(PDF) The crime reduction effects of public cctv cameras

Assessment three Statistics for
Research

TABLE OF CONTENTS
DATA..............................................................................................................................................1
METHODS......................................................................................................................................1
RESULTS........................................................................................................................................1
REFERENCES................................................................................................................................9

DATA
In researching the valid data base on which research have selected the two variables such as crime rate in Queensland as well as
CCTVs. It has been determined that the installation of CCTVs have reduced the crime rate in such location. To analyse the data base
there have been analysis over such information with considering various tests that are to be examined (Cronk, 2017).
Hypothesis
Null hypothesis: There is no mean significance difference between CCTV installation and crime rate reduction.
Alternative hypothesis: There is a mean significance difference between CCTV installation and crime rate reduction.
METHODS
As per analysing the relationship between such variables there have been implication of various methods which will represent the
adequate analysis over the outcomes such as:
Descriptive analysis: This is a summary of entire set in the form of various factors such as mean, mode, median, standard
deviation etc. it helps researcher in fetching relevant information regarding variables (Sivam & et.al., 2018).
Regression: It comprised on analysing the relationship between two variables with the help of tools such as Anova, regression,
correlation, coefficients, model summary, R statistics as well as Descriptive (Allen, Bennett & Heritage, 2018).
Correlation: This is the analysis which determines the relationship between variables on the basis of dependent and independent
variables (McCormick & et.al., 2017).
RESULTS
Descriptive
Descriptive Statistics
N Range Minimum Maximum Sum Mean Std.
Deviation
Variance Skewness Kurtosis
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Statistic Statistic Statistic Statistic Statistic Statistic Std.
Error
Statistic Statistic Statistic Std.
Error
Statistic Std.
Error
CCTVs 150 1 0 1 75 .50 .041 .502 .252 .000 .198 -2.027 .394
Crimes 150 6 0 6 424 2.83 .125 1.536 2.359 .273 .198 -.729 .394
Valid N
(listwise) 150
Interpretation: By considering the above listed analysis on which there have been determination descriptive on CCTV and
Crime rates in Queensland had been addressed. However, the mean value of data base has been analysed as 0.50 which is near to the
variable 1, in crimes the outcomes are 2.83which is near to variable 3. Therefore, there are reduction in the crime at the areas 3.
Regression
Descriptive Statistics
Mean Std. Deviation N
Crimes 2.83 1.536 150
CCTVs .50 .502 150
Correlations
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Crimes CCTVs
Pearson Correlation Crimes 1.000 -.618
CCTVs -.618 1.000
Sig. (1-tailed) Crimes . .000
CCTVs .000 .
N Crimes 150 150
CCTVs 150 150
Model Summaryb
Model R R Square Adjusted R
Square
Std. Error of
the Estimate
Change Statistics
R Square
Change
F Change df1 df2 Sig. F Change
1 .618a .382 .378 1.211 .382 91.655 1 148 .000
a. Predictors: (Constant), CCTVs
b. Dependent Variable: Crimes
ANOVAa
Model Sum of Squares df Mean Square F Sig.
1 Regression 134.427 1 134.427 91.655 .000b
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Residual 217.067 148 1.467
Total 351.493 149
a. Dependent Variable: Crimes
b. Predictors: (Constant), CCTVs
Coefficientsa
Model Unstandardized Coefficients Standardized
Coefficients
t Sig. 95.0% Confidence Interval for B
B Std. Error Beta Lower Bound Upper Bound
1 (Constant) 3.773 .140 26.983 .000 3.497 4.050
CCTVs -1.893 .198 -.618 -9.574 .000 -2.284 -1.503
a. Dependent Variable: Crimes
Residuals Statisticsa
Minimum Maximum Mean Std. Deviation N
Predicted Value 1.88 3.77 2.83 .950 150
Residual -2.773 2.227 .000 1.207 150
Std. Predicted Value -.997 .997 .000 1.000 150
Std. Residual -2.290 1.839 .000 .997 150
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a. Dependent Variable: Crimes
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Interpretation:
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As per considering the regression analysis of the data base where the CCTV and Crime rate of the data base has been analysed.
Thus, the R square of outcomes have been addressed such as 0.382 which determines that there is relationship between such variables
is for 38.2%. Moreover, as per analyzing the significance value which is less than 0.05 that is 0.000 on which there will be acceptance
to the alternative hypothesis. Therefore, there is a mean significant difference between CCTVs and Crime rates.
Correlations
Descriptive Statistics
Mean Std. Deviation N
CCTVs .50 .502 150
Crimes 2.83 1.536 150
Correlations
CCTVs Crimes
CCTVs Pearson Correlation 1 -.618**
Sig. (2-tailed) .000
Sum of Squares and Cross-
products
37.500 -71.000
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Covariance .252 -.477
N 150 150
Crimes
Pearson Correlation -.618** 1
Sig. (2-tailed) .000
Sum of Squares and Cross-
products -71.000 351.493
Covariance -.477 2.359
N 150 150
**. Correlation is significant at the 0.01 level (2-tailed).
Interpretation:
As per analysing the above presented outcome of determining the relationship between CCTVs and crime rates it can be said that,
they are not correlated to each other. The idol criteria of outcome are needed to be between -1 to 1. Here the outcomes vary and are
not perfectly fit in this category. Therefore, CCTV and crime rate in Queensland are not correlated to each other.
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REFERENCES
Books and Journals
Cronk, B. C. (2017). How to use SPSS®: A step-by-step guide to analysis and interpretation. Routledge.
Sivam, S. S. S. & et.al., (2018). Grey Relational Analysis and Anova to Determine the Optimum Process Parameters for Friction Stir
Welding of Ti and Mg Alloys. Periodica Polytechnica Mechanical Engineering. 62(4). 277-283.
Allen, P., Bennett, K., & Heritage, B. (2018). SPSS Statistics: A Practical Guide with Student Resource Access 12 Months. Cengage
AU.
McCormick, K. & et.al., (2017). SPSS Statistics for data analysis and visualization. John Wiley & Sons.
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