Statistical Analysis on Manufacturing Equipment Quality, Strength, & Accuracy

This project is curated for learners to learn the art of applying various data cleaning & data visualization techniques and statistical tests to validate hypotheses.

Statistical Analysis on Manufacturing Equipments to study the Quality, Strength and Accuracy
  • 1 bar graph
    Difficulty: Beginner

    No prior knowledge or experience with data required.

  • Asset 1
    Duration: Approximately 3 hours

Case Overview

  • This project is curated for learners to learn the art of applying various data cleaning & data visualization techniques and statistical tests to validate hypotheses.
  • As technology is evolving, it will enable businesses to adopt new ways of producing products or parts, and it is likely that this adoption will make way for future needs.Hence, it is vital to investigate and examine all the features that influence production.
  • Machinery upgrade, changes in print speed, or even a change in product can all be very beneficial for manufacturers to formulate better production mechanism by using various visualization techniques and statistical inferences.
  • Learn to apply Hypothesis testing on several parameters to use the results generated to perform visualizations.

What’s included


Lifetime Access

Access this project for life once completed


Flexible Scheduling

Start learning online immediately, at your own pace


Desktop Only

We recommend completing this project on a desktop

Skills You Will Learn

Data Management

Data Visualization

Data Analysis

Data Aggregation

Data Pre-Processing

Statistical Analysis

Hypothesis Testing

Data Distribution Charts

Associated Learning Tasks

Case Context

  • The manufacturing sector is under constant pressure to increase profitability in a growingly competitive international market.
  • 3D printing allows you to produce more complex shapes with less material and was originally intended mainly for prototype and one-off production, but it is quickly growing into a manufacturing process.
  • As a result, it’s critical to investigate the components that influence the instances’ manufacturing or printing.

  • EDA was used to evaluate the data set to investigate and summarize the primary characteristics involved in an accident, with the help of various functionalities and other data visualization techniques.
  • Correlation test was performed to test the relationship between input variable: layer height and output variable: Roughness; nozzle temperature and output variable: Elongation.
  • Various statistical tests such as the t-test, chi-square, ANOVA were used.

  • This case can be emulated to check what all factors affect the printing accuracy, quality, and strength, and based upon the results certain measures can be incorporated to improve the output.
  • Identification of significant factors can result in good levels of productivity maintained for a long period.
  • Improvements to machinery, adjustments to the print speed, or even a product change can be achieved.

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