Download presentation

Presentation is loading. Please wait.

Published byPeter Belvin Modified over 2 years ago

1
GLOBAL WARMING AND HURRICANE CORRELATION BY THE SHARK TEAM BY THE SHARK TEAM

2
Null Hypothesis There Is No Correlation Between Global Warming And Hurricane Frequency And IntensityThere Is No Correlation Between Global Warming And Hurricane Frequency And Intensity

3
Global Warming Indicator Average global temperature deviation data from 1899 until present is used as the global warming indicator in all correlations and statistical analysis.Average global temperature deviation data from 1899 until present is used as the global warming indicator in all correlations and statistical analysis. We consider the signature of global warming to be present in the temperature data from 1973 until 2005.We consider the signature of global warming to be present in the temperature data from 1973 until 2005.

4
Hurricane Frequency The data shows a relative steady frequency of hurricanes until a distinct increase in the last decadeThe data shows a relative steady frequency of hurricanes until a distinct increase in the last decade

5
Hurricane Pressure …and also a substantial decrease in the average hurricane pressure…and also a substantial decrease in the average hurricane pressure

6
Analysis Procedures ANOVA between the last two decades of hurricane frequency looking for a significant differenceANOVA between the last two decades of hurricane frequency looking for a significant difference Correlation between average global temperature deviation from 1899 and hurricane frequencyCorrelation between average global temperature deviation from 1899 and hurricane frequency Correlation between temperature from 1987 until present with the hurricane frequency Correlation between temperature from 1987 until present with the hurricane frequency

8
This graph shows… Average hurricane strength as measured by category has not changed much over this time span.Average hurricane strength as measured by category has not changed much over this time span. However, there is a sharp increase in hurricane frequency after 1994 after a long period of downward trend.However, there is a sharp increase in hurricane frequency after 1994 after a long period of downward trend.

9
ANOVA analysis We attempted to quantify this change in frequency using an ANOVA between the years ’82- ’94 and ’95-’05.We attempted to quantify this change in frequency using an ANOVA between the years ’82- ’94 and ’95-’05.

10
ANOVA Results ’82-’94 yields an average of 3.6 hurricanes per year. ’95-’05 has an average of 7.85.’82-’94 yields an average of 3.6 hurricanes per year. ’95-’05 has an average of 7.85. The difference in means was significant with p=.017.The difference in means was significant with p=.017.

11
ANOVA interpretation This means that there is a significant change in frequency in the last 10 years compared to the previous 10.This means that there is a significant change in frequency in the last 10 years compared to the previous 10.

12
Regression Since we are using global mean temperature as our measure of global warming, it seems logical to look for a correlation between the temperature and hurricane frequency.Since we are using global mean temperature as our measure of global warming, it seems logical to look for a correlation between the temperature and hurricane frequency.

13
Regression Analysis To this end, we ran two regressions.To this end, we ran two regressions. The first was for the all the data, the second was from ’73 on.The first was for the all the data, the second was from ’73 on.

15
First regression The first regression yielded an F-score of 39.1 with 105 degrees of freedom. This yields a p-value of 9e-9, which is very highly significant.The first regression yielded an F-score of 39.1 with 105 degrees of freedom. This yields a p-value of 9e-9, which is very highly significant.

16
But… Obviously, there are residuals about the linear fit that are non-random, especially a clump around 0 on the X-axis.Obviously, there are residuals about the linear fit that are non-random, especially a clump around 0 on the X-axis.

18
Explanation… If you look at the first graph, we can see that hurricane frequency has a peak that corresponds with about a fifteen year lag behind the global temperature.If you look at the first graph, we can see that hurricane frequency has a peak that corresponds with about a fifteen year lag behind the global temperature.

20
More explanation… This lag means that for any change in our X value (temperature), there will be a time of about 15 years before our Y values change, which will cause a clump in the data.This lag means that for any change in our X value (temperature), there will be a time of about 15 years before our Y values change, which will cause a clump in the data.

21
This means… There is about a fifteen year lag behind the global warming signal.There is about a fifteen year lag behind the global warming signal. Which means that the system hasn’t fully responded to the increase in global temperature.Which means that the system hasn’t fully responded to the increase in global temperature.

22
More meaning… The data shows an approximately stable slope in temperature increase over the last 20 years. Running a regression with hurricane frequency should yield a good linear model with some predictive power for future hurricane frequency for the next 15 years.The data shows an approximately stable slope in temperature increase over the last 20 years. Running a regression with hurricane frequency should yield a good linear model with some predictive power for future hurricane frequency for the next 15 years.

23
’87 on Temp/Freq. Reg.

24
Prediction Using a least squares fit the the temperature data from ’73-’05, we get a prediction of.77 degrees from the 1899 mean temp and a prediction of about 15 hurricanes for 2020 up from 14 in 2005.Using a least squares fit the the temperature data from ’73-’05, we get a prediction of.77 degrees from the 1899 mean temp and a prediction of about 15 hurricanes for 2020 up from 14 in 2005.

25
THE END In conclusion, the analysis of the hurricane data and global temperature—allows us to reject our null hypothesis that the two variables aren’t correlated.In conclusion, the analysis of the hurricane data and global temperature—allows us to reject our null hypothesis that the two variables aren’t correlated.

26
But… The correlations also have a high standard errors in our slope which when factored in give a 95% confidence interval for hurricane frequency of –15 to 52. Obviously, this range is not physical, which leads us to conclude:The correlations also have a high standard errors in our slope which when factored in give a 95% confidence interval for hurricane frequency of –15 to 52. Obviously, this range is not physical, which leads us to conclude:

27
More but… 1) We used a poor proxy for global warming1) We used a poor proxy for global warming oror 2) There hasn’t been enough time for the last uptick of temperature to show in the frequency of hurricanes.2) There hasn’t been enough time for the last uptick of temperature to show in the frequency of hurricanes.

Similar presentations

OK

1 Multiple Regression A single numerical response variable, Y. Multiple numerical explanatory variables, X 1, X 2,…, X k.

1 Multiple Regression A single numerical response variable, Y. Multiple numerical explanatory variables, X 1, X 2,…, X k.

© 2017 SlidePlayer.com Inc.

All rights reserved.

Ads by Google

Ppt on pay per click Service marketing ppt on hotel industry Ppt on 3g mobile technology Ppt on bluetooth architecture images Ppt on varactor diode applications Ppt on seven stages of life Ppt on female foeticide Ppt on punjabi culture in punjabi language Ppt on channel estimation Ppt on advertising and brand management