MHB TEST OF HYPOTHESIS INVOLVING THE POPULATION MEAN 𝝁 WHEN THE VARIANCE IS UNKNOWN

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The discussion centers on a company's ongoing attrition issues among customer service representatives, with a proposed solution contingent on achieving a monthly attrition rate of at least 10. A dataset of attrition rates over 20 months has been collected, and it is assumed to follow a normal distribution. The president's plan includes implementing salary adjustments, enhanced compensation packages, and professional development initiatives if the threshold is met. The conversation also questions the clarity of the hypothesis test being conducted. Overall, the focus is on analyzing the attrition data to inform potential changes in employee compensation and support.

What is the null and alternative hypothesis???

  • H_0 = u = 9 ; H_\alpha = u > 9

  • H_O = u \geq 10 ; H_\alpha = u < 10


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bunnypatotie
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1. You are working in a company facing attrition problems of the customer service representatives for the past five years. The company president proposed that if the attrition rate is at least 10 per month, then the salary scale, compensation package, and professional development programs for employees will be initiated. For this purpose, attrition rates for 20 months selected at random were considered and listed below. Assume that the data follow normality.
8 9 12 10 6 15 14 11 8 10 10 12 15 11 12 7 14 14 7 11
 
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bunnypatotie said:
1. You are working in a company facing attrition problems of the customer service representatives for the past five years. The company president proposed that if the attrition rate is at least 10 per month, then the salary scale, compensation package, and professional development programs for employees will be initiated. For this purpose, attrition rates for 20 months selected at random were considered and listed below. Assume that the data follow normality.
8 9 12 10 6 15 14 11 8 10 10 12 15 11 12 7 14 14 7 11
Why is this a poll? There should be an unambiguous answer. That's why you are asking, isn't it?

Please show us what you've been able to do with this so far.

-Dan
 
The standard _A " operator" maps a Null Hypothesis Ho into a decision set { Do not reject:=1 and reject :=0}. In this sense ( HA)_A , makes no sense. Since H0, HA aren't exhaustive, can we find an alternative operator, _A' , so that ( H_A)_A' makes sense? Isn't Pearson Neyman related to this? Hope I'm making sense. Edit: I was motivated by a superficial similarity of the idea with double transposition of matrices M, with ## (M^{T})^{T}=M##, and just wanted to see if it made sense to talk...

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