Statistical Analysis and Evaluation of Crime Committed by Inmates in Benin Prison in Edo State using Time  Series Model

Crimes exist in every society. This paper presents the statistical analysis of crime committed Inmates in Benin City Prison in Edo State with a suitable model using a time series approach. The paper examines the extent of crime committed in the prison for a period of seven years between 1999 to 2005. The study showedsss that age has no influence on the type of crime committed and that religion has no influence on the crime rate. The presentation gives future forecast in the population of prisoners in the prison with available crime rate data.

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Statistical analysis of traffic system: A time series approach

Transportation Research ◽

10.1016/0041-1647(77)90074-0 ◽

1977 ◽

Vol 11 (1) ◽

pp. 72-73

Keyword(s):

Time Series ◽

Statistical Analysis ◽

Traffic System ◽

System A ◽

Time Series Approach

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Time Series Analysis of PM10 Concentration in Parit Raja Residential Area

International Journal of Engineering & Technology ◽

10.14419/ijet.v7i3.23.17252 ◽

2018 ◽

Vol 7 (3.23) ◽

pp. 15

Author(s):

Ahmad Fauzi Raffee ◽

Hazrul Abdul Hamid ◽

Radin Maya Saphira Radin Mohamed ◽

Muhammad Ismail Jaffar

Keyword(s):

Time Series ◽

Absolute Error ◽

Residential Area ◽

Time Series Model ◽

Pm10 Concentration ◽

Primary Data ◽

Suitable Model ◽

Short Term ◽

Term Prediction ◽

Short Term Prediction

Parit Raja is one of the sub-urban area that rapidly grow due to its location containing industrial and education hub. Pollution from factories and the increasing number of vehicles are the main contributors of PM10. Since PM10 can give the adverse effect to human health such as asthma, cardiovascular disease and lung problem, appropriate action mainly involve short-term prediction maybe required as a precaution. This research was conducted to predict the PM10 concentration using the best time series model in Parit Raja, Batu Pahat, Johor. Primary data was obtained using E-Sampler at three monitoring stations; Sekolah Menengah Kebangsaan (SMK) Tun Ismail, Kolej Kediaman Melewar and Sekolah Rendah Kebangsaan Pintas Raya. ARIMA time series model was used to predict the PM10 concentration and the most suitable model is identify using by Akaike Information Criterion (AIC). Prediction of PM10 concentration for for the next 48 hours at all monitoring locations was verified using three error measures which are mean absolute error (MAE), normalized absolute error (NAE) and root mean square error (RMSE). After comparing the time series model, the short term prediction model for station 1 is AR(1), station 2 is ARMA(1,1) and station 3 is ARMA(2,1) based on the smallest AIC value and the best time series model that used for prediction at Parit Raja residential area is AR(1). Since the best model was identified for Parit Raja residential area, PM10 concentration can be predicted using AR(1) model to identify the value of PM10 concentration in the next day.

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Is There Convergence in National Alcohol Consumption Patterns? Evidence from a Compositional Time Series Approach

Journal of Wine Economics ◽

10.1017/jwe.2018.2 ◽

2018 ◽

Vol 13 (1) ◽

pp. 92-98 ◽

Cited By ~ 8

Author(s):

Terence C. Mills

Keyword(s):

Time Series ◽

Statistical Analysis ◽

Alcohol Consumption ◽

Statistical Approach ◽

Consumption Patterns ◽

Data Sets ◽

Share Data ◽

Time Series Approach ◽

Global Mean ◽

Jel Classifications

AbstractHolmes and Anderson (2017a) introduce two extensive data sets on world alcohol consumption and expenditure and with them investigate, among other things, the possible convergence of national alcohol consumption patterns using wine, beer, and spirit shares. Such share data define a composition, on which conventional statistical analysis using covariances and correlations is invalid. This note reanalyses the data using techniques appropriate for a composition and introduces a statistic that can validly track the variation in national shares around the global mean through time. This variability statistic shows that such convergence of national alcohol patterns has clearly taken place over the period 1961 to 2014 and thus confirms Holmes and Anderson's findings using a valid statistical approach. (JEL Classifications: C18, D12, L66)

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Modeling U.S. Broiler Supply Response: A Structural Time Series Approach

Agricultural and Resource Economics Review ◽

10.1017/s1068280500006559 ◽

1998 ◽

Vol 27 (2) ◽

pp. 241-251 ◽

Cited By ~ 8

Author(s):

Crispin M. Kapombe ◽

Dale Colyer

Keyword(s):

Time Series ◽

The United States ◽

Time Series Model ◽

Poultry Production ◽

Seasonal Component ◽

Supply Response ◽

Structural Time Series ◽

Time Series Approach ◽

Structural Time ◽

Feed Costs

A structural time series model is used to estimate the supply response function for broiler production in the United States using quarterly data and a structural time series model. This model has the advantage of expressing trend and seasonal elements as stochastic components, allowing a dynamic interpretation of the results and improving the forecast capabilities of the model. The results of the estimation indicate the continued importance of feed costs to poultry production and of technology as expressed by the stochastic trend variable. However, seasonal influences appear to have become less important, since the seasonal component was not statistically significant.

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About One Approach to Defuzzifikation of Outputs of Fuzzy Time Series Model

Indian Journal Of Applied Research ◽

10.15373/2249555x/sept2013/173 ◽

2011 ◽

Vol 3 (9) ◽

pp. 562-566

Author(s):

Ramin Rzayev ◽

◽

Musa Agamaliyev ◽

Nijat Askerov

Keyword(s):

Time Series ◽

Time Series Model ◽

Fuzzy Time Series

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Correlation in the heart rate data

Nonlinear Analysis Modelling and Control ◽

10.15388/na.1998.2.0.15301 ◽

1998 ◽

Vol 2 ◽

pp. 141-148

Author(s):

J. Ulbikas ◽

A. Čenys ◽

D. Žemaitytė ◽

G. Varoneckas

Keyword(s):

Nonlinear Dynamics ◽

Experimental Data ◽

Heart Rate ◽

Time Series ◽

Cardiovascular Function ◽

Statistical Properties ◽

Rate Data ◽

Heart Rate Data ◽

Dynamical Nature ◽

Analysis Of Time Series

Variety of methods of nonlinear dynamics have been used for possibility of an analysis of time series in experimental physiology. Dynamical nature of experimental data was checked using specific methods. Statistical properties of the heart rate have been investigated. Correlation between of cardiovascular function and statistical properties of both, heart rate and stroke volume, have been analyzed. Possibility to use a data from correlations in heart rate for monitoring of cardiovascular function was discussed.

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