### Distributed Solar: The Democratizaton of Energy

### Blogroll

- South Shore Recycling Cooperative
- What If
- Why "naive Bayes" is not Bayesian
- American Association for the Advancement of Science (AAAS)
- Dr James Spall's SPSA
- Earle Wilson
- John Kruschke's "Dong Bayesian data analysis" blog
- James' Empty Blog
- OOI Data Nuggets
- Mark Berliner's video lecture "Bayesian mechanistic-statistical modeling with examples in geophysical settings"

### climate change

- Social Cost of Carbon
- Ellenbogen: There is no Such Thing as Wind Turbine Syndrome
- HotWhopper: It's excellent.
- ATTP summarizes all that stuff about Committed Warming
- Paul Beckwith
- Climate impacts on retail and supply chains
- Équiterre
- weather blocking patterns
- History of discovering Global Warming
- Eli on the spectroscopic basis of atmospheric radiation physical chemistry

### Archives

### Jan Galkowski

# Category Archives: statistical regression

## “Lockdown WORKS”

Originally posted on Open Mind:

Over 2400 Americans died yesterday from Coronavirus. Here are the new deaths per day (“daily mortality”) in the USA since March 10, 2020 (note: this is an exponential plot) As bad as that news is,…

## What happens when time sampling density of a series matches its growth

This is the newly updated map of COVID-19 cases in the United States, updated, presumably, because of the new emphasis upon testing: How do we know this is the recent of recent testing? Look at the map of active cases: … Continue reading

Posted in American Association for the Advancement of Science, American Statistical Association, anti-intellectualism, anti-science, climate denial, corruption, data science, data visualization, Donald Trump, dump Trump, epidemiology, experimental science, exponential growth, forecasting, Kalman filter, model-free forecasting, nonlinear systems, open data, penalized spline regression, population dynamics, sampling algorithms, statistical ecology, statistical models, statistical regression, statistical series, statistics, sustainability, the right to know, the stack of lies
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## “Code for causal inference: Interested in astronomical applications”

via Code for causal inference: Interested in astronomical applications From Professor Ewan Cameron at his Another Astrostatistics Blog.

Posted in American Association for the Advancement of Science, American Statistical Association, astronomy, astrostatistics, causal inference, causation, counterfactuals, epidemiology, experimental design, experimental science, multivariate statistics, prediction, propensity scoring, quantitative biology, quantitative ecology, reproducible research, rhetorical mathematics, rhetorical science, rhetorical statistics, science, statistical ecology, statistical models, statistical regression, statistics
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## Reanalysis of business visits from deployments of a mobile phone app

Updated, 20th October 2020 This reports a reanalysis of data from the deployment of a mobile phone app, as reported in: M. Yauck, L.-P. Rivest, G. Rothman, “Capture-recapture methods for data on the activation of applications on mobile phones“, Journal … Continue reading

Posted in Bayesian computational methods, biology, capture-mark-recapture, capture-recapture, Christian Robert, count data regression, cumulants, diffusion, diffusion processes, Ecological Society of America, ecology, epidemiology, experimental science, field research, Gibbs Sampling, Internet measurement, Jean-Michel Marin, linear regression, mark-recapture, mathematics, maximum likelihood, Monte Carlo Statistical Methods, multilist methods, multivariate statistics, non-mechanistic modeling, non-parametric statistics, numerics, open source scientific software, Pierre-Simon Laplace, population biology, population dynamics, quantitative biology, quantitative ecology, R, R statistical programming language, sampling, sampling algorithms, segmented package in R, statistical ecology, statistical models, statistical regression, statistical series, statistics, stepwise approximation, stochastic algorithms, surveys, V. M. R. Muggeo
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## A quick note on modeling operational risk from count data

The blog statcompute recently featured a proposal encouraging the use of ordinal models for difficult risk regressions involving count data. This is actually a second installment of a two-part post on this problem, the first dealing with flexibility in count … Continue reading

Posted in American Statistical Association, Bayesian, Bayesian computational methods, count data regression, dichotomising continuous variables, dynamic generalized linear models, Frank Harrell, Frequentist, Generalize Additive Models, generalized linear mixed models, generalized linear models, GLMMs, GLMs, John Kruschke, maximum likelihood, model comparison, Monte Carlo Statistical Methods, multivariate statistics, nonlinear, numerical software, numerics, premature categorization, probit regression, statistical regression, statistics
Tagged dichotomising continuous variables, dichotomizing continuous variables, premature categorization, splines
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