Applied Statistics and Forecasting: Relationship between Salary and Casual Factors

BE279 Applied Statistics and Forecasting

TABLE OF CONTENTS
INTRODUCTION.........................................................................................................................2
METHOOLOGY AND DATA.....................................................................................................2
ANALYSIS AND RESULTS........................................................................................................3
Task 1..........................................................................................................................................3
Task 2..........................................................................................................................................7
DISCUSSION...............................................................................................................................11
CONCLUSION............................................................................................................................12
REFERENCES............................................................................................................................14

INTRODUCTION
Statistical forecasting is mainly implies that the use of statistics based upon the historical
data of a project so that it will be easy to determine the same in future. This is mainly used in the
quantitative data in order to determine the interdependence between the variables. The present
study is also helpful to develop a deep understanding pertaining to statistics and tool used to
determine the answers. For that, there are two topics on which the entire study is based. such that
under first, study will determine the relationship between salary and other casual factors by using
an inferential tool. Thus, to check the autocorreltion, different test will be applied which include
Durbin-Watson test, residual analysis), collinearity (VIF), and error distribution. Moreover,
under topic 2, the study will examine how an innovation developed by the companies has change
through its casual factors and by using different inferential tool, the present study will explain the
results.
METHOOLOGY AND DATA
Research type: Quantitative research type has been adopted for the present study in
which numbers and figures has been considered in order to derive a valid outcome (Nayak and
Singh, 2021). In order to analyse the association between the variables, only quantitative
research type will be beneficial.
Research approach and philosophy: In accordance with the research type, deductive
approach and interpretivism philosophy has been adopted that assist to examine the relationship
between salary and casual factors effectively (Snyder, 2019). This in turn assist to determine the
valid outcome and answer the research questions as well.
Data collection: The entire study is based upon secondary data collection methods in
which responses have been gathered from previously conducted research so that the relationship
can be identified. In this, 787 people has been interviewed and this is further supported by
different inferential tool so that effective outcome can be generated (Mohajan, 2018). Along with
this, under discussion chapter, the sources has been further selected that helps to support the
results so that effective outcome can be generated.

Data analysis: In order to analyse the data effectually, SPSS software has been used
which help to ascertain the hypothesis and formulate the results (Pandey and Pandey, 2021). In
this, regression and anova as an inferential tool will be used to determine an association and
further with the help of table and graphs the report will present the data in an effective manner.
ANALYSIS AND RESULTS
Task 1
Null hypothesis (H0): There is no significant relationship between casual factors (publication,
position and university) upon likelihood of faculty salary.
Alternative hypothesis (H1): There is a significant relationship between casual factors
(publication, position and university) upon likelihood of faculty salary.
Model Summaryb
Model R R Square Adjusted R
Square
Std. Error of
the Estimate
Durbin-Watson
1 .659a .434 .428 20677.910 1.396
a. Predictors: (Constant), prof, osu, female, pubindx, assist, assoc
b. Dependent Variable: salary
ANOVAa
Model Sum of Squares df Mean Square F Sig.
1
Regression 185458035781.
828 6 30909672630.3
05 72.290 .000b
Residual 241580416113.
242 565 427575957.723
Total 427038451895.
071 571
a. Dependent Variable: salary
b. Predictors: (Constant), prof, osu, female, pubindx, assist, assoc
Coefficientsa