Q1: Carry out Multiple linear Regressions for the following case. (10 Marks)
Sawari Rajdan operates a candle production company situated in Gulalwadi, Mumbai. She is actively promoting her products through social media platforms, crafting engaging reels featuring a variety of candles accompanied by trendy music. In her pursuit to enhance her business strategy, she is in search of a model that can forecast her product sales based on the quantity of likes and comments garnered by her Instagram reels. She has also extended her content to YouTube Shorts, believing that the number of likes and comments on these videos might influence sales as well. To aid in this endeavor, Sawari has carefully compiled data specific to each reel, with each reel showcasing a different type of candle. The scale of likes and comments mentioned below.
Write the regression model.
Interpret the Regression statistics Table
Interpret the ANOVA Table
Discuss the bo and b1,b2,b3,and b4 (discuss significant one only
Ans :
The regression model for Sawari Rajdan’s candle sales based on the quantity of likes and comments garnered by her Instagram and YouTube Shorts videos, we can use multiple linear regression. In this model, we will have four independent variables: Likes on Instagram Reels, Comments on Instagram Reels, Likes on YouTube Shorts, and Comments on YouTube Shorts. The dependent variable is Sales (in 000’s INR). The regression equation can be written as:
Concept & Application:
Intercept (bo): The intercept represents the expected Sales when all predictor variables are zero. In this case, the intercept is 10.28896153, suggesting that, with zero social media interactions, there is a baseline expected Sales value of approximately 10,000 INR.
Likes on Instagram Reels (b1): This coefficient is 0.029705282, and it’s highly significant (p-value = 0.00028556). For each additional like on Instagram Reels, Sales are expected to increase by approximately 0.0297 units in 000’s INR. This indicates a positive and significant impact on Sales.
Comments on Instagram Reels (b2): The coefficient for Comments on Instagram Reels is -0.01890629. However, it is not statistically significant (p-value = 0.436234738). This suggests that the number of comments on Instagram Reels does not have a significant impact on Sales in this model.
In summary, the regression model indicates that Likes on Instagram Reels have a statistically significant positive impact on Sales, while Comments on Instagram Reels, Likes on YouTube Shorts, and Comments on YouTube Shorts do not have a statistically significant impact. The overall model is highly significant, and it explains approximately 79.82% of the variance in Sales.
Q2: Run the Discriminant Analysis in the following case. (10 Marks)
Sharmila Kaul, an entrepreneur based in Mumbai, heads a company named “Bhartiya Khanna.” She has developed a line of nutritious meal products consisting of ten different options for both morning and evening consumption. These products are focused solely on their nutritional value. Sharmila has incorporated a variety of healthful ingredients, including millets, into the product formulations.
Over the past year, she received numerous comments about her products not aligning well with traditional Indian cuisine in terms of taste. In response, she made the decision to carry out a brief survey within her workplace’s vicinity. Following product demonstrations and trials, she gathered input from random individuals in the neighbourhood.
The respondents sampled and comprehended the products before providing their feedback. The feedback required customers to indicate their preferences and concerns on a scale of 1 to 9, where higher numbers reflected a stronger liking. The resulting data is presented below. Can this information be used to distinguish potential purchasing intent? (set the prior probabilities are equal for each group)
Wilks Lambda table and its interpretation
Centroid table and its interpretation
Conclusion about model with justification (include group wise mean and classification matrix in your writing)
Ans :
Introduction to Discriminate Analysis
Discriminate Analysis is a statistical technique used to classify observations or individuals into predefined categories based on their characteristics or features. It’s commonly utilized in machine learning and statistics to determine which attributes or variables contribute the most to group separation. The goal is to find the best discriminating features that distinguish different groups.
Concept & Application:
In the case of Sharmila Kaul, an entrepreneur running “Bhartiya Khanna” in Mumbai, Discriminant Analysis can provide valuable insights into customer preferences and potential purchasing intent for her line of nutritious meal products. Sharmila has developed a variety of meal options, aiming to focus on nutritional value by incorporating healthful ingredients such as millets.
The study involves collecting data from individuals who have sampled and comprehended the products, providing feedback on various aspects such as craving, nutrition, fat, and protein intake. The respondents’ feedback is crucial for understanding their preferences, which can be utilized to distinguish between those likely to buy the product and those who may not.
Conclusion and Classification Matrix:
Based on the Wilks Lambda and Centroid analysis, you can draw conclusions about the model’s ability to distinguish between potential purchasing intent. You can also create a classification matrix to evaluate the model’s performance in classifying new data points.
The classification matrix should include metrics such as accuracy, precision, recall, and F1-score for each group. These metrics will help you assess how well the model classifies observations into the “Will buy the product” and “Won’t Buy the Product” groups.
Q3: Cluster Analysis Case
Ruchika Taploo, an entrepreneur is looking for some insights for her beauty product that can fit to dry skincare routine; she has formulated a whole product with some herbal ingredients. The product designed with some ecofriendly packaging material. After spending a whole economic year with the same product into market, now she is looking from consumers by keeping the focus on product features. She is thinking for redesigning her products aligned with customer’s need. She has gathered the few attributes related to her product as well as tried to capture customers’ liking on a scale of 1 to 9 (1=strong dislike…9=strong like).
Part A: Discuss how many clusters are advisable to form using the results of Hierarchical cluster analysis
Ans :
Enhancing Beauty Product Design through Consumer-Centric Clustering Analysis
Introduction:
In the dynamic realm of the beauty industry, understanding consumer preferences is a critical aspect that drives product innovation and success. Entrepreneurs like Ruchika Taploo recognize the pivotal role of aligning their beauty products with the distinct needs and desires of their target market.
Concept & Application:
Inspect the Dendrogram: Look at the dendrogram and identify the point at which the branches fuse. This indicates the number of clusters.
Choose the Optimal Number of Clusters: Look for the tallest vertical line that doesn’t intersect with any horizontal line. Draw a horizontal line through that point. The number of vertical lines this newly drawn horizontal line intersects will give you the optimal number of clusters.
For instance, if the line intersects 3 vertical lines, then 3 clusters are advisable.
Interpret the Clusters: Once you’ve determined the optimal number of clusters, examine the data within each cluster to understand the characteristics and preferences of the consumers in each cluster.
Conclusion:
Understanding consumer preferences is fundamental to the success of any product in the beauty industry. Ruchika Taploo, the visionary entrepreneur, recognized this critical aspect and embarked on a journey to align her beauty product, tailored for dry skincare routines, with the desires of her target market.
Part B: Discuss the K-means Clusters significance with ANOVA considering suggestion form part A, also wrote memberships and labels of clusters. (5 Marks) –
Ans :
Introduction
In this case study, we examine the scenario of Ruchika Taploo, an entrepreneur in the beauty product industry. Ruchika has developed a beauty product crafted with herbal ingredients, aiming to cater to individuals with dry skin. Furthermore, she has emphasized the use of eco-friendly packaging for her product, aligning with sustainable practices.
Concept & Application:
The attributes assessed in the study are fragrance, price, packaging, texture, and long-lasting effect. The ratings provided by customers represent their satisfaction levels, with 1 indicating strong dislike and 9 indicating strong satisfaction.
Conclusion
In this case study, we embarked on a journey to assist Ruchika Taploo, an entrepreneurial beauty product developer. Ruchika had designed a beauty product infused with herbal ingredients, specifically tailored for individuals with dry skin. Her product also emphasized sustainability through the use of eco-friendly packaging materials. After a year in the market, she sought to refine her product based on consumer preferences.
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