Authors: Sascha Vongehr
Comments: 8 pages, 2 figures, 26 references
Ashkenazim Jews (AJ) comprise roughly 30% of Nobel Prize winners, ‘elite institute’ faculty, etc. Mean intelligence quotients (IQ) fail explaining this, because AJ are only 2.2% of the US population; the maximum possible would be 13% high achievement and needing IQs above 165. The growing anti-Semitic right wing supports conspiracy theories with this. However, standard deviations (SD) depend on means. An AJ-SD of 17 is still lower than the coefficient of variation suggests, but lifts the right wing of the AJ-IQ distribution sufficiently to account for high achievement. We do not assume threshold IQs or smart fractions. Alternative mechanisms such as intellectual AJ culture or ethnocentrism must be regarded as included through their IQ-dependence. Antisemitism is thus opposed in its own domain of discourse; it is an anti-intelligence position inconsistent with eugenics. We discuss the relevance for ‘social sciences’ as sciences and that human intelligence co-evolved for (self-)deception.
It is very vital for suppliers and distributors to predict the deregulated electricity prices for creating their bidding strategies in the competitive market area. Pre requirement of succeeding in this field, accurate and suitable electricity tariff price forecasting tools are needed. In the presence of effective forecasting tools, taking the decisions of production, merchandising, maintenance and investment with the aim of maximizing the profits and benefits can be successively and effectively done. According to the electricity demand, there are four various electricity tariffs pricing in Turkey; monochromic, day, peak and night. The objective is find the best suitable tool for predicting the four pricing periods of electricity and produce short term forecasts (one year ahead-monthly). Our approach based on finding the best model, which ensures the smallest forecasting error measurements of; MAPE, MAD and MSD. We conduct a comparison of various forecasting approaches in total accounts for nine teen, at least all of those have different aspects of methodology. Our beginning step was doing forecasts for the year 2015. We validated and analyzed the performance of our best model and made comparisons to see how well the historical values of 2015 and forecasted data for that specific period matched. Results show that given the time-series data, the recommended models provided good forecasts. Second part of practice, we also include the year 2015, and compute all the models with the time series of January 2011 – December 2015. Again by choosing the best appropriate forecasting model, we conducted the forecast process and also analyze the impact of enhancing of time series periods (January, 2007 to December, 2015) to model that we used for forecasting process.