Elegant Graphics for Data Analysis †Free Samples to Students

Question: Discuss about the Elegant Graphics for Data Analysis. Answer: Introduction: From the given situation, it can be observed that 100 customers are there. All of the 100 customers have taken participation in the survey in order to provide response. There will be four kinds of scores. All these scores will be represented in four categories; they are strongly disagree, disagree, unsure, agree and strongly agree. From the provided situation, it can be seen that there are two types of processes or situation and results from both the situations management are different from each other. According to the first situation, in case, all the numbers of the customers are added up, the total numbers will be 100. On the contrary, in case the total percentage of responses is added up, it will be 100%. From the above situation, it can be seen that in both of the cases, they are 100 customers and 100%. It can be said that both the figures are matching and it is the proof that the process is correct as well as effective. Hence, it can be said that it is the correct process to sum maries the data. However, in case of the second situation, the condition and result is very different when compared to the first situation. In case of this situation, it has been seen that multiplication is done among the number of customers from the each step and the number of ranks. As a result of this, the total number of customers become 319 from 100. However, the total percentage remains the same that is 100%. In this regard, it needs to be mentioned that calculations in both the situations have done based on the same situation. For this reason, the total numbers of customers in both the situations should be the same that is 100. However, the number of customers differs between the two situations. Due to this mistake, this particular process fails to derive the correct result from the situation and it has failed to provide the valid summary. Thus, based on the whole analysis, it can be said that the first method has provided more valid process for the presentation of data that shows the correct and actual value of the customer. At the same time, it can be said that people should not accept the results of the second analysis, as it is not proper method. As per the given situation, it can be observed that the customers have the facility for filling out the form of survey from the website of the store. This is the only way for the customers to fill up the survey forms of the store and the store is collected the data from this way only. From this particular process, it can be seen that the store will get mixed reactions from the customers as all the customers will not fulfill the server based on their true perception. For this reason, in some situations, the collected data from the customers will not reflect the true perception of the customers; and in some situations, the collected data from the survey will reflect the true perception of the customers. Many reasons lead to this disparity of perceptions of the customers. It can be happened that some of the customers take the survey as a joke and they fill the survey form out of fun. Thus, in this situation, the customers do not fulfill the survey from their true perception. Apart from this, it can be happened that a customer of the store is extremely happy with the products and services of the store. In this case, at the time of the survey, the customers will fill it from the true perception. In this case, the store will get response from the true perception of the customer. It can also be seen that a customer is not satisfied with the product and service of the company and for this reason; he/she is not even interested in taking part in the survey. Hence, the above discussion shows that the conducted surveys with the help of online forms do not always reelect the true perception of the customers. From the provided situation, it can be seen that there are five particular situations. The first situation is about gender. In case of gender, nominal data are collected. In this regard, it nees to be mentioned that gender is a categorical variable and it consists of two categories management; they are male and female. Apart from this, any intrinsic ordering cannot be seen in this. Thus, gender is considered as categorical variable (Boone Boone, 2012). The next items are Fahrenheit thermometers and Kelvin thermometers. In case of both of these thermometers, the researcher will collect interval data. In addition, temperature is the type of element which is used for illustration and interval scale. Hence, the researcher will collect interval data (Thomson Emery, 2014). The third element is number of items. For the number of items, ordinal data will be collected. With the help of ordinal variables, the researchers can categorize different values. Thus, ordinal data will be collected (Cliff, 2014). The next item is bank account balance. In case of bank account balance, the researcher will collect ratio scale type of data. Ratio scale variable is considered as interval variables. In this particular case, the measurement of zero (0) indicates that there is not any balance in the bank. This same concept is applicable for bank balances (Wickham, 2016). In the aspect of descriptive non-experimental study, one can see the predictor variable (Yarcheski, Mahon and Yarcheski, 2012). Predictor variable provides great assistance in the testing of hypothesis in the descriptive way. For this particular reason, the researcher can develop the case study for the testing of hypothesis. The next step in this process of descriptive non-experimental is quasi experiment (Cook, 2015). For this case, the researcher considers the variables. In this case, the variables can be referred as three times per week and four days each week. However, in case of experimental study, the researcher tests the hypothesis on the accurate basis for the combination of above two steps. Hence, an experimental study will be helpful to provide the firmest result. References Boone, H. N., Boone, D. A. (2012). Analyzing likert data.Journal of extension,50(2), 1-5. Cliff, N. (2014).Ordinal methods for behavioral data analysis. Psychology Press. Cook, T. D. (2015). Quasi?experimental design.Wiley Encyclopedia of Management. Thomson, R. E., Emery, W. J. (2014).Data analysis methods in physical oceanography. Newnes. Wickham, H. (2016).ggplot2: elegant graphics for data analysis. Springer. Yarcheski, A., Mahon, N.E. and Yarcheski, T.J., 2012. A descriptive study of research published in scientific nursing journals from 1985 to 2010.International journal of nursing studies,49(9), pp.1112-1121.