-e)	(o-e)2	(o-e)2/e
Monday/ Friday
Midweek
Total	100	100
Check

 

Ok, so now take that calculated chi square value and compare it to the critical chi square on the table.

 

You should have gotten a chi-square-calc of 104.166, compared to the chi-square-crit of 3.84. So, the chi-square-calc is much greater than the chi-square-crit -- so the data does not fit the model and you reject your null hypothesis. In other words, the engineer's random sickday model does NOT hold up.