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

September 13, 2023
Thomas Lewis
🇺🇸 United States
R Programming
I'm Thomas Lewis, a seasoned statistician specializing in R programming with 8+ years of experience. Holding a Ph.D. in Statistics from Cornell University, I assist students in completing their assignments with expertise and precision.
Key Topics
• Problem Description
• Logistic Regression Model:
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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:

CoefficientsEstimateOddsStd. Errorz valuePr(>|z|)
(Intercept)-30.373036.4446.73408-4.5106.47e-06
X1.f21.825966.2080.895302.0390.04140
X1.f30.732032.0791.150930.6360.52476
X20.842252.3220.510411.6500.09891
X31.661915.2690.620722.6770.00742
X4-0.370740.6900.87190-0.4250.67069
X50.032001.0320.648410.0490.96065
X61.133933.1070.320293.5400.00040
X70.686081.9860.586031.1710.24171
X8-0.217750.8040.25178-0.8650.38711
X9-0.049570.9520.40779-0.1220.90325
X10-0.568300.5660.28025-2.0280.04258
X112.6442014.0721.501851.7610.07830
X121.010212.7460.481122.1000.03576
X13-0.557800.5720.21988-2.5370.01119
X140.615611.8510.495821.2420.21438
X150.013651.0130.153700.0890.92924
X16-0.076730.9260.38419-0.2000.84171
X172.8872917.9451.564721.8450.06500
X18-3.504370.0302.91833-1.2010.22982

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
• Sales force image
• Competitive pricing

Recommendations:

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.

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