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Gunasekara & Dharmawardane
5 4 3 2
f(x) = p1x + p2x + p3x + p4x + p5x + p6 p1 = 901.9 (-1879, 3683)
Where x is normalized by mean 5 and p2 = 2604 (29.76, 5179)
standard deviation 2.739. Coefficients (with Goodness of fit:
95% confidence bounds) are given below.
SSE: 3.511e+07
p1 =-105.2 (-601.2, 390.9)
p2 = 55.72 (-284.7, 396.3) R-square: 0.2529
Adjusted R-square: 0.1035
p3 = 32.26(-1060, 1705) RMSE: 2650
p4 =-54.16 (-826.1, 717.8)
p5 =-167.9 (-990.7, 665) According to the model 25.29%
p6 =179.9(-120.1, 479.9) variation can be explained. The following
Goodness of fit: figure illustrates deaths on house during the
SSE: 6.388e+04 period 2012 to September-2016.
R-square: 0.4014
Adjusted R-square: -0.5963
RMSE: 145.9
Coefficient of determination is 40.14%.
Monthly Guppy fish population for the year
2016 is given by the following figure.
Figure 3: Deaths on house
The total deaths on house throughout a
month are taken from the stock sheet to plot
monthly deaths on house in order to analysis
whether there is an increasing or decreasing
trend. According to the figure, relatively
Figure 2: Monthly Guppy fish population high DOH is recorded in February and
for the year 2016 March. It had come to notice that there was
th
a sudden power failure on 14 March 2016
The population of month January is which consequences the high DOH.
excluded as an outlier in Figure 2.
Comparatively the highest population is Moreover, it is expected to be opposite
recorded in June. The fitted linear model is of the population graph derived for the same
given by period, due to many factors however it has
not become opposite. One reason could be
f(x) = p1x+p2
local purchasing during the considered
Where x is normalized by mean 5 and period. Though, DOH is higher in a certain
standard deviation 2.16 and coefficients time, if the local purchasing quantity is also
(with95% confidence bounds) are given by higher during the same time, the expected
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