Comparison of Fuel Consumption Based on Engine Horse Power and Number of Cylinders

Business research Methodology
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12th May 2019

Research question 1
Comparison of fuel consumption based on engines horse power
Technique to be used
We are going to use Pearson correlation test. This is based on the fact that we need to test for the
relationship between two continuous variables (fuel consumption and the engines horse power).
The test will reveal whether the two variables are correlated and we can also tell the direction
and strength of the relationship between these two variables using the proposed Pearson
correlation test (Nikolić, Muresan, Feng, & Singer, 2012).
Setting the hypothesis test
In this section, we sought to test whether the average fuel consumptions varies based on the car’s
engine horse power. The research question we sought to answer is whether there is significant
relationship between fuel consumption and the engines horse power. The following is the
hypothesis to be tested;
Null hypothesis (H0): There is no significant relationship between fuel consumption and the
engines horse power.
Alternative hypothesis (HA): There is significant relationship between fuel consumption and the
engines horse power.
This hypothesis was to be tested at 5% level of significance using Pearson correlation test.
Assumptions needed to use Pearson correlation
The following are the assumption needed to perform Pearson correlation test;

i) The levels of measurement of each variable should be continuous
ii) Absence of outliers in the dataset
iii) Normality of the variables. The variables need to follow a normal distribution.
iv) Linearity of the variables. The variables need to be linear
v) Related pair; each subject need to a pair of values.
Checking if the assumptions are met
i) The levels of measurements for both the two variables are continuous. This means
that the assumption on level of measurement is met for both the two variables.
ii) Linearity of variables. This assumption has been met since both the two variables are
linear.
iii) Related pair. The assumption on related pair was met since each subject had a pair of
values related to it.
iv) Test of normality
Results on normality test is presented below;
Table 1: Tests of Normality
Kolmogorov-Smirnova Shapiro-Wilk
Statistic df Sig. Statistic df Sig.
GallonsPer100Miles .114 392 .000 .943 392 .000
Hores Power of the
Engine
.164 392 .000 .904 392 .000
a. Lilliefors Significance Correction
From the results (considering either Shapiro-Wilk test or Kolmogorov-Smirnov test) it is
clear that both the two variables are not normally distributed (p < 0.05). This means that
the assumption on normality is violated.

v) Test for the presence of outliers