In this logistic regression assignment, we embarked on a comprehensive analysis to discern the complex web of factors that significantly affect the perception of future relationships with HBAT, a fictitious company. Employing the robust capabilities of R programming, we constructed a logistic regression model that serves as an invaluable tool to unearth insights from the data at hand. The central objective was to investigate the probability of a purchasing manager considering a strategic alliance or partnership with HBAT, given a multitude of independent variables, such as customer type, industry type, firm size, region, distribution system, and performance perception.
Logistic Regression Model:
Our logistical regression model ventured deep into the intricacies of these variables to reveal a wealth of statistical insights. Here is a snapshot of the model's results:
|Coefficients||Estimate||Odds||Std. Error||z value||Pr(>|z|)|
Table 1: Logistic Regression Model Results
The odds ratio divulged by this model yields fascinating insights into the multifaceted world of future relationships with HBAT. For instance:
- Managers who have been purchasing from HBAT for a duration between 1 and 5 years are six times more inclined to consider a future relationship compared to those with less than a year of buying history.
- Large firms boasting 500 or more employees exhibit five times greater proclivity to consider a future alliance with HBAT compared to smaller firms.
- In the grand tapestry of industries, the newsprint industry displays twice the inclination to consider a future relationship with HBAT in contrast to the magazine industry.
Notably, we identified variables with a p-value less than 0.05 as statistically significant. These are the forces that exert a substantial impact on the perception of a future relationship with HBAT. Our model illuminated the following pivotal variables:
- Buying history between 1 to 5 years
- Large firm size (500 or more employees)
- Product quality
- Sales force image
- Competitive pricing
The culmination of our analysis has paved the way for actionable recommendations:
- Increase advertising efforts, with a keen focus on managers with a buying history of 1 to 5 years and large firms.
- Elevate the quality of the product and enhance the image of the sales force.
- Maintain competitive pricing to wield a significant influence over the perception of future relationships with the company.
Beyond logistic regression, we delved into the intricacies of customer satisfaction through K-means clustering. This advanced analysis technique unveiled an intriguing revelation: both the magazine and newsprint industry segments appear equally satisfied with HBAT.
To evaluate the real-world applicability of our model, we subjected it to a prediction exercise. The accuracy of the model was determined by calculating the percentage of accurately classified perceptions of future relationships with HBAT, resulting in an accuracy rate of 18%.
In summation, this rigorous analysis has unearthed valuable insights, empowering HBAT to make informed decisions, enhance customer relationships, and drive sales in a competitive market landscape.