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This unavoidable and irreducible complexity means that schools are challenging place to study, to understand and to manage change within. Even for the teacher who spends everyday inside the school there is so much going on that unguided observations and the plans based upon them come with no guarantee of success. - We need a lens and a lever to manage this complexity. -  Such a lens is offered by the 'cultural forces'.

At the heart of mathematics are a set of connected thinking dispositions. The mathematician uses these dispositions as the cognitive tools of their trade. While the traditional imagining of mathematics might be all about the accurate application of well-rehearsed algorithms and processes, in the real world of mathematics, it is all about the thinking. As we consider what our students need from their mathematical education, we should not overlook the importance of these dispositions. 

After eight years of engaging our students with a Personal Passion Project during Term Four we shifted to a 'Genius Hour' model for 2015. In the end the results from the students were impressive but along the way some lessons were learned and we are looking forward to making some minor tweaks for 2016 that should further enhance the learning opportunities.

One of the most popular and effective multidimensional statistical approaches is regression analysis. It postulates the nature and extent of significant relationships between the variables in a data set. Once you understand how to apply regression, you can identify and determine the relationship between two or more variables. Regression methods help researchers make inferences and forecast future outcomes and behaviours. For example, sales forecasts and weather forecasts depend upon the regression analysis. However, there are some common mistakes that every statistician mistakes in regression methods. These mistakes compromise the validity of the results and lead to incorrect inferences and predictions. This article will tell you about the eight mistakes that every statistician makes in regression analysis.