Notice of RetractionStatistical analysis of categorical climatic variables: Case of temperature and rain fall

Notice of Retraction

After careful and considered review of the content of this paper by a duly constituted expert committee, this paper has been found to be in violation of IEEE´s Publication Principles.

We hereby retract the content of this paper. Reasonable effort should be made to remove all past references to this paper.

The presenting author of this paper has the option to appeal this decision by contacting TPII@ieee.org.

Climate as the general weather conditions in the air above the earth such as wind, temperature, humidity, rain... especially at a particular time over a particular area, it said that there is interdependence between those elements. In order to check out this connectivity, a statistical analysis between rainfall and temperature was carried out by analyzing the correlation and linear regression as well as testing the independence between rainfall and temperature. Working on three stations meteorological data, monthly recorded over a period of 5 years, our study has shown that there is no significant linear relationship between rainfall and temperature at confidence level α = 0.1. The statistical significance of the regression slope explains an insignificant amount of variation in the dependent variable and the slope coefficient is equal to zero. Finally, the independence test between rainfall and temperature has shown that population correlation coefficient ρ is not significantly different from zero which means that rainfall is independent from temperature. Our Statistical analysis of climatic data has concluded that temperature alone is not sufficient to explain the amount of rainfall for a given area meaning an inconsistent pattern of change in rainfall quantity as the temperature changes. So, this gives an idea that there are other factors apart from temperature that determine the amount of rainfall for a given area.