MAT300 Assignment 1: Descriptive Statistics Paper on Crime Seasonality

This assignment paper, prepared for MAT300 at Strayer University, analyzes the application of descriptive statistics in the context of crime seasonality. The paper examines the journal article "Crime Seasonality: Examining the Temporal Fluctuations of Property Crime in Cities with Varying Climates," focusing on how measures of dispersion, such as maximum, minimum, and standard deviation, are used to assess the relationship between climate and property crime rates in Canadian cities. The analysis includes an overview of the article's methodology, which involves comparing crime data from Vancouver and Ottawa, and discusses the real-world implications of the findings for law enforcement and property owners. The assignment highlights the role of descriptive statistics in drawing preliminary conclusions and laying the groundwork for potential inferential statistical analysis, emphasizing the importance of these statistical tools in understanding and addressing crime patterns.

Running Head: ASSIGNMENT 1: DESCRIPTIVE STATISTICS 1
Assignment 1: Descriptive Statistics Paper
Author’s Name
Professor’s Name
Strayer University
MAT300
Date

ASSIGNMENT 1: DESCRIPTIVE STATISTICS
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Assignment 1: Descriptive Statistics
Introduction
It is normally hypothesized that there exists some correlation between seasons of the year
and fluctuations in crime statistics. Most researchers believe that there are times of the year when
crime is considerably high compared to other seasons in a year (McDowall, et.al., 2011). If this
assumption is true, then it can be easily demonstrated using descriptive statistical analysis. For
example, the frequency or count of crimes committed in one season will tend to be higher than in
another period of the year. Descriptive statistics are an easy way to evaluate for differences in
two or more datasets. A researcher can use measures of central tendency and dispersion to
clearly demonstrate whether there are contrasts when comparing two datasets. In this research
assessment we will evaluate the journal article “Crime Seasonality: Examining the Temporal
Fluctuations of Property Crime in Cities with Varying Climates” with regard to the application of
measures of dispersion i.e. maximum, minimum, and standard deviation. We will assess how
these measures are used in the formulation of a conclusion relating to the connection between
crime and variation in climate (Linning, et.al, 2016).
Summary
According to Shannon, martin, and Paul (2016), there is a connection between different
property crimes in cities and variations in climate. The article evaluates two research questions
based on data collected from two Canadian cities (Vancouver, and Ottawa). The analysis of this
two sets of data are used to draw conclusions on the state of property crimes in Canadian cities
relative to climatic conditions being experienced (Linning, et.al, 2016).
Descriptive Statistics

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The researchers employ measures of dispersion maximum and minimum to evaluate the
differences between the highest and lowest recorded cases of property crimes across four
categories (vehicle theft, robbery, commercial burglary, and residential burglary). Also the
highest and lowest climate figures are recorded with regard to (temperature, rain, snow,
illumination, and twilight hours). Moreover, the research does also indicate the extent to which
the various property crimes and climatic variables deviate from the mean. A table containing this
information (i.e. Table 2) is located on page 10 of the journal article (Linning, et.al, 2016).

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Real World Applications
The issue investigated by Shannon, Martin, and Paul (2016), has real world application in
that it is important for property owners and law enforcement personnel to be aware of the times
of the year when the risk of property related crimes is considerably high. This knowledge will
allow property owners to double up on security measures and motivate law enforcement to carry
out more patrols in residential and commercial areas (Linning, et.al, 2016).
Analysis
Shannon, Martin, and Paul (2016) decided to use two sets of data from Vancouver and
Ottawa in order to get conclusive information that is unbiased. Moreover, the difference in the
data collected from the two cities (caused by geographical location and climatic patterns) creates
realistic grounds for the investigation of whether or not climate does influence the frequency of
property crimes in Canada.
Conclusion
In conclusion, we can see that there is considerable information that we can gather from
the measures of dispersion employed in this assessment. For example, we are able to gather
insight on the highest and lowest recorded cases of property crimes in Ottawa. Moreover, the
descriptive statistic analysis creates precedent for the performance of inferential statistics. If the
measures of dispersion revealed that the maximum and minimum values were equivalent and
Standard deviation was zero then there would be no ground for performing any inferential
analysis on the two data sets. Descriptive statistics as shown above is a great way to assess for
differences between and within data sets.

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References
Linning, S. J., Brantingham, P. J., & Andersen, M. A. (2016). Crime Seasonality: Examining the
Temporal Fluctuations of Property Crime in Cities With Varying Climates. International Journal
of Offender Therapy and Comparative Criminology , 61 (16), 1-27.
McDowall, D., Pate, M., & Loftin, C. (2011). Seasonal Cycles in Crime, and Their Variability. Journal
of Quantitative Criminology , 28 (3), 1-20.