Ccst 9039 – Statistic Assignent
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CCST 9039 Assignment 2Name: Zhou ShujieUID: 3035449308Original data used:
Cons: it only depends on certain numbers in a series, other changes in data, for example, the second number in 2017 and 2018 separately change to 14.4 and 8.0, will not be showed using the five-number summary, limiting its representativeness.Histogram:  Pros: visualizing the distribution and difference in frequency in data series. And it can be used under many situations.  Cons: the judgement can be influenced by the choice of class width, increasing the difficulty to use compared to methods above. Besides, some of the information may be excluded in a histogram since the histogram can not show the exact number in a data series. It only involve frequency in each interval.(e):These time plots mainly display not only the exact numbers but also consistent changes in minimum temperatures in January in both years. We can analysis and compare the degree of volatility of data from the time plots. Additionally, these time plots tell us the changing trend of data.[pic 3][pic 4](f):22nd in 2018:When n = 1 Prediction = 16.8 Prediction error = 17.1-16.8 = 0.3When n = 5 Prediction = (15.9+15.9+17.9+17.4+16.8)/5 = 16.78          Prediction error = 17.1-16.78 = 0.32Using the same method, we get:When n = 1, prediction errors are:(22nd ): 0.3  (23rd): 0.1  (24th): 0.6  (25th): 0.5  (26th): 0.5  (27th): 1.6  (28th): 1.4  (29th): 3.7  (30th): 0  (31st): 1.1  Mean = (0.3+0.1+0.6+0.5+0.5+1.6+1.4+3.7+0+1.1)/10 = 0.98When n = 5, prediction errors are:(22nd): 0.32  (23rd): 0.18  (24th): 0.68  (25th): 0.92  (26th): 1.16  (27th): 2.52  (28th): 3.3  (29th): 6.08  (30th): 4.54  (31st): 4.2Mean = (0.32+0.18+0.68+0.92+1.16+2.52+3.3+6.08+4.54+4.2)/10 = 2.39n=1 gives me the most accurate prediction on average.Original data used: