But where there is interaction between the chosen variables, especially where the humans are involved as a variable unlike inanimate objects like gases or salt will not produce the same linear results that could be expected from a scientific experiment as in physics for example. In contrast, the interaction between the multifarious individuals that comprise of the data collected may actually delay or change the patterns of the results based on many factors "that actually may dampen the individual effects of the two variables, as when two noises combine to create a zone of apparent quiet. Two gases may be relatively harmless when released into the atmosphere separately, but may yield lethal toxins when released together."
When we test the interaction effects in the case of the managers and customers of the bank, and try to establish the level of the customer relationship management, there is the same dilemma. Statistics can be used at best when the researcher has designed the experiment properly. For example where human feelings and interactions are involved, individual responses that are prompted by extraneous factors like personal feelings, the different view, or definition of relation and service that an individual may have, it is pertinent at this juncture to see if modern analysis methods that have evolved specifically for business analysis will fit the case. The importance of the method and the tools can be seen if we analyze a real experiment. For example if we were to study the fact that cat owners tend to get diseases from their pets, there are many variables that have to be considered. For example does the owner fondle his cat? What are the precautions that the cat owner takes to keep off from being infected? Here individuals participating vary and are unpredictable variables. Statistics has tools that are effective even in such dilemma. The method used is based on the experimental design.
Experimental Design
In this hypothetical experiment the cats and their owners have to participate and as a control group the individuals who have no feline pets are used. The experiment is designed keeping in mind that in scientific experiments involving humans, the experiment becomes a well planned observational process by which a question can be answered to certainty or an understanding can be reached of the external world. This is done through the observation-hypothesis-experiment. It begins with a chance observation of a new phenomenon.
The important part in the design is finding the appropriate variables. Therefore the experiment has two sets of participants -- one being the households that have cats, and another set in equal number that do not own or have cats. It boils down to a single variable if the family has a cat or not. This is the use of a single variable but not suited to this purpose although the primary position is that it is very easy to summarize results in the case of a single variable. Normally a research cannot be done in the boundary of a single variable but rather the interconnectedness of the variables is the subject of the study. Thus two variables if proved are related, could help in using the information about one to predict the other. Thus in this case the two variable models where the use of one variable is used to predict the probability of the outcome of the other is the bivariate regression model.
Another test that has been considered is the chi-square (x2) distribution which is by far the best for data analyses, and can be used to determine if the variables are dependent or independent. These considerations have prompted the following model for this research: In this case it is to be remembered that there are many pitfalls and things that would not be considered and these may lead to errors. Statistics has no answer to inherent error correction methods if the design is faulty. However statistical methods do have inherent error correction facility. For example in the analysis of the hypothetical cat disease, the discussion can go beyond the suspected diseases that the cat can pass on to any disease that is not yet suspected. This can throw more light on the issue. For example itching if noticed with cat owners but not so with the control group can be a positive indication that the itch may be caused by some dealing with the cat.
Then once this is established there can be further investigation into the issue as a sub-research. There are thus very few variables and the outcome will be based on the explanatory variables used to test the main hypotheses and this must be precise measurements that can be used to accurately measure the outcome, and also later measure the impact of the interventions...
Demographic characteristics may be used to generate this profile. Results generated may show that after cluster analysis, respondents who belong to the upper middle to upper class socio-economic group are identified as having a high degree of health consciousness, while respondents aged between 25 and 25 are the ones who most rely on self-medication. Multidimensional scaling, meanwhile, will be useful in this example by mapping out these attitudes towards
Research Analysis: Procurement Structures in CARICOM CountriesIntroductionPublic procurement management is the management of the processes surrounding the acquisition of services, goods, materials, and services required for efficient running of operations in the public sector (Khan, 2018). Among CARICOM member states, public procurement is governed by the public procurement protocols for the Caribbean community. The CARICOM protocols define public procurement as the acquisition of works, goods, or services by a procuring
Bio-Statistics Research activities, whether clinical trial based, experimentally designed, or product oriented, must exhibit and command interest, enthusiasm, and passionate commitment. To this end the researcher must catch the essential quality of the excitement of discovery that comes from research well done. The first step in the attainment of the desired research goal is to develop a scientific approach toward that which is being investigated. A requirement within the scientific
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Statistics in News Reports According to a recent article published on The Chart, CNN.com's comprehensive medical blog authored by Dr. Sanjay Gupta and Elizabeth Cohen, the number of American children who fall victim to accidental death each year has plummeted during the last decade. The article, entitled Accidental Death Rate for Children Falls, details the dramatic decrease in the "death rate from unintentional injuries among children and adolescents from birth to
Statistics and Their Importance to Research Investigation. Although all research activities do not require the use of statistical data analysis when an investigator wants to report upon the differences, effects and/or relationships between and amongst groups or phenomena (i.e. variables) there must a concerted effort to measure the phenomenon with as much precision and accuracy as possible (Mendenhall & Ramey, 1973). This is, of course, accomplished through the use of
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