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First, we like to introduce our runway length data.
we could see the average is 3187.1. In these data, I want you to observe kurtosis and skewness the more. we are not familiar with them. Kurtosis is -0.22. It is low kurtosis, which means there are not lots of extreme values in the data. Skewness=0.099
It is positive skewness, which means the data is closed to the right side.

Next, we look at the Q-Q plot. we could see the points. The points closed to y=x
represented which is the normal distribution. Because we choose airports in random, that our data is the normal distribution. We used IBM SPSS to plot it.

And we see the temperature. The figure in our textbook. we could know low-density altitude (high air density) allows an airplane to lift the runway sooner and climb more steeply.

we know temp is related to buoyancy, and ro is a portion to inverse Temperature.
while the temperature gets higher, then buoyancy gets lower. That leads runway length longer to support airplanes take off. above all our temperature data is the average of 31 days near the hottest day each year.

we see the plotting and table. P-value is less than 0.05, meaning they are not from the same source.