======Extrapolation and Interpolation With Spaghetti======


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**Alternative titles:** Using a Line of Best Fit with Spaghetti, Spaghetti Length and Mass Graphing
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====Summary====                   
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====Procedure====  
  - Break spaghetti sticks into various random lengths.  
  - Measure the length of each piece with a ruler (in millimeters).  
  - Measure the mass of each piece using a beam or electronic balance.
  - Plot the data on a graph with length on the x-axis and mass on the y-axis.  
  - Draw a straight line of best fit through the data points.  
  - Select a length not directly measured (within the range of the graph for interpolation, or outside of the range of the graph for extrapolation), mark it on the x-axis, and use the graph to predict its mass.  
  - Break a fourth spaghetti stick to that length, measure its mass, and compare the actual value with your prediction.



====Links==== 
Graphing Data from Spaghetti Mass vs Length Investigation - Suzie “Fedsie” Feodoroff: 
{{youtube>YcE3kqmnlf8?}}\\

📄 Spaghetti Graphing Activity - mychemistryclass.net: [[https://www.mychemistryclass.net/Files/1%20Interactive%20Notebook%202011%202012/Unit%200%20Foundations%20in%20Chemistry%20DONE/Graphing/Spaghetti%20Graphing%20Activity%20with%20graph%20paper.pdf]]\\


====Variations====
  * Test different brands or types of spaghetti to see if they follow the same mass–length relationship.  
  * Extend the experiment by comparing thin versus thick pasta shapes (e.g., spaghetti vs. linguine).  
  * Instead of snapping randomly, cut precise lengths and compare predictions with actual data.



====Safety Precautions====
  * Be cautious when snapping spaghetti as small pieces may fly off.


====Questions to Consider====
  * Why is a line of best fit better than joining the points dot-to-dot? (It smooths out small measurement errors and shows the overall trend more clearly.)  
  * What is the relationship between length and mass of spaghetti? (They are directly proportional—the longer the piece, the greater the mass.)  
  * Why might your prediction not match the measured value exactly? (Experimental error, uneven density, or slight inconsistencies in breaking the spaghetti.)  
  * How could this method be applied to other real-world measurements? (Predicting weight from height, estimating costs from quantities, or scaling recipes.)