Notice you do not have enough points to make a cubic regression.
Linear Model: y=-0.057x+19.93 Quadratic Model: y=-0.025x^2+0.14x+19.595 Best Fit: The quadratic model better fits the data set.
Practice makes perfect
To know which models to use to approximate the data, we will begin by plotting the values using our graphic calculator. Let's consider x to be the month and y to be the products supplied.
x
Products Supplied
2
19.782
4
19.768
6
19.553
Now we push STAT, choose Edit, and enter these values.
Once the values have been entered we can plot them by pushing 2nd and Y= and choosing one of the plots in the list. Make sure you turn the plot ON, choose scatterplot as the type, and use L1 and L2 as XList and YList. Finally, you can pick whatever mark you want.
By pushing GRAPH the calculator will plot the data set. A standard viewing window might not show the data points, so we can change it if necessary.
It looks like the number of barrels of crude oil and petroleum supplied per day can be approximated either by using a linear or a quadratic regression.
Performing Regressions
By pressing STAT we can find the linear regression under the CALC menu. If we choose LinReg the calculator performs a linear regression test. One line below in the list we can find QuadReg, which performs a quadratic regression.
Which Model is Best?
To determine which model is best, we will plot the results we got and examine how they fit the data.
We can see that the quadratic regression fits the data set perfectly. Therefore, we will choose the quadratic model as the one that represents the data set.
Extra
Note
Here, we didn't try to make a cubic regression because we have less data than is necessary.
