700007 Statistics for Business Assignment | Case Study Help

 

Assignment Details:

• Referencing Styles: Harvard
• of Words: 3000
• Course Code: 700007
• Course Title: Statistics for Business
• University: Western Sydney University
• Country: AU

 

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Question:

Datasets are common in everyday life. Here is a shortlist of kinds of everyday information that are, in fact, data:

• Sales reports (and other internal records).
• Market survey results.
• Production quality measures (and other manufacturing records).
• Human resource productivity records.
• Product price and quantity sold (and other sales data).

Think about it. Probably much of what you do daily depends indirectly on data.

Looking at data and asking some questions about them may reveal surprises: You may find out that the quality of the data is not as high as you had thought, or you may find out the opposite and be reassured.

In this task, you are required to:

 

1: Select a Dataset

Find a dataset on the Internet (you can choose any field, e.g. sports, finance, manufacturing, weather, health and wellness, technology, sports and leisure). Save this data in an Excel spreadsheet. You must be able to quote the source of your dataset (e.g., provide the internet link the data was downloaded from in your typed word document). The data you choose must contain TWO or more variables and more than twenty individuals (people or objects described by a set of data).

 

2: Analyse Data.

Refer to your dataset and answer the following questions.

1. Formulate a research question that can be answered using your dataset?
2. Define the target population and describe the sample in the context of your data.
3. What are the two main variables in your dataset? Classify these variables either as nominal, ordinal, discrete or continuous?
4. What kind of issues did you face in this data collection?
5. Summarise your data using appropriate graphical displays and numerical summaries(covered in modules 1 and 2).

 

3: Write Report

Cut and paste all relevant computer output with your analysis. Be sure to include both computer output and your discussion of that output in every case. As you discuss each analysis, be sure to interpret what you are finding in the context of your data situation.