Reference no: EM133886161

Descriptive Analytics and Visualisation

Assessment Task - Data Analysis & Report

Tasks
Assignment two is an individual assignment with three tasks. 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.

Case Study

The K-Olive is a regional olive grower with a 30-year history. Although its operations are limited to the Wimmera and Grampians regions, the company has been financially successful. The K-Olive product (400g Pitted Olive Jar) is sold directly to customers (Public, Grocery Chains, Shops, and Restaurants) or indirectly through an external distribution network.

Despite its successful operations and solid financial turnovers in the last two years, K-Olive forecasts a shift in consumer demand. Now more than ever, K-Olive management wishes to ensure a strong relationship with its diverse customer base.

The management team wants to understand K-Olive's customers' characteristics and repurchase intentions. In addition, they would like a formal procedure for forecasting demand for their olive product. The formal forecasting would help K-Olive with demand planning and production scheduling.

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

Discussion items:
Modelling Quantity Ordered.
Testing the effect of Brand Image and Quality on Order Quantity.
Modelling the likelihood of recommending K-Olive to others.
Forecasting demand in the upcoming four quarters.
Producing a technical report.

Detailed Action Items

What:
Build a model to estimate the order quantity.
Prior research shows that the perception of product quality is a significant predictor of the quantity ordered, and this relationship can vary depending on the brand image. Specifically, customers often associate the brand image with product quality. Cindy believes the relationship between quality and quantity ordered should be stronger for those with more favourable brand perceptions. Test Cindy's assumption by modelling the interaction between the predictors and the target variable.
Cindy has already done the initial analysis for a model to predict the likelihood of recommending K-Olive to others. She has narrowed the key predictors to "Distribution Channel, Quality and Brand Image".
Develop a model to ascertain the likelihood of recommending K-Olive to others.
Cindy is specifically interested in understanding how changes in perceptions of quality (scores from 1 to 10), a positive brand image (a score of 10) and the distribution channel (i.e., those purchasing directly and through a distribution network) affect the predicted probabilities of recommending K- Olive products. Accordingly, visualise the predicted probabilities of recommending K-Olive to others using the values and attributes described earlier.
Develop a time-series model to forecast demand for K-Olive products for the next four fiscal quarters.
Produce a written technical report detailing all conclusions and analysis activities. The report should be comprehensive (describe all critical analyses and findings). The analysis should drive the conclusions and the recommendations (a clear link/alignment).

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. Get professional assignment help service now!
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 3.1 - Model building
You should start building the predictive model by including ONLY the variables listed in the team meeting (Item 3.1). You must make reasonable/realistic/practical assumptions about the parameters mentioned in Task 3.1.
The technical report should provide a detailed interpretation of ALL elements of the model/logistic regression output and state the conclusions.

Task 3.2 - 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:

"How changes in perceptions of quality (scores from 1 to 10), a positive brand image (a score 10) and the distribution channel (i.e., those purchasing directly and through a distribution network) affect the predicted probabilities of recommending K-Olive."

Task 4. - 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. The report also must include a detailed interpretation of the final model (e.g. a practical interpretation of the time-series model...etc.)
Task 5. - Technical report
The technical report must be 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.
Note: The use of technical terms is encouraged and expected in this assignment.
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.