Line of Best Fit: y=-0.76x+69.81 Correlation: r=-0.984, it is very strong. Average Temperature in Kuwait City: 48^(∘)F
Practice makes perfect
To find the line of best fit, we have to perform a linear regression analysis. There are a few steps that are necessary to take in order to properly instruct the calculator on what to do.
Enter the Data Set into Lists
The first step is to enter the data points into the calculator. We can do this by pressing STAT and then choose the first alternative, Edit, in the menu that's presented to us.
Having done this, we will see a number of columns marked L1, L2, and L3.
Using the keypad, we enter our data sets into the first two lists. The first lists will contain the latitudes, and the second list will have the average temperatures. Note that we will leave out Kuwait City, Kuwait, as we do not know it's average temperature.
Making a Scatter Plot
To make a scatter plot of the data, press 2nd, then Y=, and choose a plot. In the plot menu, turn on the scatter plot, choose L1 and L2 as your x- and y-lists, and choose an appropriate mark for the data points.
Before pressing graph, you might want to change the Window so it fits your data. To do that you press WINDOW.
Finally, by pressing GRAPH we can plot our values.
Calculate Linear Regression
Once the values are entered, we can perform a linear regression. Press STAT and use the keypad to choose the CALC menu. Here you have all of the available types of regression analyses.
By choosing the fourth option, LinReg ax+b, the calculator performs a linear regression analysis on our data set.
The line of linear regression is y=-0.76x+69.81. We can also see that it has a correlation coefficient of - 0.984, which means that the line fits the data very well. Let's graph this line over the scatter plot.
Estimate the Temperature
To estimate the average temperature of Kuwait City, we can substitute the latitude of this city into our line of best fit and calculate the corresponding y-value.
