Descriptive statistics allow for gathering and presenting the information in a meaningful way. A good example of the use of descriptive statistics is the initial demographic profiling of target cities in the Coffee Time simulation. In the quest for market expansion of the Coffee Time in South Asian marketplace, Brad Collins of Coffee Time has profiled top 12 cities of India based on their cultural outlook and affluence. The data gives the information about the consumer class density in each of the cities. The initial research provides information on lifestyle and leisurely spending patterns to study the spending power and outlook in each of the cities. Research data also presents the distribution of population in these cities by age, income groups, spending patterns on leisure and lifestyle, demographics and many other research variables. The data available on such factors will help in ranking the cities to further narrow down the target cities.
Inferential statistics plays a big role in the Coffee Time's research objective of expected revenue. As part of the primary data collection a sample from targeted population segment is collected and surveyed for the consumers' coffee spending patterns, brand name consciousness and presence of competitors in various categories researched by use of secondary data. The result of the survey is compiled to calculate the expected revenue as R = Sum(Pi * pi * fi * si) where, R = Expected revenue
Pi = population in ith segment
pi = population in ith segment who will come to coffee Time. Fi = frequency of visit of customers from ith segment
Si= amount spent by customers of the ith segment.
Statistics, graphics, and ethics:
Statistics and graphics are about data crunching, interpretation and presentation of data to find useful and reasonable information to accomplish the research objectives. Interpretation and reliability are the two key words that may change the meaning or implication of the research results. In the coffee Time simulation, it is important to collect the sampling data by combining various factors based on cultural outlook, demographics, competitors' data and consumer market data. Leaving out a survey question that will otherwise be an important variable for research will lead to incomplete information and misleading results. Similarly, asking wrong question or sensitive information may offend the target sample population. For example, asking the salary information may cause some discomfort in the population sample. "Good experimental research design provides the researcher better control over the variables under study" (Coffee Time Simulation, 2007)
There is use of graphs in the Coffee Time simulation to present with the pictorial comparison of research variables like distribution by age groups for the target cities. The population distribution can be easily studied by use of bar charts. The compound bar charts used in the Coffee Time simulation presents a good example of graphical excellence. The vertically drawn compound bar charts trace the trends of each individual age group as well as help in making comparisons between the cities. By looking at the chart, one can quickly conclude the age group that has highest consumer density and the comparison can be run across the cities as well.
Constructing a frequency distribution:
A frequency distribution table helps in pointing out where the data values tend to concentrate and help distinguish the largest and the smallest values. Frequency distribution is used throughout the Coffee Time simulation's primary research. For example, the survey question about the age of the customer helps in studying the pattern of spending by age groups in the target cities. There are 5 classes (13-19, 20-24, 25-34,35-54, and greater than 55) for which one can count the responses...