Statistical Analysis of Two Variables: Correlation and Covariance

This report presents a statistical analysis focusing on correlation and covariance. It begins with an analysis of covariance, examining the relationship between job satisfaction before training and life happiness, revealing a positive correlation. The report then investigates the relationship between years of experience and salary using correlation analysis, indicating a very low correlation between these two variables, suggesting minimal impact of experience on salary. The report provides interpretations of the statistical findings, explaining the implications of the calculated values and their meanings in the context of the variables being analyzed.

Statistic

Table of Contents
1.Covariance calculation and assessing whether variable moving in same direction.................3
2. Analysing correlation and relationship between two variables...............................................3

1.Covariance calculation and assessing whether variable moving in same direction.
Particulars Figures
Co-variance 0.23
Satisfaction before mean 2.1
life mean 4.67
Interpretation:
Covariance is a statistical tool which depicts the relationship factor of two variables
because of change in one variable factor. It helps in measuring the degree of change in one
variable which arises as a result of change in the second variable factor. The present value of
covariance between 'Job satisfaction score before training' and 'life happiness score' is 0.23. This
value of covariance indicates that there is a positive relationship between two factors. Both the
factor are positively related and moving in the same direction. Increase in 'Job satisfaction score
before training' is bringing change in 'life happiness score as well in the same direction. With the
increase in satisfaction level of job, it is leading to more happiness in the life as well.
2. Analysing correlation and relationship between two variables.
Null hypothesis (H0): There is no significant correlation between years of experience and salary.
Alternative hypothesis (H1): There is a significant correlation between years of experience and
salary.
Particulars
Years of
experience
Salary
(000)
Years of
experience 1 0.003
Salary(000) 0.003 1
Interpretation:
Correlation measures the relationship between two and more variable factors. It studies
the impact of change in one variable factor on the another variable factor. In the above table,
correlation value assessed is 0.003. With the help of this value, it can be evaluated that there is

very low correlation between factors viz. 'Years of experience' and 'Salary'. It can also be said
that these two factor are having no relationship between them. It means that as the person or
individual employee is gaining more and more experience across the years in life, it is bringing a
very minimum or low change in the level of salary. The value of correlation between 'years of
experience' and 'salary' is 0.003 from which it can be depict that there is absence of relationship
between these factors. These two variable factors are not depending on each other for bringing in
changes in their own.