Generate 100 random data points from some hypothetical climate variable x~N(2,2) - in other words, from...

 

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Generate 100 random data points from some hypothetical climate variable x~N(2,2) - in other words, from a normal distribution with u = 2 and o = 2. Now suppose there is an impact- relevant variable³ that is related to the climate variable via the following non-linear function: y = x³. Calculate values of y from all the values of x, and then calculate the expected value of y (i.e. E(y)) • Calculated the expected value of x (i.e. E(x)), and then calculate the expected value of y from this value(i.e. E(y) = [E(x)]³). Question 2a (0.5%). Comment on whether the two different ways of calculating E(y) yield the same or different answers. Given that most real-world systems have a non-linear relationship with climate (or, indeed, with other drivers of risk), what does this suggest about the manner in which climate projections should be summarized to support impact modelling? Generate 100 random data points from some hypothetical climate variable x~N(2,2) - in other words, from a normal distribution with u = 2 and o = 2. Now suppose there is an impact- relevant variable³ that is related to the climate variable via the following non-linear function: y = x³. Calculate values of y from all the values of x, and then calculate the expected value of y (i.e. E(y)) • Calculated the expected value of x (i.e. E(x)), and then calculate the expected value of y from this value(i.e. E(y) = [E(x)]³). Question 2a (0.5%). Comment on whether the two different ways of calculating E(y) yield the same or different answers. Given that most real-world systems have a non-linear relationship with climate (or, indeed, with other drivers of risk), what does this suggest about the manner in which climate projections should be summarized to support impact modelling?

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