The total sum of squares and the total degrees of freedoms are disaggregated by calculating in sample variance and "between" sample variance and their respective degrees of freedoms. It is demonstrated numerically that both these measures add up to the total sum of squares and the total degrees of freedom.
The sum of squares and degree of freedom calculation from the previous videos are put into a ratio to calculate the F Value, on whose basis the null hypothesis is confirmed or rejected. If variance is higher between samples than within the null hypothesis is more likely to be rejected. The results of a numerical example are interpreted more abstractly and then tested with regards to a confidence interval and the corresponding F table.
The definition of a chi-square distribution is given. Chi-square is defined as the sum of random normally distributed variables (mean=0, variance=s.d.=1). The number of added squared variables is equal to the degrees of freedom. With more degrees of freedom the probability of larger chi-square values is increased.
As opposed to the conventional over-simplified assumption of self-interested individuals, strong evidence points towards the presence of heterogeneous other-regarding preferences in agents. Incorporating social preferences – specifically, trust and reciprocity - and recognizing the non-constancy of these preferences across individuals can help models better represent the reality.
What’s inflation? Why is it relevant? And is there an agreed theory about its roots and causes, or is it a contentious concept? That’s what this text is all about: We define what inflation actually means before we delve into the theoretical debate with an interdisciplinary and pluralist approach: What gives rise to it, what factors might influence it, and, consequently, what might be done about it?