discussion

Causal Analysis Instructions

Magic Foods – Case Study

Case Description

Magic Foods is a leading manufacturer of pickles, spices, pastes and instant mixes. With quality products and customer satisfaction, the company maintains a leading position in the processed food market in a number of regions. However, very recently, the company has witnessed an increase in competition resulting in a decrease of sales. Company leadership wants to know the critical factors driving sales and what should the company focus on to be able to increase sales.

Generally, the company would like to know how many potential customers are in their market space, how can they be reached and how fierce is the competition? However, the ultimate measure of performance is sales. As a result, the company collected data on:

a. Last year sales in various regions

b. Population in each region

c. Number of food stores in each region

d. Number of Magic Food’s dealers in each region

e. Number of popular competitor brands of similar products sold in each region

Instructions

You may work with other students in this course section. First, conduct necessary calculations and scatter plot visualizations to address parts below using the provided Excel spreadsheet model with data. Use the Excel spreadsheet model to determine the relationships between all of the data using Correlation Analysis (aka Causal Analysis). The first relationship has been calculated and you are to complete the correlation coefficients (r) and coefficient of determination values (R2) for each of the remaining variables. The correlation coefficient is to be calculated using the Excel CORREL function. The coefficient of determination (R2) is the squaring of the correlation coefficient (r). The dependent variable is the objective measure (sales) and the independent variables are the population, No. of stores, No. of dealers, No. of competitive brands and the region number.

Finally, prepare a short response (1-2 paragraphs, not to exceed 200 words) for this week’s discussion describing the relationships of each correlated set – in terms of strength and polarity (positive or negative) using the r coefficient. The r coefficient is always shown using a leading 0 with at least 3 decimal places – example: 0.9746 or -0.9748. Then, describe the proportional quality of the relationships using the correlation of determination R2 (r squared) as a percent with 2 decimal places – example 94.98%. Finally, which variable best describes the impact on Sales?

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