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

**Recommendations:**

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.

**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.

## Similar Samples

Delve into our diverse array of samples to gain insight into statistical concepts. Whether you're a student or simply curious, our collection offers valuable examples and explanations. Explore our samples to sharpen your understanding and excel in statistical assignments. Uncover the power of statistical analysis through our carefully selected examples.

R Programming

R Programming

R Programming

R Programming

R Programming

R Programming

R Programming

R Programming

R Programming

R Programming

R Programming

R Programming

R Programming

R Programming

R Programming

SAS

R Programming

R Programming

R Programming