Design Solutions for the to Improve Student Participation Rates – Accounting & Finance Assignment Help

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Design Solutions for the to Improve Student Participation Rates

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Imagine you work for a consulting firm called FixIt! Consulting. Your company has a reputation for on-time and comprehensive problem-solving solutions that integrates different evidence sources, namely statistical information with insights from literature and ethical decision-making processes.

FixIt! Consulting recently started a consulting project for the Career Accelerator team at UNSW Business School. The aim of the project is to design solutions for the Career Accelerator team to improve student participation rates in the Microsoft Excel Certification. Please read the Project Brief (pages 2-5) for more information about the project background, business problem and tasks.

As a member of the FixIt! Consulting team (also as a UNSW Business School student who is a stakeholder of this issue), the overarching issue you need to address is to analyses the problem and statistics to understand the issue. 

1. FixIt! ‘statistical toolbox’

This section of the report is approximately 700 words (guide only, not a word limit). 

After considering the descriptive statistics, the career accelerator team is interested inidentifying the factors which affect the chance of participation in the certification exam(i.e. variable Participate_Cert_Exam). They are more interested in identifying behaviouraltraits which could affect these probabilities. These include (for both tests):

  • Number of attempts (A1a_Num_of_Attempts, A1b_Num_of_Attempts);
  • Score of the attempts (A1a_Score_1st_Attempt, A1a_Score_Best_Attempt,A1b_Score_1st_Attempt, A1b_Score_Best_Attempt);
  • Completion of attempts, hours before deadline (A1a_1st_Attempt_Before_Deadline, A1a_last_Attempt_Before_Deadline, A1b_1st_Attempt_Before_Deadline, A1b_last_Attempt_Before_Deadline)

To understand whether these factors play an important role, broadly speaking, you will need to (i) formulate a multiple linear regression model (refer to week explaining your choice of variables) and (ii) explain whether the relationship is statistically and/or economically significant. As aforementioned, the use of a multiple linear regression (week 4-8), confidence intervals (week 7) and hypothesis testing would help you address this and thus provide evidence for your arguments. NOTE: You will need to know how to use EXCEL data analysis tool and its regression analysis tool. Show all the data from your excel calculations, including Multiple R, R square, Adjested R square, Standard Error, Observations, AVOVA, Coefficients, t stat, P-value and more…

More specifically, you need to consider relevant factors to understand the relationship between Participate_Cert_Exam and the behavioural variables incorporating any control variables which are relevant.
In addition to interpreting the results of your analysis, you will also need to draw to attention issues of causality (Week 7) and confoundment (Week 4), which can impact the conclusions from the analysis.

As a part of this assessment, assumptions and limitations need to be explicitly identified. e.g. What variable would you want to have in an ideal situation to measure different variables in this analysis? Do you have this variable in the dataset? If not (which is often the Case in practice: we often do not have all the ideal data/variables that we need to perform an analysis, and have to rely on the data available to us), what variable in the dataset do you have to use as a performance/ability measure? What are the assumptions and limitations of using this variable?]

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