An article in Computers & Electrical Engineering ["Parallel Simulation of Cellular Neural Networks" (1996, Vol. 22, pp. 61–84)] considered the speedup of cellular neural networks (CNN) for a parallel general-purpose computing architecture based on six transputers in different areas. The data follow: 3.775302 3.350679 4.217981 4.030324 4.639692 4.139665 4.395575 4.824257 4.268119 4.584193 4.930027 4.315973 4.600101 (a) Is there evidence to support the assumption that speedup of CNN is normally distributed? Include a graphical display in your answer. (b) Construct a 95% two-sided confidence interval on the mean speedup. (c) Construct a 95% lower confidence bound on the mean speedup.

N=1,2,3,4,5 a n= 9,18,36,72,144 a recursive rule for the sequence is: f(1)= and f(n)= for n≥2. an explicit rule for the sequence is: f(n)= find a recursive rule and an explicit for the geometric sequence. 768,192,48,12,3,… a recursive rule for the sequence is: f(1)= and f(n)= for n≥2. an explicit rule for the sequence is: f(n)=∙ -1. concept 3: deriving the general forms of geometric sequence rules your turn use the geometric sequence to find a recursive rule and an explicit rule for any geometric sequence. 4,8,16,32,64,… recursive rule for the geometric sequence: f(1)= and f(n)=f(n-1)∙ for n≥2. recursive rule for any geometric sequence: given f(1),f(n)=f(n-1)∙ for n≥2. explicit rule for the geometric sequence: f(n)=∙-1. explicit rule for any geometric sequence: f(n)=∙-1 concept 4: constructing a geometric sequence given two terms your turn find an explicit rule for the sequence using subscript notation. the third term of a geometric sequence is 1/48 and the fifth term of the sequence is 1/432. all the terms of the sequence are positive. the explicit rule for the geometric sequence is