Example 3: Given the data tables below: Table 1: Time (s) 0 0.04 0.08 0.12 0.16 0.20 0.24 0.28 0.32 0.36 0.40 Height (ft) 4.54 4.46 4.34 4.16 3.94 3.68 3.37 3.02 2.63 2.2 1.74 Table 2: Time in minutes (x) 1 4 7 9 13 17 20 23 25 Number of bacteria (y) 3 21 46 65 108 158 198 240 270 Table 3: Roll 1 2 3 4 5 6 7 8 9 10 Number 252 207 170 146 123 100 85 67 56 48 Cubes Remaining Table 4: (mths) 0.5 1.5 2.5 3.5 4.5 5.5 6.5 7.5 8.5 9.5 10.5 11.5 12.5 13.5 t () -10.1 -3.6 11.0 30.7 48.6 59.8 62.5 56.8 45.5 25.1 2.7 -6.5 -10.8 -5.0 T Table 5: Year 2008 2009 2010 2011 2012 2013 2014 Average Price 3.26 2.35 2.78 3.52 3.64 3.52 3.36 (dollars per gallon) Match the data table with the correct model. Exponential Power Quadratic Quartic Trigonometric Find a regression/prediction equation for each data table. Compare the actual and predicted values of the dependent variable. How closely does the model predict the given values of the domain? Pre-Calculus Objective 2.04 Composition/Inverse Functions
Time (s) 0 0.04 0.08 0.12 0.16 0.20 0.24 0.28 0.32 0.36 0.40 Height (ft) 4.54 4.46 4.34 4.16 3.94 3.68 3.37 3.02 2.63 2.2 1.74 Example 3: Given the data tables below: Table 1: Table 2: Time in minutes (x) 1 4 7 9 13 17 20 23 25 Number of bacteria (y) 3 21 46 65 108 158 198 240 270 Table 3: Roll 1 2 3 4 5 6 7 8 9 10 Number Cubes Remaining 252 207 170 146 123 100 85 67 56 48 t (mths) 0.5 1.5 2.5 3.5 4.5 5.5 6.5 7.5 8.5 9.5 10.5 11.5 12.5 13.5 T () -10.1 -3.6 11.0 30.7 48.6 59.8 62.5 56.8 45.5 25.1 2.7 -6.5 -10.8 -5.0 Table 4: Table 5: Year 2008 2009 2010 2011 2012 2013 2014 Average Price (dollars per gallon) 3.26 2.35 2.78 3.52 3.64 3.52 3.36 Match the data table with the correct model. Exponential Power Quadratic Quartic Trigonometric Find a regression/prediction equation for each data table. Compare the actual and predicted values of the dependent variable. How closely does the model predict the given values of the domain? Pre-Calculus Objective 2.04 Composition/Inverse Functions
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