standard_description: Interpret the parameters in a linear or exponential function in terms of a context.
problem_activity: Task
Below is a table showing the approximate boiling point of water at different elevations:
{"table": [{"_attributes": {"class": ["regular"]}, "tbody": [{"tr": [{"th": [{"_value": "Elevation (meters above sea level)"}, {"_value": "Boiling Point (degrees Celsius)"}]}, {"td": [{"_value": "0"}, {"_value": "100"}]}, {"td": [{"_value": "500"}, {"_value": "98.2"}]}, {"td": [{"_value": "1000"}, {"_value": "96.5"}]}, {"td": [{"_value": "1500"}, {"_value": "94.7"}]}, {"td": [{"_value": "2000"}, {"_value": "93.1"}]}, {"td": [{"_value": "2500"}, {"_value": "91.3"}]}]}]}]}
Based on the table,
if we were to model the relationship between the elevation and the boiling point of water, would a linear function be appropriate? Explain why or why not.
Below are some additional values for the boiling point of water at higher elevations:
{"table": [{"_attributes": {"class": ["regular"]}, "tbody": [{"tr": [{"th": [{"_value": "Elevation (meters above sea level)"}, {"_value": "Boiling Point (degrees Celsius)"}]}, {"td": [{"_value": "5,000"}, {"_value": "83.2"}]}, {"td": [{"_value": "6,000"}, {"_value": "80.3"}]}, {"td": [{"_value": "7,000"}, {"_value": "77.2"}]}, {"td": [{"_value": "8,000"}, {"_value": "74.3"}]}, {"td": [{"_value": "9,000"}, {"_value": "71.5"}]}]}]}]}
Based on the table,
if we were to model the relationship between the elevation and the boiling point of water, would a linear function be appropriate? Explain why or why not.
When the information from both tables is combined, would a linear function be appropriate to model this data?  What kind of function would you use to model the data?  Why?


aligns: no