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Leveraging Logistic Regression Modeling in R to Analyze Future Relationship Perception with HBAT

In this analysis, we harnessed the power of logistic regression modeling in R to explore the intricate dynamics of future relationship perceptions with HBAT. Our extensive study uncovered a multitude of factors influencing this perception. Through a detailed examination of customer type, industry type, firm size, region, distribution system, and performance perception, we unveiled significant insights. These findings enable HBAT to make data-driven decisions, optimize customer relationships, and boost sales. Our approach showcases the practical application of implementing logistic regression models and the use of R for robust statistical analysis.

Problem Description

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|)
(Intercept) -30.37303 6.444 6.73408 -4.510 6.47e-06
X1.f2 1.82596 6.208 0.89530 2.039 0.04140
X1.f3 0.73203 2.079 1.15093 0.636 0.52476
X2 0.84225 2.322 0.51041 1.650 0.09891
X3 1.66191 5.269 0.62072 2.677 0.00742
X4 -0.37074 0.690 0.87190 -0.425 0.67069
X5 0.03200 1.032 0.64841 0.049 0.96065
X6 1.13393 3.107 0.32029 3.540 0.00040
X7 0.68608 1.986 0.58603 1.171 0.24171
X8 -0.21775 0.804 0.25178 -0.865 0.38711
X9 -0.04957 0.952 0.40779 -0.122 0.90325
X10 -0.56830 0.566 0.28025 -2.028 0.04258
X11 2.64420 14.072 1.50185 1.761 0.07830
X12 1.01021 2.746 0.48112 2.100 0.03576
X13 -0.55780 0.572 0.21988 -2.537 0.01119
X14 0.61561 1.851 0.49582 1.242 0.21438
X15 0.01365 1.013 0.15370 0.089 0.92924
X16 -0.07673 0.926 0.38419 -0.200 0.84171
X17 2.88729 17.945 1.56472 1.845 0.06500
X18 -3.50437 0.030 2.91833 -1.201 0.22982

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.

Significant Variables:

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
  • Advertising
  • Sales force image
  • Competitive pricing


The culmination of our analysis has paved the way for actionable recommendations:

  1. Increase advertising efforts, with a keen focus on managers with a buying history of 1 to 5 years and large firms.
  2. Elevate the quality of the product and enhance the image of the sales force.
  3. Maintain competitive pricing to wield a significant influence over the perception of future relationships with the company.

Cluster Analysis:

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.

Model Accuracy:

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.