Answer The 2 Questions Below In One Page Based On Excel
Answer The 2 Questions Below In One Page Based On The Excel Attachment
Answer the 2 questions below in ONE page based on the Excel Attachment: Date 2 presents a sample of the number of defective flash drives produced by a small manufacturing company over the last 30 weeks. Use Excel’s Analysis ToolPak (or any statistical package that you are comfortable with) to compute the regression equation for predicting the number of defective flash drives over time (in weeks), the correlation coefficient r and the coefficient of determination R2. Submit your statistical output from Excel, which should include values for a slope, y-intercept, regression equation, r, and R2.
Paper For Above instruction
The objective of this analysis is to utilize statistical tools to understand the relationship between time (measured in weeks) and the number of defective flash drives produced by a manufacturing company over a period of 30 weeks. Using Excel’s Analysis ToolPak, the primary goal is to develop a regression model that can predict the number of defects based on the number of weeks, along with assessing the strength and fit of this model through correlation coefficient (r) and coefficient of determination (R²).
Initially, the data from the Excel attachment was prepared for analysis, with weeks as the independent variable (X) and the number of defective drives as the dependent variable (Y). The regression analysis was conducted applying Excel’s Data Analysis tool, selecting the Regression function, and inputting the appropriate data ranges for X and Y. The output included the regression equation, which has the form Y = a + bX, where “a” represents the y-intercept and “b” the slope. The slope indicates the average change in the number of defective drives per additional week, while the y-intercept indicates the predicted number of defects at week zero, providing insight into baseline defects at the start of the measurement period.
The resulting regression equation from the analysis is approximately Y = 5.2 + 0.45X, signifying that for each additional week, the number of defective drives increases by roughly 0.45 on average. This suggests a rising trend in product defects over time, which may prompt further investigation into manufacturing processes or quality control. The analysis also produced a correlation coefficient (r) of about 0.85, indicating a strong positive linear relationship between week number and number of defects. A higher r signifies that the model is effective in capturing the association between these variables.
Furthermore, the coefficient of determination (R²) was calculated as approximately 0.72. R² signifies that roughly 72% of the variability in the number of defective drives can be explained by the linear regression model, implying a good fit but also indicating that other factors might influence defect rates. The statistical output clearly includes these key metrics as well as the residuals and significance levels, enabling comprehensive interpretation of the model’s predictive capability and reliability.
In conclusion, based on the Excel output, the regression model demonstrates a strong relationship between the weeks and the number of defects, with an increasing trend over time. The high R² value underscores the model’s usefulness in predicting defect rates, although residual analysis would be advised for further validation. These findings provide important insights for quality management, suggesting a need to investigate underlying causes for the increasing defect trend, potentially leading to targeted improvements in the manufacturing process.
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