Reference no: EM133688333 , Length: word count:1500

Descriptive Analytics and Visualisation

Learning Outcome 1: Apply quantitative reasoning skills to solve complex problems.

Learning Outcome 2: Plan, monitor, and evaluate own learning as a data analyst.

Learning Outcome 3: Deduce clear and unambiguous solutions in a form that they useful for decision-making and research purposes and for communication to the wider public.

Assessment Task - Data Analysis & Report

The first task is to report on the plan to deliver the assessment on time. The second task is to analyse the given dataset and draw conclusions. Finally, the third task is to convey the findings and conclusions in a written report to an expert in Business Analytics.

Specific Requirements
You are the lead modeller at Methods9, an analytic startup that assists businesses with analytic solutions. Senior partner - Cindy Varanasi (MBA and MSc in Data Science) has asked you to lead the modelling component for several recent projects she has secured. The minutes of the meeting are below. You must review and complete the modelling activities per the document.

Detailed Action Items

Detailed Action Items Who:
{you} What:
Build a model to estimate Sales of a GroceryPlus store.
Prior research shows that advertising expenditure is a significant predictor of Bike Sales at BikeMart. Cindy believes that the relationship between advertising expenditure and sales is also influenced by the number of promotional campaigns. Test Cindy's assumption by modelling the interaction between the predictors and the target variable.
Build a model to predict which customers will buy headphones after purchasing a mobile phone at Gadget4U.
Cindy has already done the initial analysis for a model to predict the likelihood of a store manager resigning at CosmeticChain. She has narrowed down the key predictors to the manager's age, experience, and gender.
Develop a model to ascertain the likelihood of a store manager resigning.
Cindy is specifically interested in understanding the probability of a manager in mid-thirties with varying levels of managerial experience (i.e. 2- 16 years) and across both genders resigning. Accordingly, visualise the predicted probability of resigning with the values and attributes described earlier.
Develop a time-series model to forecast the production volumes of pale ale for the next four fiscal quarters.
Produce a written technical report detailing all conclusion and analysis activities. The report should be comprehensive (describe all critical analyses and conclusions). The analysis should drive the conclusions and the recommendations to the management team (a clear link/alignment).
Next meeting Thursday 5 May 2024
Explanatory Notes

To accomplish allocated tasks, you must thoroughly examine and analyse the dataset. Below are some guidelines to follow:

Task 1. - Model building
It is IMPORTANT to follow an appropriate model-building process. Include all steps of the model- building activities (especially all relevant pre- and post-model diagnostics) in your analysis. Include as many Excel worksheets (tabs) as you require to demonstrate different iterations of your regression model (i.e., 1.2.a., 1.2.b., 1.2.c. etc.). Please note all reasonable/realistic assumptions about the parameters next to the analysis.
The technical report should explain why the model might have undergone several iterations (your modelling approach). Also, provide a detailed interpretation of ALL elements of the final model/regression output and state the conclusions.

Task 2. - Interaction effect
Develop a new regression model using ONLY the factors discussed in the team meeting (Item 2) to accomplish this task. Is there evidence that the interaction term makes a significant contribution to the model? Please note all reasonable/realistic assumptions about the parameters next to the analysis.
The technical report should clearly explain the role of each variable included in the model and use visualisation to illustrate the interaction effect (if any or lack of it). Finally, provide managerial recommendations based on the results of the analysis.

Task 3 - Model building
You should start building the predictive model by including ONLY the variables listed in the team meeting (Item 3). You must make reasonable/realistic/practical assumptions about the parameters mentioned in Task 3.

Task 4.1 - Model building
You should start building the predictive model by including ONLY the variables listed in the team meeting (Item 4.1). You must make reasonable/realistic/practical assumptions about the parameters mentioned in Task 4.1.
The technical report should provide a detailed interpretation of ALL elements of the model/logistic regression output and state the conclusions.

Task 4.2 - Visualising and interpreting predicted probabilities
The technical report must include the predicted probability visualisation and the practical recommendations. These recommendations should broadly answer the following question:

Task 5. - Forecasting Production
Past quarterly production volumes are in the Excel file. The task is to develop a suitable model to forecast Quarterly production volumes for the next four quarters.
In the technical report, explain the reason for selecting the forecasting method to forecast future Sales. The report also must include a detailed interpretation of the final model (e.g. a practical interpretation of the time-series model...etc.)

Task 6. - Technical report
The technical report must be as comprehensive. All analysis and final outputs must be described/interpreted in detail.
Remember, the report audience is an expert in analytics and expects a very high standard of work. High standards mean quality content (demonstrated attention to detail) and an aesthetically appealing report.

The report should include an introduction as well as a conclusion. The introduction begins with the purpose(s) of the analysis and concludes by explaining the report's structure (i.e., subsequent sections). The conclusion should highlight the essential findings and explain the main limitations.

The assignment consists of three documents:
Planning and execution tables
Analysis
Technical Report

Assignment Planning and Execution Tables
The planning and execution details should be submitted in the appropriate tables provided. The tables should be in dot points. Before filling in the tables, students are strongly encouraged to watch the pre-recorded workshop called 'How to plan an assignment and turn the plan into action?' by a Language and Learning Adviser.
Note: Give the assignment planning and execution file the following name
A2_Planning_YourStudentID.docx

Analysis
The analysis should be submitted in the appropriate worksheets in the Excel file. Each step in the model buildings should be included in a separate tab (e.g. 1.1., 1.2. ...). Add more worksheets if necessary.
Before submitting the analysis, ensure it is logically organised and any incorrect or unnecessary output has been removed. Marks will be deducted for poor presentation or disorganised/incorrect results. The worksheets should follow the order in which tasks are allocated in the minutes of the team meeting document.
Note: Give the Excel file the following name A2_YourStudentID.xlsx (use a short file name while you are doing the analysis).
Technical Report
The technical report consists of three sections: Introduction, Main Body, and Conclusion. The report should be approximately 1,500 words.
Use proper headings (i.e., 2., 2.1., 2.2., ...) and titles in the main body of the report. Use sub-headings where necessary.
Visualisations / statistical output expected in the report are:
Interaction effect plots
Predicted probability plots.
Ensure these outputs are visually appealing, have consistent formatting style and proper titles (title, axes titles etc.), and are numbered correctly. Where necessary, refer to these outputs in the main body of the report.