Data Quality and Regression Bias

The example of US maize yields represents perhaps the most accurate long (50+ years) time series available on both crop yield and climate anywhere in the world. In many countries of prime interest for food security, the quality of data can be considerably worse. The crop production database of the Food and Agriculture Organization of the United Nations (FAO), for instance, contains an enormous wealth of information but much of it is visibly suspect. Reported yields are often identical for 3 or more years in a row, and areas can change dramatically in a single year. Errors in the response variable tend to inflate the standard error of coefficients in a regression model, but as long as the errors are random they should not introduce bias into the estimation procedure (Chatfield 1996). Errors in the predictor variables - in our case climate measurements - are a more serious concern because they tend to bias the coefficients towards zero. This phenomenon is known as regression bias, and though several methods exist to attempt to correct for it (Frost and Thompson 2000) its effects are often not well understood.

Renewable Energy 101

Renewable Energy 101

Renewable energy is energy that is generated from sunlight, rain, tides, geothermal heat and wind. These sources are naturally and constantly replenished, which is why they are deemed as renewable. The usage of renewable energy sources is very important when considering the sustainability of the existing energy usage of the world. While there is currently an abundance of non-renewable energy sources, such as nuclear fuels, these energy sources are depleting. In addition to being a non-renewable supply, the non-renewable energy sources release emissions into the air, which has an adverse effect on the environment.

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