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Understanding Standard Data: Benefits, Disadvantages, and Statistical Concepts in Analysis

This document explores the concept of standard data, focusing on its application in predicting costs, particularly automotive repairs. It outlines various advantages, such as enabling pre-production estimates and maintaining consistency through extensive databases. However, it also discusses the potential drawbacks, including the need for thorough analytical familiarity and the cost of developing and maintaining a standard data database. Moreover, it delves into the importance of distinguishing between constant and variable elements, curve fitting techniques, and essential statistical concepts crucial to effective data analysis.

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Understanding Standard Data: Benefits, Disadvantages, and Statistical Concepts in Analysis

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  1. Ch. 30: Standard Data • Means the reuse of previous times. • For example, predict cost of automotive repairs.

  2. Advantages of Using Standard Data • Ahead of Production • The operation does not have to be observed. • Allows estimates to be made for bids, method decisions, and scheduling. • Cost • Time study is expensive. • Standard data allows you to use a table or an equation. • Consistency • Values come from a bigger database. • Random errors tend to cancel over many studies. • Consistency is more important than accuracy.

  3. Random and Constant Errors

  4. Disadvantages of Standard Data • Imagining the Task • The analyst must be very familiar with the task. • Analysts may forget rarely done elements. • Database Cost • Developing the database costs money. • There are training and maintenance costs.

  5. Motions vs. Elements • Decision is about level of detail. • MTM times are at motion level. • An element system has a collection of individual motions. • Elements can come from an analysis, time studies, curve fitting, or a combination.

  6. Constant vs. Variable • Each element can be considered either constant or variable. • Constant elements either occur or don’t occur. • Constant elements tend to have large random error. • Variable elements depend on specifics of the situation. • Variable elements have smaller random error.

  7. Developing the Standard • Plan the work. • Classify the data. • Group the elements. • Analyze the job. • Develop the standard.

  8. Curve Fitting • To analyze experimental data: • Plot the data. • Guess several approximate curve shapes. • Use a computer to determine the constants for the shapes. • Select which equation you want to use.

  9. Statistical Concepts • Least-squares equation • Standard error • Coefficient of variation • Coefficient of determination • Coefficient of correlation • Residual

  10. 10 8 6 4 2 [y] y=4 0 2 4 6 8 10 [x] Curve Shapes Y independent of X • Y = A • Determine that Y is independent of X by looking at the SE.

  11. Curve Shapes Y depends on X, 1 variable • Examples • Others:

  12. Curve Shapes Y depends on X, multiple variables • Y = A + BX + CZ • Results in a family of curves

  13. Example Application: Walk Normal Times (min)

  14. Walk Data Scatterplot

  15. Equations for Walk Data Set • Walk time h =.0054 + .01D r2 = .986 σ = .0073 h • Walk time h = –.01 + .014D –.00013D2 r2 = .989 σ = .0067 h • Walk time h = –.13 + .11 (loge Distance, m) r2 = .966 σ = .012 h • 1/Walk time h = .24 – .96 (1/D) r2 = .881 σ = .021 h-1

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