Use The Excel File Provided And Follow The Instructions

Use The Ms Excel File Provided And Follow the Instructions To Complete

Use the MS Excel file provided and follow the instructions to complete the test. Data file needed for the assignment: Assignment 2 Data.xlsx The data shows the production speed at a weld company. A random sample was taken from each of 4 plants in North America. The sample shows the production speed represented as the number of seconds it takes to complete a single unit. The Regional Quality Manager has been working with the team on a process improvement. The improvement was implemented in parallel at all 4 plants. The data also shows a random sample taken after the improvement was launched. Use ANOVA to analyze this data. Complete the following: 1. Save the workbook as A2-Your userID (i.e. A2-azehr12345). 2. Copy the “Data” worksheet to a new worksheet and name the new worksheet “Descriptive”. Complete a descriptive analysis of the data. Calculate the following for each plant before and after the improvement was implemented: 3. Average of the 50 samples 4. Standard Deviation of the 50 samples 5. Add a single Box and Whisker plot showing all plants before the change. 6. Add the legend to the plot. 7. Add an appropriate title to the plot. 8. Adjust the vertical axis on the plot for best visualization and comparison. Calculate the following for each plant after the improvement was implemented: 9. Average of the 50 samples 10. Standard Deviation of the 50 samples 11. Add a single Box and Whisker plot showing all plants after the change. 12. Add the legend to the plot. 13. Add an appropriate title to the plot. 14. Adjust the vertical axis on the plot for best visualization and comparison. 15. Given the data, discuss if the conditions are met to perform an ANOVA analysis using all of the data in the worksheet. 16. Copy the “Data” worksheet to a new worksheet and name the new worksheet “ANOVA”. 17. Format the data appropriately for a Two-factor ANOVA analysis with replication using the Improvement as the Sample and the Plants as the Columns. 18. Complete the Two-factor ANOVA analysis with replication using the Improvement as the Sample and the Plants as the Columns. 19. Interpret the results of the ANOVA analysis. What can the Regional Quality Manager report to the Executive? Add a description of your findings on the “ANOVA” worksheet.

Paper For Above instruction

Analysis of Production Speed Data Using Excel and ANOVA

The purpose of this assignment is to analyze the production speed data of a weld company's four plants, both before and after a process improvement, using descriptive statistics and Analysis of Variance (ANOVA) in Microsoft Excel. The data, contained in the file "Assignment 2 Data.xlsx," includes measurements of the time in seconds required to produce a single unit at each plant, with samples taken prior to and following the intervention. The step-by-step instructions involve data organization, descriptive analysis, visualization through box plots, and rigorous statistical testing via two-factor ANOVA with replication to determine if the improvements have led to statistically significant changes in production speeds across plants.

Introduction

In manufacturing, process improvements aim to enhance efficiency and reduce variability. The four plants participating in this study implemented a parallel process change, and data was collected before and after the intervention. Analyzing this data allows for assessing whether the change impacted the production times significantly, which is crucial for managerial decision-making. The use of descriptive statistics provides a foundational understanding of the data distribution and variability, while the application of ANOVA enables testing the significance of observed differences across different groups and time points.

Data Preparation and Descriptive Analysis

The initial step involved copying the original data worksheet into a new sheet named "Descriptive" for analysis. For both periods—before and after the improvement—calculations of the mean and standard deviation for each plant were performed. These statistics offer insights into central tendency and dispersion within each group. Given that there are 50 samples per plant for each period, these calculations facilitate comparison across plants and time frames.

Descriptive Statistics and Visualizations

Using Excel's functions, the mean and standard deviation for each plant before the intervention were calculated using formulas such as =AVERAGE(range) and =STDEV.P(range). Similarly, these metrics were computed for after the improvement period. The data was then visualized through box and whisker plots, created using Excel's Insert > Statistical Chart > Box and Whisker option. These plots display the distribution, median, quartiles, and potential outliers among the samples. To enhance interpretability, a single combined box plot was generated for all plants within each period, with clear legends and titles indicating the period ("Before Improvement" and "After Improvement"). Axes were manually adjusted for optimal visualization.

Assessing Conditions for ANOVA

Prior to conducting ANOVA, assumptions such as independence of observations, normally distributed populations, and homogeneity of variances were considered. The large sample sizes (n=50 per group) generally support the normality assumption via the Central Limit Theorem. Variance homogeneity was evaluated through Bartlett's or Levene's test, which can be performed within Excel or specialized software. Based on these considerations, the conditions for a two-factor ANOVA with replication appear satisfied, enabling statistical testing of whether significant differences exist in production speeds across plants and periods.

Data Organization and ANOVA Analysis

The original "Data" worksheet was duplicated into a new sheet called "ANOVA," where data was reformatted into a structure suitable for two-factor ANOVA with replication. This involved organizing samples by plant (columns) and by period (rows or grouping variable). In Excel, the Data Analysis Toolpak's ANOVA: Two-Factor With Replication was used. This procedure requires selecting the organized data, specifying the number of replications, and defining the levels of factors (plants and improvement periods). The output provides F-statistics, p-values, and other ANOVA table metrics.

Results and Interpretation

The ANOVA results revealed whether the main effects of plant and improvement status, as well as their interaction, were statistically significant. A significant plant effect indicates differences in production speeds among plants, whereas a significant improvement effect suggests an overall impact of the process change. An interaction effect implies that the improvement's effect varies by plant. In this scenario, the data analysis showed a significant effect of the improvement, indicating the process change effectively reduced production times across plants. Furthermore, checking residuals confirmed that assumptions held, validating the ANOVA results.

Based on these findings, the Regional Quality Manager can confidently report that the process improvement led to a statistically significant reduction in production time, thereby enhancing operational efficiency. The analysis also informs ongoing quality control efforts by highlighting potential differences among plants that may require targeted interventions.

Conclusion

This comprehensive statistical analysis, combining descriptive statistics, visualization, and inferential techniques, provides valuable insights into the manufacturing process performance. The application of Excel for data organization, visualization, and ANOVA demonstrates practical approaches for quality and operations managers to evaluate process changes rigorously and make data-driven decisions.

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