# Discussion post – prof double r

Note that all of your discussions for this course are due on Day 5. Although the initial post is due on Day 5, you are encouraged to start working on it early, as it is a four-part discussion that should be completed in sequential order.

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Part One – Analysis Toolkpak

Add the “Analysis Toolpak” to Excel. Be sure you are you able to copy, sort, and find averages and sums in Excel. Use the Load the Analysis ToolPak (Links to an external site.)Links to an external site. article for information on how to load this in Excel. (This should be completed on Day 1.)

Part Two – Data Characteristics

Read  on descriptive data and review the Employee Data . Be sure to familiarize yourself with the different variables shown on the Data tab. In this course, we will be using the Employee Data and statistical tools to answer a single research question: In our BUS308 company, are the males and females paid equally for equal work?

Lecture One discusses different ways data values can be classified. In our data set for the equal pay for equal work assignment, students in the past have correctly identify the variable gender (coded M and F for male and female respectively) as nominal level data, but they often see gender1 (coded 0 and 1 for male and female respectively) as interval or ratio level data. Why? What could cause this wrong classification? What data do you use in your personal or professional lives that might suffer from not being correctly labeled/understood? (This should be started on Day 1.)

Part Three –Descriptive Statistics

Read  on describing data sets and view The Role of Data & Analytics Today  (Links to an external site.)Links to an external site.video. Lecture Two discusses several different ways of summarizing a data set–central location, variability, etc. Often, business reports provide a mean or average value for some measure (such as average number of defects per production run). Why is the average alone not enough information to make informed judgements about the result? What other descriptive statistic should be included? Why? Can you illustrate this with an example from your personal or professional lives? (This should be started on Day 3.)

Part Four – Probability

Read  on probability. Lecture Three introduces the idea of probability—a measure of how likely it is to get a particular outcome. Looking at outcomes as resulting from probabilities (somewhat random outcomes/selections) rather than fixed constants often changes the way we see things. How does considering the salary outcomes in our sample the result of a probabilistic sample rather than a completely accurate and precise reflection of the population change how we interpret the sample statistic outcomes? What results in your personal or professional lives could be viewed this way? What differences would this cause? Why? (This should be completed by Day 5.)

Your responses should be separated in the initial post, addressing each part individually, similar to what you see here.

Guided Response: Review several of your classmates’ posts, and respond to at least two of your peers by 11:59 p.m. on Day 7 of the week. You are encouraged to post your required replies early during the week to promote more meaningful interactive discourse in the discussion.

In your responses, point out additional insight to why it is important to know and understand data types. Provide an example of descriptive statistics that you could use to answer business-related questions. Respond to at least one classmate that selected a different type of probability (empirical, theoretical, or subjective), and provide an example of how you could use it in your personal or professional life.

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