Climate Change and Adaptation Data Analysis

Running head: CLIMATE CHANGE AND ADAPTATION DATA ANALYSIS
1
Climate Change and Adaptation Data Analysis
Name
Institutional Affiliation

CLIMATE CHANGE AND ADAPTATION DATA ANALYSIS
2
Data Analysis
There are various factors that leads to rise in sea level along the Queensland coastline.
This statistical analysis attempts to analyze the effects of global temperature change, changing
intensity of storm tides and cyclones and the changing rainfall pattern as factors that contribute
to the rise in sea level of the Queensland coastline. Temperatures and rainfall amounts are some
of the factors that affect changes in sea level that subsequently lead to flooding along the
Queensland coastline.
Rise in temperatures causes snow melt and weakening of water molecules causing
increase in volumes of the ocean water. As well, higher annual rainfall amounts also add to the
ocean water volume thus causing increased sea level. Tropical cyclones and storms may also
affect the structure of the coastline as they may erosion. To prove this the temperature and
rainfall data from the Queensland Gold coast sea way weather station were obtained and
analyzed to see how these factors are related to the rise in sea levels.
1. Analysis of number cyclones from 1995 – 2017 by simple linear regression
Summary descriptive statistics
Mean 9.863636364
Standard Error 0.600488454
Median 10
Mode 10
Standard Deviation 2.816540508
Sample Variance 7.932900433
Kurtosis 0.825747265

CLIMATE CHANGE AND ADAPTATION DATA ANALYSIS
3
Skewness -0.367581064
Range 12
Minimum 3
Maximum 15
Sum 217
Count 22
For the 22 years, it is evident that the mean number of tropical cyclones is about 10 with
a standard deviation of 3. The least experienced number of cyclones is 3 and the maximum is 15.
The skewness of -0.37 indicates that the data is fairly symmetrical
Summary output of Regression analysis
Regression Statistics
Correlation coefficient -0.56368693
R Square 0.31774296
Adjusted R Square 0.28363011
Standard Error 2.3838815
Observations 22
ANOVA

CLIMATE CHANGE AND ADAPTATION DATA ANALYSIS
4
df SS MS F
Significance
F
Regression 1 52.933088 52.933088 9.314465 0.0062933
Residual 20 113.65782 5.6828910
Total 21 166.59090
Coefficien
ts
Standard
Error t Stat P-value
Lower
99.0%
Upper
99.0%
Intercept 500.19762 160.662759 3.1133389 0.005477 43.057501 957.33775
X Var 1 -0.244494 0.08011067 -3.0519608 0.006293 -0.4724367 -0.0165526
There exists a medium negative relation of tropical cyclones across the years as shown by
the correlation coefficient of -0.56. There is about 28.4% variation explained by the change in
years that affect the number of tropical cyclones. In hypothesis testing, we test if the coefficient
of X variable 1 is zero. The null hypothesis is H0 = 0, and alternative hypothesis is H0 ≠ 0.
From the ANOVA test results, at 0.01 the significance level, the p value is considered
less than the alpha level hence the null hypothesis is rejected. We can conclude that the
coefficient (slope) of X variable is therefore significantly different from zero and thus there
exists a relationship between these two variables.