Statistics of Laikipia Universirty

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The study of statistics involves the collection of data mostly in large sets, its analysis, presentation and making inferences about that data may be it will help someone to write a custom essay. From surveys to experimental studies, statistics interacts with the population in many disciplines. An example is that of carrying out clinical trials to ascertain the feasibility of a drug in medicine. Forecasting a business trend through sample data, carrying out a census in social circles and the use of probability theories to predict the outcome of say the likely winner of a political position are quintessential. This development has led to the universal adoption of statistics inclusive even of the section of the population that does not apply statistical methods directly.

There are two statistical techniques or methodologies that are applied in the analysis of data. The latter include; descriptive statistics and inferential statistics. Descriptive statistics involves summarizing sample data using measurement indexes such as the mean and the variance. Inferential statistics on the other hand deals with drawing of conclusions from the collected sample data through say, observation errors, sampling variation among others.

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Standard statistical procedures involve the development or the null hypothesis and the alternative hypothesis.

The null hypothesis is a generalized statement that depicts a relationship between two quantities. The alternative hypothesis rejects or rather statistically fails to accept the null hypothesis when such is the case. Statistical measurements are susceptible to errors. The errors may result from approximations, counting errors, the time period bias among others. Errors due do hypothesis include the Type I Error and the Type II Error both of which are the polar opposite of one another (Anderson, Sweeney, Williams & Anderson, 1984).

Statistics has been used extensively by different levels of management to make decisions as a consequence of the results of an analysis. An example of an area where statistical knowledge is widely used is in service industries such as Samsung. Samsung is a multinational conglomerate South Korean company headquartered in Samsung Town Seoul. The most notable subsidiary of Samsung is the Samsung Electronics. The latter is the world largest information technology company measured by 2012 revenues and 4th in market value. Samsung facts and figures demonstrate the company’s loyalty to statistics. Sales forecasting are common and important tools used for business planning and in the making of decisions (Sandler, 2014).

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Through time series analysis, the knowledge of statistics can be used to make forecasts or projections for the expected demand of the produced goods and services. Time series analysis involves the methods for the analysis of trend or time series data in a bid to extract meaningful statistics and other characteristics of the data. Time series forecasting has been used widely in making these projections. It involves the use of a model to predict future values based on previously observed results (Brockwell & Davis, 1991).

Samsung used this trend to make its expected smartphone sales in 2013. The first step involved data collection through interviews and on point questionnaires to its potential customers and analysis after that. Having shipped an estimated 225 million smartphones through the – quarters of 2013, the change in seasonality and an upward trend in consumption allowed for projections. Samsung analysts, for example, made a sales projection of 310 million full-year shipments allowing a 0.1% error of approximation. The sales forecasts were authenticated by Samsung unit sales of 312 million units as of the full year of 2013. This estimate demonstrates the strengths of statistics as a tool that supports arguments. The analysis of the data and its presentation through graphs helped in extrapolation and ultimately in the forecast (Sandler, 2014).

Another example is in the use of regression analysis to make estimations with regards to the degree of relationship between variables.

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This statistical technique focuses on the relationship between the independent variables and the dependent variables. An illustration is that of a study involving the United States Department of Agriculture. The research focused on the use of Multiple Regression to analyze the effects of changes in industry location and productivity. In this study involved the determinant factors associated with industry location. The results of the analysis would help local development planners to evaluate the prospects of their industry location given their manufacturing needs. The independent factors included land and its strategic placement among others. Dependent variables included the shifts in transportation routes, trading and commuting patterns, existing and potential recreation facilities among others (Shames, 2009).

The results of this particular study would be imperative not only to the United States Department of Agriculture but to also firms as well. The results of such an analysis would help the person presenting both supporting and opposing ideas about the positives and the weak sides of setting an industry in the subject location. The above illustrations showcase the importance of statistics in making forecasts, faulting an idea about a particular development and advocating for a more rational course thanks to the inferences.

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