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- 1. FIRE ADMIN UNIT1 .orct121320#ffffff#fa951a#FFFFFF#e7b3513VERSON2.2MAY OR/CITY COUNSELxNO#66b66cCITY MANAGER1zNO#CD6A80FIRE CHIEF2zNO#504DCDOPERATIONS ASSISTANT CHIEF3zNO#FF8C00ADMINISTRATIVE ASSISTANT CHIEF3zNO#8E388ECHIEF OF PREVENTION5zNO#00ae00CHIEF OF TRAINING5zNO#ff6e01CONFIDENTIAL AMINISTRSTIVE ASSISTANT3x8#935c24ADMINISTRATIVE ASSISTANT4x9#388E8EADMINISTRATIVE ASSISTANT5y10#5483a2BATTALION CHIEF (1 PER SHIFT4zNO#B0171FDISTRICT CHIEF (3 PER SHIFT)11zNO#912CEECAPTAIN (18 PER SHIFT)12zNO#0000EELIEUTANENT (18 PER SHIFT)13zNO#00868BDRIVER/OPERATOR (18 PER SHIFT)14zNO#698B22FIREFIGHTER-1 (18 PER SHIFT)15zNO#FFA500RESCUE SPECIALIST II (10 PER SHIFT)12zNO#7171C6RESCUE SPECIALIST I (10 PER SHIFT)17zNO#418cf0SENIOR FIRE INVESTIGATOR6zNO#00BFFFSENIOR FIRE SAFETY EDUCATOR6zNO#4682B4SENIOR FIRE INSPECTOR6zNO#FF8C00FIRE INVESTIGATOR II19zNO#0000EEFIRE INVESTIGATOR I22zNO#6E7B8BFIRE SAFETY EDUCATOR II20zNO#FF6103FIRE SAFETY EDUCATOR I24zNO#FFE4E1FIRE INSPECTOR II21zNO#808000FIRE INSPECTOR I (2)26zNO#9BCD9BSENIOR TRAINING OFFICER7zNO#87CEFATRAINING OFFICER II (2)28zNO#D02090TRAINING OFFICER I (3)29zNO#308014MAINTENANCE SUPERVISOR/MASTER MECHANIC5zNO#9ACD32ADMINISTRATIVE ASSISTANT31y32#418cf0MAINTENANCE TECHNICIAN
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- 3. Descriptive Statistics Descriptive statisticsare statistics that describe or summarize features of collected data. Descriptive statistics simply present quantitative information in a manner that can be easily managed. The large amount of data is reduced into a simple summary and therefore the whole process of describing the data is less laborious. For example, finding the mean helps to summarize a lot of individual information into a way that is quickly understood. The samples are likely to produce different independent variables that affect the sales of Elite Technologies Limited. For this reason, we opt to use bivariate analysis in the describing the statistics. Bivariate analysis of the descriptive statistics that is derived from the data will help in drawing relationships between different variables. For a more accurate representation the Pearson’s R bivariate method of statistical analysis will be used. The information that is received from the customers and the sales is put in a manageable way that can enable the management level to make timely but well informed decisions based on accurate summaries. Inferential Statistics With inferential statistics their aim is to derive information beyond that which the data does not present at face value. It gives a proposition about data concerning a particular population. Inferential statistics assumes that the data comes from a larger population and not merely limited to the observable data. A hypothesis or hypotheses are then proposed and tested to derive estimates. Konishi and Kagawa (2008) say that, “The majority of the problems in statistical inference can be considered to be problems related to statistical modelling”. After the statistical inference is made then a statistical proposition can be made. It is at this point that a hypothesis can be formulated which may later on be accepted or even rejected after being proven to be true or false. Trend Analysis
- 4. Trend analysis refersto the techniques for extracting an underlying pattern of behavior in a time series which would otherwise be partly or nearly completely hidden by noise. Trends may be linear or have more complex forms such as polynomial or logistic. It is important to specify the trend explicitly prior to further analysis and modelling. Creating a time series graph where data like sales is plotted can be visually examined to determine whether there is the existence of a trend. Autocorrelation analysis is also a useful technique is useful for identifying the existence of such trends and is even more accurate than employing the use of visual inspection. Having established that trends exist one can then consider procedures for identifying and managing trends. Some of these procedures include curve fitting, for example, through linear regression or growth curves and also filtering and differencing. Linear Regression for Trend Analysis Linear regression is the most basic and commonly used predictive analysis where estimates are used to describe data and to explain the relationship between one dependent variable and one or more independent variable. With respect to trend analysis, linear regression may be used to identify the effect that the independent variables have on the dependent variables. For this case independent variables like customer dissatisfaction will be analyzed for their effect on sales which is the dependent variable. Secondly we are able to understand how much the dependent variable will change when one or more independent variables are changed. For this case we will be able to understand and observe the change in sales when independent variables like customer dissatisfaction are changed. By drawing a trend line we are able to see the variations that have taken place with respect to sales data. In as much as trend lines lack scientific validity, their ease of use has made them
