MIS771 - Descriptive Analytics and Visualisation Assignment, Deakin University, Australia

Unit Learning Outcome -

• Apply quantitative reasoning skills to solve complex problems.
• Use contemporary data analysis and visualisation tools and recognise the limitation of such tools.

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Task - Read the case study and questions asked by Sam carefully. Once you have completed your data analysis, you need to summarise the key findings for each question and write a response to Sam's questions in a report format.

Answer - Data analysis report of the health and population statistics of East Asian and Pacific countries

1. Introduction

1.1 Authorisation and Purpose

The data taken into consideration represents the initial basic information about road transport crash deaths in Australia and it was consolidated based on the reports generated by the police each month to the State and Territory road safety authorities

1.2 Limitations

Here, the study is limited to only Australian population and hence the interpretation will be valid to the population and characteristics of Australian people and cannot be used for general population

1.3 Scope

The major objective of this study is to determine the factors that influence the road death accidents. Also, we try to determine the distribution of road death accidents which helps to understand the highest frequency of death rates along with its time and speed limit

1.4 Methodology

The data was gathered in csv format and the analysis was performed using R software package. Descriptive statistics was used to assess the distribution for continuous variables and frequency distribution was used to assess the distribution for qualitative variables. Independent sample t test, One way ANOVA and Chi - square test for independence was used to test the claim

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2. Data Setup

The data cleaning procedure was performed by the researcher or the staff who are well trained in this process. Data cleaning is a process of identifying inconsistent records that deviate far away from the normal dataset value and therefore, these values or variables needs to be removed from the dataset before performing the statistical analysis. For example, let us consider the situation of questionnaire related to life satisfaction where the data related to quality of life is missing and replacing these missing values is not appropriate as it will relate to the exact quality of life of the participants included in the study. There is difference between data missed due to some cause or data missed intentionally, even though it doesn't play a significant role, the difference in these two types of missing data was easily categorized by the statisticians. Analysis based on the missing data at random seems to be unbiased as it will not play a major role due to its small numbers of missing values when compared to the sample size of the original data set. Here, the data are cleaned by replacing the missing values by mode value for categorical variables and by median value for continuous variables

3 Exploratory Data analysis

3.1 One variable analysis

3.1.1 One variable analysis 1

mydata<-read.csv("D:/Downloads/crashdata.csv",header=T,sep=",")

attach(mydata)

head(mydata)

boxplot(Speed.Limit)

The box plot for speed limit variable is given below

From the above box plot, we see that the distribution of speed limit follows normal distribution approximately. The middle line in the box plot called the median divides the box in equal halves, indicating that the distribution of box plot follows normal distribution

3.1.2 One variable analysis 2

The distribution of crash type is given below

plot(Crash.Type)

The bar chart given above shows that the maximum number of crash type is either single or multiple. Thus, majority of the crash type fall between these two categories and therefore proper preventive action should be taken to minimize these kind of fatalities

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3.1.3 One variable analysis 3

The time variable is categorized into eight groups and are given below

12:00 AM-3:00 AM

3:00 AM-6:00 AM

6:00 AM-9:00 AM

9:00 AM-12:00 PM

12:00 PM-3:00 PM

3:00 PM-6:00 PM

6:00 PM-9:00 PM

9:00 PM-12:00 AM

From the above bar chart, we see that the maximum number of accident occurred during the time of 9 AM and 12 PM which shows a clear indicating that the fatalities occurs during the peak hours of the day

3.2 Two-variable analysis

3.2.1 Two-variable analysis 1

The association between crash type and time is assess using clustered bar chart

3.2.2 Two-variable analysis 2

counts <- table(Crash.Type,Time3hours)

> barplot(counts)

> counts <- table(Crash.Type,Christmas.Period)

> barplot(counts)

Here, we see that most of the crash type fatalities are happening on non-Christmas days

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4 Advanced analysis

4.1 Clustering

The k-means clustering is a statistical technique used in cluster analysis to partition n observations in to k clusters where each observations is said to be belong to the nearest mean clusters. On the other hand, hierarchical clustering is a method of cluster analysis which seeks to build a hierarchy of clusters. Therefore, to partitioning n observations in K non-overlapping clusters such that there is at least one observation in each cluster can be done in nCk ways

4.1.1 Brief explanation of k-means and clustering

4.2 One Way ANOVA

4.2.1 Brief definition of one way ANOVA

The one way ANOVA was used to determine whether there is a difference in the mean speed limit among various crash types

fit<-aov(Speed.Limit~Crash.Type)

fit

From the above results, we see that there is a significant difference in the mean speed limit among various crash types. The speed limit is very high in the multiple crash type followed by single

5. Conclusion

The data taken into consideration represents the initial basic information about road transport crash deaths in Australia and it was consolidated based on the reports generated by the police each month to the State and Territory road safety authorities. Here, we see that the majority of the crash type fall between these two categories and therefore proper preventive action should be taken to minimize these kind of fatalities. Also, the maximum number of accident occurred during the time of 9 AM and 12 PM which shows a clear indicating that the fatalities occurs during the peak hours of the day

6. Reflection

The major objective of this study is to determine the factors that influence the road death accidents. Also, we try to determine the distribution of road death accidents which helps to understand the highest frequency of death rates along with its time and speed limit. Here, the study is limited to only Australian population and hence the interpretation will be valid to the population and characteristics of Australian people and cannot be used for general population.

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