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Part Two – Data Characteristics Read Lecture One 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 si

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Part Two – Data Characteristics

Read Lecture One 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 Lecture Two on describing data sets and view The Role of Data & Analytics Today 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 Lecture Three 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? 

 
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