Data Analysis Assignment: Statistical Analysis and Interpretation

This assignment delves into statistical analysis, focusing on hypothesis testing and the interpretation of data. It includes the application of chi-square tests to analyze the relationship between different variables. The assignment explores the significance of p-values and the implications of rejecting or failing to reject the null hypothesis. It examines the relationship between gender and funding, age groups and smartphone ownership, and the impact of marketing spend on attitudinal segments. The document provides detailed analysis and interpretation of statistical results, including the identification of significant differences between groups and the implications of these findings, referencing relevant literature.

Running head: Data Analysis
Data Analysis
Name of the Student
Name of the University

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Data Analysis

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Data Analysis
Table of Contents
Q1...............................................................................................................................................4
Q2...............................................................................................................................................4
Q3...............................................................................................................................................5
References:.................................................................................................................................6

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Data Analysis
Q1.
a)
There seems to be no evidence of difference between male and female founders who secure
series A funding at the 0.05 significance level.
b)
The p value is 0.7852 which is higher than the 0.05 level which means the null hypothesis
can’t be rejected and the chi square test statistic is lower than the critical value.
Q2.
a)
Yes there is significant evidence of a difference between the different age groups in UK that
reported owning a smartphone.
b)
The p value for the chi square test is 6.2E-178 which indicates that the p value is much lower
than the standard level of significance 0.05 and the chi square test statistic (828.1319) is also
much higher the critical value of 9.4877.
c)
Using Marusculio procedure it was seen that the main difference existed across:
Age group: 16- 24 and age groups 35-54 , 55-64 and 65+.
Age group 25-34 and age groups 55-64 and 65+
Age group 35-54 and age groups 55-64 and 65+
Age groups 55-64 and 65 +

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Data Analysis
Q3.
As the p value of 0.0009 is much less than the standard 0.05 , the null hypothesis can be
rejected and it can be said that there is a relationship between money spent per month on
marketing activities and attitudinal segment.

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Data Analysis
References:
McHugh, M.L., 2013. The chi-square test of independence. Biochemia medica: Biochemia
medica, 23(2), pp.143-149.
Sharpe, D., 2015. Chi-Square Test is Statistically Significant: Now What?. Practical
Assessment, Research, and Evaluation, 20(1), p.8.