- In the last year, based on quality inspections, the reject (failure) rate of solar panels was 15.2 per 1,000 panels. What is the probability that a randomly selected panel will be rejected?

- If 30 panels are randomly selected, find the probability that two or more will be rejected (round results to three decimals)
- If 30 panels are randomly selected, find the probability that fewer than two will be rejected (round results to three decimals)
- If 30 panels are randomly selected, what is the probability that none will be rejected (round results to three decimals)
- If 100 panels are randomly selected, what is the probability that none will be rejected (round results to three decimals)
- What does this tell you about using 30 vs. 100 as a random sample size?

- Price information on solar panels is stored on a server by SKU (Stock Keeping Unit). The sales representative, after receiving an order, scans the SKU for each component of the solar panel purchase. Audits are conducted routinely to ensure that the scanned price matches the posted price and the advertised-special price. The table below summarizes the results for a scan errors of each type items from a sample of 956 units

- What is the probability that a randomly selected posted price scan item is incorrect?
- What is the probability that a randomly selected advertised-price item scan is correct?
- What is the probability that any randomly selected scan is incorrect?
- What is the probability that any randomly selected scan is either over priced
an advertised item?*or* - What is the probability that any randomly selected scan is a posted-priced item
is under priced?*and* - What is the probability that any randomly selected scan is either over priced
under priced?*or*

** Scanner Errors**

Item type Over Priced Under Priced Priced Correctly

Posted-Price Item 18 32 450

Advertised Item 6 20 430

- If you were responsible for operational quality, what changes might you make to the SKU entry process?

__Homework Assignment 5__

- One of the Sales Manager at Solar Solutions has been asked to analyze the relationship between price/unit and demand. She is to make a presentation to the Executive Team that includes the following:
- A scatter plot of the data over the past 10 years. (See
*Solar Solutions*in Course Materials.) - A calculation of the correlation coefficient
coefficient of determination.*and* - A description of both coefficients' characteristics (weak/strong, positive/negative, x/y relationship etc.).
- A recommendation of actions the Executive Team might take based on this data.
- One of the executives, the sales manager, raises the issue of correlation/causation. Can the sales manager demonstrate that one variable causes a change in the other? What evidence would she need to establish causality? If she does not have sufficient evidence, how might she design an "experiment" to support it?

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