Regression Analysis in Six Sigma

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What is regression analysis used for in Six Sigma?

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Regression analysis is used to forecast the change in the dependent variable and describe the relationship between predictor variables (X factors) and response variables (Y outputs).

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What does simple linear regression help us find?

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Simple linear regression helps us find the line of best fit, which can be visualized as a red line through the center of a plot on a chart.

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What is the formula for simple linear regression?

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The formula is: y = β0 + β1x + e, where β0 is the y-intercept, β1 is the slope, and e is the error term.

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What is the difference between simple and multiple linear regression?

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Simple linear regression involves one predictor variable (X) and one response variable (Y), while multiple linear regression involves multiple predictor variables and one response variable.

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What is the simple linear least-squares regression formula?

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The formula is: ŷ = β0 cap + β1 cap * x, where β0 cap and β1 cap are estimates of the true values of the y-intercept and slope.

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What are some important considerations in simple linear least-squares regression?

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Considerations include non-linear relationships between X and Y, the impact of outlier data, and the inconsistency of variance in residuals.

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How does the presence of outlier data affect regression analysis?

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Outlier data can significantly affect the results of regression analysis, potentially skewing the line of best fit.

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Why is consistency of variance in residuals important?

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Consistency of variance in residuals is important for accurate model predictions, especially for small values of X.