- 5. gain popularity. Theymay appear as straight lines connecting data points or may take a more complex form of polynomials. Time Series Time series can be defined as an ordered sequence of values of a variable at equally spaced time intervals. Time series analysis takes into account the fact that the data points taken over time have an internal structure such as autocorrelation, trend or seasonal variation that should be accounted for. Time series analysis, unlike linear regression, can be able to provide analyses over time and is not just simply instantaneous. For purposes of making forecasting like economic forecasting and sales forecasting and also making projections and controls among other things, the application of a time series is then favorable for use. Time series analysis comprises methods for analyzing time series data in order to extract meaningful statistics and other characteristics of the data. Time series forecasting is a model used to predict future values based on the previously observed values. The advantage of using time series analysis is that it can be applied to real-valued, continuous data, discrete numeric data, or discrete symbolic data such as letters that are used in the English alphabets. Some of the methods used in the fitting of a time series model include Box-Jenkins ARIMA models, Box Jenkins Multivariate Models and Holt-Winters Exponential Smoothing (single, double, triple). Instead of making assumptions that may be costly to Elite Technologies Company concerning its sales, more informed decisions may be made with the application of a time series analysis. The organization will be better placed at making decisions now that they will be able to anticipate certain events based on the information received from having a time series analysis.
- 6. Reference Chatfield C. (1975).The Analysis of Times Series: Theory and Practice. Chapman and Hall. London. Mann Prem S. (1995). Introductory Statistics (2nd ed). Wiley. Nick Todd G. (2007). Descriptive Statistics. Topics in Biostatistics. Methods in Molecular Biology. New York: Springer. Pp. 33-52. Bickel Peter J., Doksum Kjell A. (2001). Mathematical Statistics: Basic and Selected Topics. 2nd ed. Cox D. R. (2006). Principles of Statistical Inference, Cambridge University Press. Freedman D.A. (2009). Statistical Models: Theory and Practice (revised ed.) Cambridge University Press. Lewis, S. (2007). Regression Analysis. Practical Neurology, 7(4), 259-264. Imdadullah. Time Series Analysis. Basic Statistics and Data Analysis. Itfeature.com. Retrieved 2 January 2014. Shumway, R. H. (1988). Applied Statistical Time Series Analysis. Englewood cliffs, NJ: Prentice Hall. Bloomfield, P. (1976). Fourier Analysis of Time Series: An Introduction. New York: Wiley Australian Bureau of Statistics, (2008, July 25). Time Series Analysis. The Basics. Retrieved November 29, 2012, from Australian Bureau of Statistics.
- 7. Developing an EffectiveData Collection Process Jared Linscombe QNT/275 Dr. Davisson August 29, 2016 Developing an Effective Data Collection Process Elite Technologies Company is one of the best companies globally, and it has a relatively bigger market share which is attributed to its ability to provide quality products and service to its customers. The company deals with assembling and sale of electronics ranging from household electronics to entertainment appliances. The company also has a subsidiary service branch that deals with all the service problems from the company and any other services brought to them. The existing combination has been able to forge a strong relationship with customers improving the company-customer relationship. The company has been enjoying higher profits due to its suitability and good business relations, unlike other companies who do not have a renowned service affiliated company that can be able to improve its customer satisfaction. Problem Despite having better business deals, recent seasons have seen a slight decline in the sales of some of its best-suited products. However, the organization remains adamant that the low season will soon be over, and it will achieve its set sales objectives. Therefore the organization depends on assumptions rather than engaging its customers into finding out what the problem might be. The fact that the company is customer dependent, there is need to establish better strategies that the organization can be able to know if there is anything wrong which should be
- 8. achieved as well.The company has been making abnormal profits which have made it more adamant in addressing the basic needs of some of its longstanding stakeholders who played a huge role in the rise of the company by providing invaluable advice and support which have placed the organization at its current level of success. Research variable A research variable is an important factor that is put into consideration when developing a research analysis. It is important to identify the research variables before commencing any data collection because the whole research will ultimately depend on these variables to make a conclusion about the situation of the company. In any given research, the variables may assume different roles depending on how the study is developed. In this particular case scenario, one of the variables will be sales which will constitute the dependent variable (Male, 2015, p. 18). A dependent variable, in this case, will depend on other independent variables which will provide the organization with the mush needed information regarding the fluctuations in sales. The independent variables are factors that are