MIS770-Foundation Skills in Data Analytics - Data Analysis within an Organization - Information System and Business Analytics Report Writing Assignment Help

Assignment Task:

The purpose of this assignment is to investigate a dataset utilizing the knowledge learned in Modules One and Two. This will enable conclusions to be drawn that ultimately assist in decision making. 

The assignment requires you to analyze a given dataset, interpret the results, and then draw conclusions such that you are able to reply to specific questions being asked of you in the form of a report. (These questions are asked in the following memorandum). 

The aims of the assignment are to: 

• provide you with some examples of the application of data analysis within an organization 

• test your understanding of the material in the relevant topics 

• test your ability to analyze and interpret your results 

• test your ability to effectively communicate the results of your analysis to others 

Before tackling the assignment, make sure you have prepared yourself well.

Data Analysis Instructions/Guidelines 

In order to prepare a reply to Paul’s memorandum, you will need to examine and analyze the dataset KingfisherbayData.xlsx thoroughly. 

Paul has asked a number of questions and your Data Analysis output (i.e. your charts/tables/graphs) should be structured such that each question is answered on the separate tab/worksheet provided in your Excel document. There are also extra tabs in KingfisherbayData.xlsx called CI, SampleSize, and HT and you should use the various templates contained in these tabs in your “CI_Mean/CI_Proportion”, “samples” and “HT_Mean/HT_Proportion” answers. 

In order to effectively answer the questions, your Data Analysis output needs to be appropriate. Accordingly, you’ll need to establish which of the following techniques are applicable for each question: 

• Numerical Summary Measures (Inc. Outlier detection) 

• Suitable tables and charts or graphics (Module One) that will illustrate more clearly, other important features of a variable Numerical variable can be converted to categorical variables 

• Comparative Summary Measures, Scatter diagrams, Correlation analysis and Cross Tabulations (sometimes called Contingency Tables), used to establish the relationships (dependencies) between two variables 

• Confidence Intervals: You can assume that a 95% confidence level is appropriate. We use Confidence Intervals when we have no idea about the population parameter we are investigating. Additionally, we would use Confidence Intervals if we are asked to provide an estimate of a population parameter. 

• We Use Hypothesis Tests when we are testing a Claim, a Theory or a Standard. Use 5% significance in any hypothesis tests you perform, and provide a summary of your conclusions. Where appropriate, make comparisons with other levels of significance (2%, 1%). 

• Sample size calculation: You can assume that a 95% confidence level is appropriate. You may include comparisons for 90% and 99% and a recommendation for the appropriate sample size. 

• To answer some questions you may need to make certain assumptions about the data set we are using. Mention these in your data analysis, where relevant. There is no need to mention this in the memo. 

Graduate Learning Outcome (GLO) Unit Learning Outcome (ULO):

•  Interpret and appraise statistical output to assist in real?world decision making 

• Manipulate and summarise data that accurately represents the real world

Critical thinking:

evaluating information using critical and analytical thinking and judgment

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