Descriptive statistics refer to the numbers that are used in summarizing the descriptive data. In this case, the data refers to the raw information that has been collected in the field. Once the data has been collected, it is important for the user of the information to analyze in order to be able to interpret it and make sense of the information that has been collected. Thus, the first step in any statistic problem is to make sense of the raw information that has been collected. For a decision to be made, evidence has to be prepared in such a way that it reflects the information at the ground. The final decision will thus be made evidence based. Data consists of the information that has been collected and it includes data elements, and data points. These are the variables that are used as the units of measurement. In this paper, we shall describe the types of descriptive statistics that can be employed in the data analysis. Thus, the analysis will include of the types of inferential statistics, trend analysis, linear regression and time series.
In our case, Coca-Cola Company is facing hard times owing to the increased health awareness and campaigns, most customers are going for the fresh health products at the expense of carbonated drinks. Thus, the company is strategically positioning itself to capture the lost market share through Coca-cola fresh juices. In the United States, Odwalla fresh juice has already penetrated the market and is in the process of expanding globally. The company will thus need to evaluate the needs of the company in order to make a strategic decision for its future. The company has no intention of abandoning the production and marketing of its core product but instead wish to reposition it better and make it more appealing to the customers. As a result, there is a need for the company to collect the data that will be used in the decision making. The aim of the research will thus be to evaluate the susceptibility of the customer to opting for the alternatives, and as well for determining the healthy impacts of the products.
A descriptive statistic is a technique that is used in the description of the data that has been collected. Thus, it is used in summering the data in a meaningful way. It enables the data to visualize the data in such a way that the user will be able to make inference to it and simplify the interpretation. The first descriptive statistics that we shall use is the measure of the central tendency through the frequency distribution. Through the series of the data that will be collected through the questionnaires, a frequency distribution table will be used to visualize the distribution of the results. Each variable will be assigned a figure and then the figures will be used in coming up with a frequency distribution table. Another method that will be used is the standard deviation and the quartile analysis for the case of the evaluation of the susceptibility of the market. Thus, the descriptive statistics will give inference to the characteristics of the data from the entire group.
Inferential statistics are used in the analysis of the sample results and in the generalization in order to come up with a conclusion that represents the entire population. In this case, the data collected from the sample will be used in making the decision that will affect the entire population that include all the current and potential customers of the Coca-cola branded carbonated drink. The sample is the people who will fill in the questionnaires and as well the long term customers that will be observed to evaluate the potential health implications of the products. Thus, there is need for the evaluation of the extent to which the sample represents the population. Thus, assumptions made in the sampling and error margins are evaluated. An example of inferential statics that will be carried out in the data is the hypothesis testing, also known as significance testing. The analysis will also include the analysis of the biases in the data collected. Thus, a null and an alternative testing are formulated. A null hypothesis is thus formulated alongside an alternative hypothesis. In this case, a null hypothesis will be that Coca-cola carbonated drinks have adverse health implications to the users and the alternative hypothesis will state that the carbonated drinks have no negative health impacts to the users. Then, a significance level will be defined. This refers to the margin of errors and the relative results that will make one to either accept or reject a particular hypothesis.
The data collected and analyzed will be used in making the decisions that will be implemented over time. It is therefore paramount to understand how the sample and the results will change over time. In order to gain insight into the future impacts, a series of successive tests can be used and the results analyzed against each other. This is due to the fact that markets are dynamic and are likely to change in future. Trend analysis gives insight to the future happenings relative to the current status. Thus, probability in this case will help relate the results from the sample to the population and analyze the possibility of the sample results to represent the entire population.
In statistics, linear regression is the method that is used in the evaluation of the relationship between to variables. In this case, the aim of the research is to analyze the impacts that Coca-cola branded carbonated drinks have on health. We thus can analyze the relationship between the intake of the product and the health implications over time. Thus, the conclusion will demonstrate the long term impact of the intake of the products on the health of the sample. Time series will be used in the analysis of the information that has been collected over time. Thus, an analysis of results from various studies conducted at different times will give inference to the impact of product over time. Also, the health implication parameters can be plotted over time to give inference to either the causation, acceleration and be compared against a control sample. For example, if a patient has a chronic condition, the impact of carbonated drinks can be assessed by comparing the disease progression in him against another person that is not suffering from the condition.
Thus, a complete data analysis will include the descriptive statistics for the data properties, inferential statistics for the population, probability and trend for the relationship to the population and linear regression and time series for the development over time.
References
The Coca-Cola Company. (2015). Our Company - The Coca-Cola Company: The Coca-Cola Company. Retrieved from http://www.coca-colacompany.com/our-company
