1) Build a multiple regression model to estimate the order quantity. 2) In a separate project, Peter found that the perception of product quality is a significant predictor of the quantity ordered. In line with his findings, prior research shows that the strength of this relationship may vary according to brand image. That is, customers tend to associate the brand image with product quality. Therefore, Peter believes that the relationship between quality and quantity ordered should be stronger for those with more favourable brand perceptions. Model the interaction between image, quality and quantity to test Peter's assumption and comment on whether there is sufficient evidence to conclude that the interaction term is statistically significant in the model. 3) Build a logistic regression model to predict the likelihood of recommending B-Hive products to others. 4) Peter has completed the first step for this task. He has narrowed down the key predictors of the likelihood of recommending B-Hive products to "Distribution Channel, Quality and Brand Image". a) Continue Peter's work and develop a logistic regression model to predict the "likelihood of recommending B-Hive products". b) Create a plot that shows how the 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 or through a distribution network) affect the predicted probabilities of recommending B-Hive products.

MIS771 Descriptive Analytics and Visualisations

Page 4 of 8

5) Develop a time-series model to forecast Honey demand for the next four fiscal quarters.

6) Produce a written technical report detailing all conclusions and all analysis activities. The report should be comprehensive (i.e., describe all critical outputs of the analysis and conclusions). The analysis should drive the conclusions and the recommendations to the management team (i.e., a clear link/alignment).