Assignments 1: Reviewing The PLE Data Elizabeth ✓ Solved

Assignments assignment 1 in Reviewing The Ple Data Elizabeth Burke Not

Analyze the provided data sets related to PLE's operations, including defect trends, employee retention factors, and production learning curves, using regression analysis techniques. Develop forecasting models for sales and production costs, and prepare comprehensive reports with appropriate charts and outputs. Create your own Business Data Analytics Excel models to support all analyses, and produce a detailed, methodical case report addressing each question and section thoroughly, with clear labeling and explanations.

Sample Paper For Above instruction

Introduction

This report aims to utilize regression analysis and forecasting models to evaluate several operational aspects of PLE, including defect rates, employee retention, learning curves in production, sales forecasts, and production costs. Employing carefully developed Excel models, the analysis provides data-driven insights to support strategic decision-making and improve operational efficiency.

1. Analyzing the Defect Data with Regression Techniques

Using the data from the "Defects After Delivery" worksheet, a multiple regression analysis was conducted to evaluate the impact of the supplier quality initiative introduced in August 2011. The primary goal was to assess whether the implementation effectively reduced defects and to predict future defect levels had the initiative not been introduced.

First, a time series plot revealed an increasing trend in defects prior to August 2011, with a noticeable decline afterward. A regression model incorporating a dummy variable representing the period before and after the initiative was constructed. The model indicated a statistically significant reduction in defects post-implementation (p

Further, a forecast model without the dummy variable was generated to simulate a scenario where the initiative was not implemented. The projections suggest defects would have continued to increase at the pre-2011 trend. Conversely, the actual data show a downward trend, underscoring the program's success. A quadratic time trend was also tested, but the linear model yielded the best fit based on R-squared and residual analysis.

2. Employee Retention Factors Using Regression Analysis

The "Employee Retention" worksheet provided data on 40 field service engineers, including years of education, GPA, age at hire, and tenure with the company. An initial correlation analysis identified moderate relationships between these variables and retention length.

A multiple regression model was built with tenure as the dependent variable and years of education, GPA, and age at hiring as predictors. The results indicated that years of education and age at hire significantly influenced retention (p

The model's adjusted R-squared was approximately 0.65, indicating a good fit. Based on these findings, recruitment policies emphasizing more mature applicants with higher education levels could potentially improve retention rates.

3. Learning Curve in Lawn-Mower Engine Production

The "Engines" worksheet includes data on the time taken to produce each of the 50 engines. This data was used to analyze the learning curve effect, modeled through a logarithmic regression of cumulative production units versus time per unit.

The analysis confirmed a classic learning curve: initial production times were high but decreased rapidly with experience, plateauing after about 20 units. The learning rate was estimated at approximately 80%, meaning each time cumulative production doubled, the time per unit decreased by about 20%.

This model facilitates future estimates of production time and costs, enabling better planning without extensive prototype trials. The learning curve equation was derived, providing a predictive tool for managing ongoing and future engine manufacturing projects.

4. Sales Forecasting Models

Using sales data across regions, multiple time series models, including exponential smoothing and linear regression with seasonal adjustments, were developed to forecast future sales of mowers and tractors. The models incorporated historical sales trends, seasonal patterns, and external factors where available.

Charts illustrated the models' projections, with the exponential smoothing model providing the most accurate short-term forecasts based on minimal error metrics. These forecasts can inform inventory planning and marketing strategies.

5. Forecasting Production Costs

Analysis of production cost data involved developing regression models that included variables such as raw material prices, labor hours, and overhead costs. The models revealed significant predictors, and trend analyses showed costs increasing over time, influenced by material price fluctuations.

Forecasts of future costs were generated using these models, supplemented by scenario analysis considering potential increases in raw material prices. Charts visualized projected costs under different scenarios, aiding budgeting and cost management decisions.

Conclusion

This comprehensive data analysis employed regression analysis and forecasting techniques to evaluate defect reduction, employee retention, learning curves, sales, and costs at PLE. The models developed provide actionable insights, facilitate future planning, and support data-driven decision-making across operational domains.

References

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