Being able to predict the weather can save lives as people prepare for tornados or floods. Knowing when a drought will come, or when high temperatures will scorch the land could save crops and keep our children from danger at school. I plan to use this dataset to develop an improved method of predicting temperature so that weatherpersons can better prepare us for the future. There are many variables measured, some of which are useful (like funding) and some which surprisingly enough are useless (like pressure changes). My model will show that I can predict the temperature to within the nearest 176 degrees.
I found the following errors using the program R by…..
Clearly these are mistakes and not real data points, so I deleted them. This shouldn’t be a problem overall since I have a monstrous amount of data.
Looking at the temperature scores I can see they tend to range from 0 to 8000 degrees. This seems hot to me, but what do I know? The average of the data set had an average of 2784 degrees which means the true average temperature will be between 2750 and 2817 degrees with 95% confidence.

I examined the variables used for the data, and I noticed that the residuals for clouds had a rainbow style curve. For this reason I decided to use clouds 2 in my model. Funding had a very strange residual plot. I determined that the appropriate model would have Funding logged as well as funding 2 and funding 3 . I did not include funding in the model because the p-value was greater than my alpha of 0.05. Windspeed also had a very strange residual, so I logged and included the cubic term. I do not have the linear or quadratic terms because the p-values were larger than 0.05. The graph of snowmelt showed exponential growth, so I used a log term. I searched for categorical interactions, but only system ended up being significant. I also searched for interaction terms, especially between windspeed and snowmelt because their residual plots were so complicated, but those interaction terms were also not significant.
In the end my residual plots all look randomly scattered, my residuals are normal, and the pvalues are all small which indicates I have an appropriate model for predicting the temperature.
I can predict the temperature with the following equations:
If the system is Low:

Temperature = -1049786.1520+0.0967*Altitude-
48.6221*Clouds+27.2774*GroundLevel+34.9364*PercentRecorded+
1297.6498*Polarvortex+104328.6856*Funding-127.5957*log(Snowmelt)+
0.1417*Clouds
2
-3434.2746*Funding
2
+37.5814*Funding
3
-1305.0641*log(Windspeed)-13.2726*Windspeed
3
If the system is High:
Temperature = -1049888 +0.0967*Altitude-
48.6221*Clouds+27.2774*GroundLevel+34.9364*PercentRecorded+
1297.6498*Polarvortex+104328.6856*Funding-127.5957*log(Snowmelt)+
0.1417*Clouds 2 -3434.2746*Funding 2 +37.5814*Funding 3
-1305.0641*log(Windspeed)-13.2726*Windspeed
3
If the system is Top:
Temperature = -1049835 +0.0967*Altitude-
48.6221*Clouds+27.2774*GroundLevel+34.9364*PercentRecorded+
1297.6498*Polarvortex+104328.6856*Funding-127.5957*log(Snowmelt)+
0.1417*Clouds 2 -3434.2746*Funding 2 +37.5814*Funding 3
-1305.0641*log(Windspeed)-13.2726*Windspeed
3
These prediction equations will predict to within about 176 degrees. It might be off by as much as 352 on rare occasions.
Here are some things I noticed about what affects temperature.
When the system changes from Low to High the temperature drops 101°
When the system changes from Low to Top the temperature drops 48°
If the altitude increases by 1 then the temperature climbs 0.09
° on average
As cloud increases the temperature drops until about 175 where the temperature will actually start increasing again at a rapid rate. This can be seen in the picture below:
As ground level increases the temperature increases by 27 per increase.

As percent recorded goes up by 1 the temperature increases by 35
As the polar vortex increases the temperature climbs by 1300 degrees and we all die
As funding increases the change in temperature will be to increase until about 29 then decrease until 32, then increase again. You can see this in the plot below
As snowmelt increases by 1% the temperature decreases by 1.2 degrees. This is shown below:
As windspeed increases the temperature drops as you can see in the graph below
So if you want a warm day you should spend a lot on funding, have a high polar vortex, and high altitude. To a lesser degree it would also help to have a low system, lots of clouds, and not much snowmelt. It should be noted that air pressure, radiation, smog, and animal behavior were not helpful in predicting the temperature.
In conclusion I have serious concerns about the high values of the temperatures in this study. Where were you, on the sun? It is clear that high funding has a strong effect, although I have no idea why this might be without Stephanie. Also clouds was surpringly effective. Since I don’t know what a polar vortex
I don’t know what to tell except please do not burn the earth to cinders. I think in the future you should design a realistic study, and include variables like air pressure and season.
With this study we can avert horrible natural disasters and save people lives by predicting the weather well. Also this may mean poep0oe will want to watch the news again.