Nonliner project
Nonlinear Model Project – Parameters  
Quadratic Model  Exponential Model  
Student Number  Last Name  First Name  Input Time of Day (hour)  Target Outdoor Temperature  Input Elapsed Time (minutes)  Target Coffee Temperature 
1  
8  Fra……  Eve…..  10.0  3.5  48  87 
Quadratic Model parameters:
(QR4): Each student will compute a temperature estimate for a different input time of day assigned to you in the row associated with your name in this NonlinearProjectParameters spreadsheet.
(QR6): Each student will estimate the time(s) of day when the outdoor temperature is a different specific target outdoor temperature, assigned to you in the row associated with your name in this spreadsheet.
Exponential Model parameters:
(ER4): Each student will compute a temperature estimate for a different input elapsed time x assigned to you in the row associated with your name in this NonlinearProjectParameters spreadsheet.
(ER5): Each student will work with a different target coffee temperature T assigned to you in the row associated with your name in this spreadsheet.
Quadratic Regression (QR)
Data: On a particular day in April, the outdoor temperature was recorded at 8 times of the day, and the following table was compiled.
Time of day (hour) x 
Temperature (degrees F.) y 
7  35 
9  50 
11  56 
13  59 
14  61 
17  62 
20  59 
23  44 
REMARKS: The times are the hours since midnight. For instance, 7 means 7 am, and 13 means 1 pm.
The temperature is low in the morning, reaches a peak in the afternoon, and then decreases.
Tasks for Quadratic Regression Model (QR)
(QR1) Plot the points (x, y) to obtain a scatterplot. Note that the trend is definitely nonlinear. Use an appropriate scale on the horizontal and vertical axes and be sure to label carefully.
(QR2) Find the quadratic polynomial of best fit and graph it on the scatterplot. State the formula for the quadratic polynomial.
(QR3) Find and state the value of r^{2}, the coefficient of determination. Discuss your findings. (r^{2} is calculated using a different formula than for linear regression. However, just as in the linear case, the closer r^{2} is to 1, the better the fit. Just work with r^{2}, not r.) Is a parabola a good curve to fit to this data?
(QR4) Use the quadratic polynomial to make an outdoor temperature estimate. Each class member will compute a temperature estimate for a different time of day assigned by your instructor. Be sure to use the quadratic regression model to make the estimate (not the values in the data table). State your results clearly — the time of day and the corresponding outdoor temperature estimate.
(QR5) Using algebraic techniques we have learned, find the maximum temperature predicted by the quadratic model and find the time when it occurred. Report the time to the nearest quarter hour (i.e., __:00 or __:15 or __:30 or __:45). (For instance, a time of 18.25 hours is reported as 6:15 pm.) Report the maximum temperature to the nearest tenth of a degree. Show work.
(QR6) Use the quadratic polynomial together with algebra to estimate the time(s) of day when the outdoor temperature is a specific target temperature. Each class member will work with a different target temperature, assigned by your instructor. Report the time(s) to the nearest quarter hour. Be sure to use the quadratic model to make the time estimates (not values in the data table). Show work. State your results clearly — the target temperature and the associated time(s). Show work.
Please see the Technology Tips topic for additional information about generating the scatterplot and quadratic polynomial.
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MATH 107 4060 College Algebra (2145)
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