Analysis of Infection Rate of COVID-19 in White and BAME Patients

ACCB4002 Data Analysis and Visualization

Contents
INTRODUCTION.......................................................................................................................................3
MAIN BODY..............................................................................................................................................3
Analysis...................................................................................................................................................3
Practical application and deployment......................................................................................................9
CONCLUSION.........................................................................................................................................15
REFERENCES..........................................................................................................................................16

INTRODUCTION
The report is based on analysis of infection rate of COVID– 19 are varied between white people
and BAME in the UK. The data is based on 50 different white and BAME patients. The report
contains detailed given data set in order to determine which types of patients affected more from
COVID 19. In order to do so various kinds of hypothesis are prepared that are tested from a
suitable statistical testes.
MAIN BODY
Analysis
Description of data set.
The given data set is about 50 people who are from different age group. The age is between 20 to
50 years. In the data set, infection rate of various kinds of people who are of white and BAME is
mentioned. The objective of data set is to assess ratio of infection rate of both types of people
including white and BAME (Black, Asian and Minority Ethnic) people. Below descriptive
analysis of given data is done in such manner:
The analysis of historical data to help explain improvements that have happened in an
organization is descriptive analytics (Borrill, Ramirez-Gonzalez and Uauy, 2016). The use of a
set of historical data to make parallels is represented in Descriptive analytics.
Descriptive statistics:
Statistics
White
patient BAME
N Valid 50 50
Missing 0 0
Mean 36.180 36.740
Median 36.000 34.500

Mode 20.0a 50.0
Std. Deviation 9.4192 9.3171
Variance 88.722 86.809
a. Multiple modes exist. The smallest
value is shown
White patient
Frequency Percent
Valid
Percent
Cumulative
Percent
Valid 20.0 4 8.0 8.0 8.0
22.0 2 4.0 4.0 12.0
25.0 2 4.0 4.0 16.0
26.0 1 2.0 2.0 18.0
27.0 2 4.0 4.0 22.0
28.0 1 2.0 2.0 24.0
29.0 1 2.0 2.0 26.0
30.0 2 4.0 4.0 30.0
32.0 1 2.0 2.0 32.0
33.0 4 8.0 8.0 40.0
34.0 2 4.0 4.0 44.0
35.0 3 6.0 6.0 50.0
37.0 2 4.0 4.0 54.0
38.0 2 4.0 4.0 58.0
40.0 4 8.0 8.0 66.0
42.0 1 2.0 2.0 68.0
43.0 4 8.0 8.0 76.0
44.0 3 6.0 6.0 82.0
45.0 1 2.0 2.0 84.0