Reference no: EM133620268

Foundation Skills in Data Analysis

Learning Outcome 1: Manipulate and summarise data that accurately represents real world problems
Learning Outcome 2: Interpret and appraise statistical output to assist in real-world decision making
Learning Outcome 3: Critical thinking: evaluating information using critical and analytical thinking and judgment

Description

Purpose
This assignment task is aligned to the learning outcomes and skills GLO4 & ULO2, ULO3 required in applying the ideas and concepts introduced in Modules 1 and 2 to undertake Descriptive Measures, Probability Theory, and Inferences to transform raw data into information and knowledge using appropriate data analysis techniques. You will require to prepare a business report that analyses a given dataset and interprets the results to demonstrate understanding of the specific business problems posed, and that offers conclusions and recommendations that address these problems. You will use plain language to report pertinent findings in a fair, neutral and transparent manner, and present compelling evidence to support their findings. By completing this task, you will encounter with some examples of the application of data analysis within an organisation, test your understanding of the material presented in the relevant topics, and your ability to analyse data, and effectively communicate your results in a language best suited to target audience/business professionals.

Context/Scenario

The Australian Electric Vehicle Council wants you to process and analyse a data set based on available information on a sample of electric vehicles (EVs) and then answer several questions. The questions you need to answer are contained in the following memorandum. Assume that your readers do not have an analytics background, so it's important that you utilise "plain, easy to understand language" in your answers. If you believe you need to include any technical terms, then you must explain these in a clear and succinct manner using layman's terms.

Q1. Summaries of key variables of interest
Can you please provide me with separate summaries of the following variables, just by themselves? In other words, please investigate each variable individually without reference to any other variable in the dataset.
"FastCharge_KmH" - charging speed in kilometers per hour.
"BodyStyle" - style/size of the car.

Q2. Exploring relationships between two variables
I would like to know if there is a link between the average consumption of the battery of EVs ("Efficiency_WhKm") and their price ("Price"). I suspect that the more efficient, the higher the price will be, but I'd like to know if this is actually the case. Therefore, I'd like you to establish from your sample data if there is any relationship between these two variables.
I'm also interested to establish if there is a relationship between the drive type ("PowerTrain") and the style ("BodyStyle").
Further, it would be helpful if we knew if the style ("BodyStyle") has any relationship with how efficient an EV runs ("Efficiency_WhKm").

Q3. Estimating EV measures
I would like you to estimate the overall price of EVs ("Price").
I'm also interested to know if you can estimate the proportion of all EVs which are perceived as smaller
cars (i.e., Hatchbacks or Liftbacks) ("BodyStyle").

Q4. Claims about EVs
I read somewhere that acceleration (i.e., 0 to 100 km/h) for EVs ("AccelSec") was 7 seconds. I think that acceleration is lower than this figure for EVs (they can go from 0 to 100 km/h in less than 7 seconds). Is there any evidence to suggest that this is the case?
Another claim concerned market segments ("Segment"). The claim was that less than 30% of EVs belonged to Segment C. Can you also check this claim against your survey data?

Q5. Appropriate sample size
Finally, I am concerned that the sample of 92 EVs is too small to provide accurate results as this seems hardly enough data. If we ever decide to repeat the analysis, I would like to be able to:
calculate approximately the average range ("Range_Km") to within 10 kilometers.
Therefore, how many EVs would we need to include in the next analysis to satisfy this requirement?

I look forward to your response,
Jane

Specific Requirements
Before attempting the assignment, make sure you have prepared yourself well. At a minimum, please read the relevant sections of the prescribed textbook and review the materials provided in Modules 1 and 2.
Report Requirements
Your report must have a cover sheet containing your personal particulars and the Unit details, an executive summary, introduction and conclusion.
Your report should be no longer than 4 pages excluding cover sheet, and there is no need to, any visualisations (i.e., Charts and Tables), or Appendices in the Report.
The Charts/Graphics and Tables you create are only to be placed in the Data Analysis file (i.e. the Excel spreadsheet) and not reproduced in the report.
Your report is meant to be a stand-alone document. That is, it should be able to be read without looking at the data analysis. To this end, do not refer to the visualisations as "as you can see from Figure 1 etc". You need to interpret your data analysis visualisations for Jane in the report.
Suggested Microsoft Word formatting for the report: Single-line spacing; no smaller that 10- point font; page margins approx. 25mm, and good use of white space.
Set out the report in the same order as in the originating Memorandum from Jane, with each section (question) clearly marked.
Use plain language and keep your explanations succinct. Avoid the use of technical or statistical jargon. As a guide to the meaning of "Plain Language", imagine you are explaining your findings to a person without any statistical training (e.g., someone who has not studied this unit). What type of language would you use in that case?
Marks will be lost if you use unexplained technical terms, irrelevant material, or have poor presentation/ organisation.
All Microsoft Excel output associated with each question in the Memorandum is to be placed in the corresponding tab in the file MIS770A2_yourstudentid.xlsx
Data Analysis Instructions/Guidelines
In order to prepare a reply to Jane's memorandum, you will need to examine and analyse the dataset
MIS770A2_yourstudentid.xlsx thoroughly.
Jane has asked a number of questions and your data analysis output (i.e., your charts/tables/graphs) should be structured such that you answer each question on the separate tab/worksheet provided in your Excel document. There are also five extra tabs in MIS770A2_yourstudentid.xlsx and you should use the various templates contained in these tabs in your "Confidence Interval", "Hypothesis" and "Sample Size" 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 any given question:

Summary Measures (e.g., descriptive statistics, Inc. outlier detection, percentiles).
Comparative Summary Measures (i.e., descriptive statistics, outlier detection and percentiles for multiple values of a variable).

Suitable tables (such as a frequency distribution) and charts or graphics (such as histograms, box plots, pie charts, bar/column charts, polygons) that will illustrate more clearly, other important features of a variable.
Scatter Diagrams (used to visually establish if there is a relationship between two numeric variables).
Cross Tabulations (sometimes called contingency tables), used to establish the relationships (dependencies) between two variables (see Additional Materials under Topic 2 - Creating Cross Tabulations in Excel using Pivot Tables).
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 were asked for an estimate. You should use the relevant Excel templates provided in the dataset and copy them to the applicable question tab.
Hypothesis Tests. You can assume that a 5% level of significance is appropriate. We use hypothesis tests when we are testing a claim, a theory or a standard. You should use the relevant Excel templates provided in the dataset and copy them to the applicable question tab.
Sample size calculation: You can assume that a 95% confidence level is appropriate. You should 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 report.
Note: There is an appendix at the end of each chapter of the prescribed textbook which describes the basic Excel steps associated with that topic. Chapters 1 to 9 are applicable for this assessment.

Attachment:- Foundation Skills in Data Analysis.rar