Statistics for Managerial Decision - Desklib

This document contains solved assignments and essays on Statistics for Managerial Decision. It includes relevant graphs, descriptive statistics, probability distributions, and normal probability plots for better understanding. It also provides 90% confidence intervals for different variables.

STATISTICS FOR MANAGERIAL DECISION
STUDENT ID:
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Question 1
(a) The required quarterly opening pricefor RMD and FPHand stem-leaf display
(b) Relevant graph
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(c) The relevant bar graph for six listed healthcare companies on ASX is shown as follows.
Relevant data for the above graph is December 31, 2018.
(d) A comparison between the two stocks ought to be drawn so as to select the better choice.
 There has been significant debt reduction during FY2018 for RMD owing to which
significant deleveraging has happened.
 Further, P/E ratio for RMD stock is 52.82 in comparison to FPH which trades at a P/E
ratio of 63.
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 Additionally, in the current year till date while FPH has given significant gains, RMD
has been rather subdued.
Considering the above factors, RMD seems to be a preferable bet considering scope for
future appreciation form a medium to long term perspective.
Question 2
(a) Required mean, median, Q1 and Q3
(b) Standard deviation, range and mean absolute deviation
(c) Box plot
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(d) The descriptive statistics in the context of weekly rent along with the box plots clearly
reflect the significant variation in trends observed in the various cities. The mean weekly
rent seem to be the most stable in Perth while they are the most volatile in Sydney as is
reflected from the dimensions of boxplot and standard deviation. Further, Sydney
commands a significant premium in terms of location over other cities. With regards to
listings in Airbnb, a huge variation was observed with regards to Sydney prices but the
average prices were lower than computed value using the given data.
Question 3
(a) Probability that selected field will be canola in Australia
P =2538678/ (4623527+2538678+371339+955321+11720277)=0.1256
(b) Probability that selected field will be wheat in NSW
= 9556517/ ((2755310+1201045+403121+483081+9556517)) =0.6637
(c) Yield would be determined by diving the production of crop with the area of field.
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(d) For the state of South Australia, grain sorghum estimates would be categorised as
unrealistic on account of the very high (in excess of 50%) relative standard error by ABS
in estimates.
Question 4
(a) Rainfall amount distribution given asPoisson.
(i) P (No rainfall)
(ii) P (X>= 3)
(b) Rainfall amount distribution given as Normal.
(i) P (10< X<50)
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(ii) Rainfall amount is X mm
Question 5
(a) The normal probability plots
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A linear trend is broadly adhered to by the normal probability, hence resting blood pressure
can be said to fit a normal distribution.
In the above normality plot, there is one outlier on the higher end with a corresponding z
value of +3. If that value is ignored, then a broad linear trend is adhered by the serum
cholesterol and it would be said to be normally distributed.
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A linear trend is broadly adhered to by the normal probability, hence maximum heart rate
achieved can be said to fit a normal distribution.
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For the given variables (i.e Old Peak), there is significant violation of the linear trend on
account of multiple outliers which tend to lie on both extremes. As a result, it would not be
correct to indicate that the given variable would be best represented by a normal distribution.
(b) 90% confidence interval for the various variables are highlighted as follows.
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Only those variables can allow differentiation between heart patients and non-heart patients
for whom the confidence interval for these two patient categories do not have a common
point. Analysing the four variables, the ones where overlapping is absent are mentioned
below.
 “Maximum Heart Rate Achieved”
 “Resting Blood Pressure”
 “Oldpeak”
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