not influenced by other occurrences but are rather reflective. The independent variable will provide the best picture to know if the company’s assumption that the sales are lower because the season is low is held true. Therefore in this respect, some of the independent variables that can be considered include attitude, prices, low season and any other that the organization may consider and are considered effective (Powell and Grossman, 2015, p. 10). Data collection In this case, the method selected for data collection should be able to capture the details involving the research variables both independent and dependent. Data collection method will involve various strategies that can be employed to obtain an unbiased outcome. In any statistical research data collection, a lot of emphases is placed on the method undertaken to obtain data
- 9. mainly because biaseddata cannot provide the most effective result since it will be influenced by other factors that are not considered in a given research. In this case, the company has a large customer base and the best response involving the subject matter can only be obtained from the individuals who consume the company’s products (Powell and Grossman, 2015, p. 12). Since the total population is much greater, the company will have to identify effectively the best sampling design that they will use to get the sample population which they will be able to work with. In this case, the best sampling design would simple random sampling. Simple random sampling will be the best sampling technique that will ensure that the sample selection is not biased in any way. This procedure ensures that the selected samples or respondents are randomly picked by the research assistants who will be undertaking the survey. The total sample should be able to reflect equally to the total population since the fishers formula will be best suited to get the best reflective sample of the total population (Shields et.al, 2016, p. 37). After the establishment of the sample size, the research then will employ the use of a questionnaire which will be administered to the selected sample to provide an honest opinion regarding the question involving the variables. The questionnaire is commonly referred because if the questions are perfectly framed, the outcome will be very much a true representation of the total population. The data collected in this case would include both qualitative and quantitative (Male, 2015, p. 33). However, it will significantly depend on how the questions in the study have been framed. Incorporating both qualitative and quantitative detail will provide a much detailed outcome. Qualitative data will provide the descriptive part of the research where the respondents will be able to provide information regarding how the situation is by use of more data that can be observed but not measured. Quantitative data, on the other hand, will provide important information regarding the exact numbers which can be measured and effectively analyzed
- 10. (Shields et.al, 2016,p. 40). Ensuring a valid and reliable data Ensuring reliability of data collected depends on some aspects that need to be monitored closely to develop a better data that can be accurately analyzed and present the best solution that can help the organization in making rightful decisions. Reliability of data means that the data collected can be used to produce undisputed outcome while validity means that the data collected is accurate and correct without any bias or influence from non-accounted for factors in research (Male, 2015, p. 51). One of the methods to ensure reliability and validity of research data are use of reliable data sources. This means that the sample population chosen should be able to provide accurate information without any influence in order to help the company make necessary changes. Another method of ensuring that the data collected is reliable and valid is to ensure that the data capture methods used in data collection are accurate and are highly recommended in the study. It is also important to ensure that the respondents in the research know perfectly well what they are supposed to do in ensuring that they are trained or guided on how to answer the questions as presented to enhance reliability and validity (Powell and Grossman, 2015, p. 28). To conduct a study that can form the basis of policy formulation or change of strategy, it is important to outline effectively the best possible research design that will be adopted for the implementation of the whole research. References Hilemon, C. G., Nelson, T. E., Skeirik, R. D., Hayzen, A. J., & Horn, D. M. (2016). U.S. Patent No. 20,160,048,110. Washington, DC: U.S. Patent and Trademark Office. Male, T. (2015). Analysing Qualitative data. Doing Research in Education: Theory and Practice, 177. Powell, M., & Grossman, A. (2015). Quality indicators in
- 11. pituitary surgery: aneed for reliable and valid assessments. What should be measured?. Clinical endocrinology. Rosen, D. L., & Olshavsky, R. W. (2015). Interactive Data Collection: Implications for Laboratory Research. In Proceedings of the 1984 Academy of Marketing Science (AMS) Annual Conference (pp. 82-86). Springer International Publishing. Shields, A. L., Shiffman, S., & Stone, A. (2016). Recall Bias: Understanding and Reducing Bias in PRO Data Collection. EPro: Electronic Solution s for Patient-Reported Data, 5.