Inferential Statistical Analysis Report - Year 2 Statistics Module

This report presents an inferential statistical analysis, examining the relationships between GDP, oil prices, interest rates, and inflation. The analysis utilizes questionnaires for data collection and applies statistical methods to assess the significance of various correlations. The report includes interpretations of coefficients, p-values, and R-squared values to determine the acceptance or rejection of null hypotheses. The findings suggest a negative correlation between oil prices and GDP, as well as between interest rates and GDP. The report also provides tables summarizing the coefficients, t-stats, and p-values for each variable, along with a chi-square test. The strengths of the analysis include the application of statistical methods for predictions and approximations of variables. The report references several sources including books and online resources.

Inferential Statistical Analysis 1
INFERENTIAL STATISTICAL ANALYSIS
Student ID Number
Module Code
Year Module Run
Assessment Title: Inferential Statistical Analysis

Inferential Statistical Analysis 2
Student A
Inferential Statistical Analysis
Method of data Collection
Questionnaires; were sent through the emails tom the participants and later returned by
through the stamped envelopes of the sampled population. The research team collected data
of the applied questionnaires and the participants responded by filling by filling in the
information.
Data Analysis
The approximated values for the coefficient of the values off p are significantly analysed by
using various relationships. Arguably the (H0) is termed as the Null hypothesis and if the
result is zero the hypothesis is rejected. Critically the results obtained from the information
provided it is argued that the association between the GDP and the prices of the oil are
assumed to be significant at the value that is less that 10% and the (P<0.1%) and the null
hypothesis is not accepted (Frost, 2017). However, the association existing between the GDP
and the oil prices show a negative association a significant increase in the prices of the oil
prices results to the significant decrease of the of the GDP by a value that is less than 0.03.
Fig 1.0
Relations Standard Coefficient DF
Real GDP
Oil prices
R value <0.05
0.05 ± 0.012
0.032 ± 0.013
0.3
5
Fig1

Inferential Statistical Analysis 3
Provided that the approximated value of the coefficients with the growth rates and the GDP
with the inflation are significant and the null hypothesis in this case is accepted. However
negative correlation show that the hypothesis is not accepted. Strengths The employment
rates are increasing, hence promising to solve the inflation crisis. The hypothesis is important
in approximations and predictions of the variables in the field of research and statistics
(Laerd, 2013).
Student B
Fig1.0 shows that the inflation influences the prices of the oil products at rate estimated as
0.015. The rest of the variables are kept constant for the analysis of the data information. An
increase on the oil products causes a decrease in the GDP while an increase in the rates of
interests results to the lowered GDP correspondingly. However, the value of the coefficient is
lowered by 0.05 meaning that the value is statically significant at the value of 0.05 and
(P<0.05). The R squared estimated at 58% showing a negative relationship among the
dependant and independents data variables (Eisenberg, 2011).
Student C
Table 1.2
Coefficients T-stats p-values
Constants 0.015 12.454 0.000
Oil -0.037 -4.565 0.003
Interest -0.012 -5.564 0.032
Inflation -0.004 -1.56 0.145
R2 58% - -
Result Critique analysis

Inferential Statistical Analysis 4
From table 1.2 it’s evident that R squared is at 58% and the above variables show non
uniform increase or decrease in the values with the T stats estimated as negative also the
values show probability being less than 0.5
Table 1.3
MOBILE
Chi-square 6.860
Asym Sig .032
a. cells with (0.0%) expected < 5 minimum value at 33.3
The chi-square estimated as 6.860 > 0.05 the null hypothesis was not accepted. Therefore the
probability not predicted for choosing either of the variables Collis & Hussey, 2013).

Inferential Statistical Analysis 5
References
Collis, J. & Hussey, R. (2013) Business Research: A Practical Guide for Undergraduate and
Postgraduate Students. 4 ed. London: Palgrave-MacMillan.
Eisenberg, J.D. (2011) The Mann-Whitney U-test [Youtube Video, Online]. Available from:
Frost, J. (April 2017) ‘How to Interpret P-values and Coefficients in Regression Analysis’,
[online] available from http://statisticsbyjim.com/regression/interpret-coefficients-p-values-
regression/ (Accessed: April 20, 2019)
https://www.youtube.com/watch?v=LnfPKGhJypu (Accessed: 18 April 2019).
Laerd Statistics (2013) Linear Regression Analysis using SPSS Statistics [Online]. Available
from: https://statistics.laerd.com/spsstutorials/linearregressionusingspssstatistics.php
(Accessed: 18 April 2019).
StatisticsLectures.com (2010) Mann-Whitney U-Test [Youtube Video, Online]. Available
from:https://www.youtube.com/watch?
v=nRAAAp1Bgnw&ebc=ANyPxKqJjaL8JunKPFpryt7ElkzJhHSOmeso_ZWMSA76Xa1gT
Znw6tITPlM2lb7OJpYpHesbB64zoFWV0j225osY3eYiIAVucg Accessed: 18 April 2019).