The two most interesting visualization are the wordclouds of three words phrases. Biden’s is first and then Trump’s.
Sentiment analysis showed only minor differences. However, the wordcloud’s show very different focus for both. Biden’s is characterised by more positive statements about issues and the need to defeat Trump. Trump’s on the other hand shows a strong focus on his slogans e.g. Corrupt Joe Biden.
Shows the proportional effect on Covid Deaths. Above the line shows greater than expected based on state population proportion, below shows less than expected.
The covid-19 death rate in the US is shown to have a statistically significant difference between between Afro-American (AA) and White populations. The graph below shows the AA proportion difference compared to the W proportion below the x=0 line. There is a clear difference across nearly all states.
The full outline, R program and data file are available at ameyenn/covid19
The PCA biplot confirms the difference. The vectors show a negative relationship between AA and W, AA is linked with the poverty vector as well. NY is the main outlier.
A basic cluster plot across all the data is shown below.
A t-test was performed with the following results, indicating a statistically significant difference.
Welch Two Sample t-test
data: dt[, 1] and dt[, 2] t = 3.7143, df = 80.722, p-value = 0.0003739 alternative hypothesis: true difference in means is not equal to 0 95 percent confidence interval: 0.3026568 1.0010984 sample estimates: mean of x mean of y 2.688740 2.036862
Australian sample is randomly chosen from 720 school forming a sample of 14,000 students. China sample is not random. It is skewed to the brightest students across 4 provinces nominated by the Chinese Government. This is the exception all other countries are sampled randomly.
The above shows the trend using the full Y-axis rather than exaggerating the decline by using a restricted access.
Climategate – as I understand it this is about the statistical techniques (trick) used to compare data sources and to ‘hide the decline’ of tree ring data from trees in Siberia after 1960 which were not consistent with other worldwide data. There have been plenty of enquires which all clear scientists of wrongdoing.
My conclusion is that there is not much that I can find to support the view expressed in a range of sites that the scientists were involved in a cover up.
Removal from Journal Editorial Boards or paper rejection
I am concerned about the claim that climate science can move into activist territory and be biased – there needs to be evidence, is there hard evidence that this is preventing contrary views being expressed?
On this brief review, I can’t find any evidence of a hoax, I find disputes, attempts to set record straight and to explain, possible confusion, accusations etc. all of which are classic in a complex change process.
The graphs below show the 1999 published data from NASA. On left there seems to be no pattern on the right the global temperature anomalies show an increasing trend. Anomalies are measured by taking the difference between yearly temperatures and the 30 year average, see the Y-axis, 1.0 means that the yearly average was 1degree hotter than the 30 year average. In the US the temperatures in the 1930s were very hot over a wide spread area, this is the dust bowl period.
The NASA 2017 graph shows the same data but has the data
from 2000 to 2018 shown and a statistical trend line.
Humansarefree.com make the claim that ‘this is significant scientific fraud’, there is no evidence for this and the trend shown should give rise to concern.
Heatwave Index
This graph is used to state that there is no discernable pattern
of temperature and that it was hotter in the 30s.
The Y-axis is not easy to interpret, but bear in mind this graph shows HEATWAVE conditions only, defined to be above the 10 year average over four consecutive days. The graph does not enable you to say if the average temperature is increasing or is the frequency of hot days increasing.
The graph is for temperature data across the US, the data shown on the following graphic is restricted to US cities.
This graph below shows the change in hot days and hot nights shown as a percentage of land areas.
How do we interpret the graph? Hot nights are on the increase, there is recent dip in day time temperature coverage of land. The trend line is upwards.
Finally, the clam 30,000 scientists are against climate
change dates to a petition circulating since 1998, there are numerous fact
checks on this and I feel at the minimum such criticism ought to be taken into
account before making such claims. Certainly they made Hoax claim.
In conclusion, the data and discussion on the Humansarefree
is misleading an does not attempt a sound analysis.
populations. The graph below shows the AA proportion difference compared to the W proportion below the x=0 line. There is a clear difference across nearly all states.
