Statistics can be defined as the science of learning from data, measuring, controlling, as well as communicating uncertainty. Statistics therefore offers the best navigation essential to control the course of societal and scientific advances (Beri, 2005). Statistical methods and thinking are applied by statisticians to a variety of social, scientific and business models in subjects such as economics, marketing, astronomy, education among others. The techniques used in interpreting statistical data are widely used in making social, political, economic and political decisions.
Qualitative data in statistics is used to describe variables who have a non-numeric format. These types of variables cannot be counted, measured or ordered such as marital status (single, married or so), gender, job title, geographical location of an organization etc. Qualitative data is normally treated as categorical data. Characteristics or non-overlapping categories are used to sort the observations made on the categorical data. Every value of the data set belongs to only one category and the choice of the category should be made carefully to avoid compromising the whole output. Eg Color is a characteristic and black, yellow, green, blue or so are non-overlapping categories of color. The measurement scale used for qualitative data is Ordinal that classifies data with ranking such as by use of grades and Nominal which classifies data without ranking such as investment type or color. Qualitative data is analyzed using graphs, modes, and contingency and frequency tables (Wyse, 2011).
Quantitative data on the other hand is the type of data that is numerically represented from observations of measurements and frequencies. The data used is discrete meaning it can be measured in terms of integers e.g number of employees in a company. Continuous data is also used where the measurement can take any value within some range such as weight. All descriptive statistics can be used to analyze quantitative data ranging from creation of categories, groups, frequency tables as well as graphs. The Measurement scale used here is Ratio and Interval which means a meaningful ordered and difference between variables and Interval. Ordinal data is not strictly measured and is treated only as ranks with a natural order and the ration between adjacent values is not meaningful (Wyse, 2011).
Tables and charts used to represent quantitative and qualitative data.
Frequency Table/Frequency Distribution
Used to give nominal, categorical and ordinal data summary. Where the data is divided into meaningful groups, this table can be used to provide a summary of continuous data. Frequency is the number associated with each category of data. Frequency distribution is the total frequencies over all the categories. Visual interpretation of data is enabled and the analysis of the data is easy. The table below shows the frequency table for qualitative data (color preferences of customers in a shop)
The other table below shows frequency distribution for quantitative data (Time distribution)
CEO Compensation (x$1 mil.)
Contingency Table
This tables enables the analysis of the relationship between variables by tabulating data using two or more categorical variables.
Graphs for qualitative data
Pie Charts – this entails a proportioned church showing the percentages of the whole falling into each category. Express the information about the relative sizes of groups more precise than tables.
Bar Charts
Have vertical scale for frequencies and horizontal one for the categories to depict the percentages of various categories allowing for easy comparison between them. They can also present simultaneous categorical variables.
Graphs for Continuous Quantitative Data
Stem and Leaf Plots
Data is put into groups or stems with values of each group or leafs stemming from the stem to the right. The data is utilized as part of the graph.
Histograms
Histograms look like Bar Charts but have no gaps between the bars and are used to show the frequency distributions of continuous variables. The intervals between data sets is same. The frequency is established and rectangles are drawn over each interval. The relative frequency is used to compare two or more groups having different sample sizes.
Frequency Polygon
Ogive
XY Scatter Chart
Used to plot quantitative and continuous variables to determine the relationship between the two.
A Line Chart can serve the same purpose but only when the horizontal values are ranked.
Measurement Levels of Data
They are used to distinguish the levels of measurements for the data presented from weakest to the strongest using Nominal, Ordinal, Interval and Ratio. In nominal there is no ordering, in ordinal there is ordering but there is no distance, in interval there is distance but there are no ratios and in Ratio there is ratios. Nominal and ordinal variables are categorical variables whereas Interval and Ratio are numerical variables. Statistics lean on the highest levels of measurements as they tend to be more sensitive and sophisticated.
The role of statistics in business decision-making
The companies which thrive well in the competitive business environment where data is abundant are the ones armed with the information that has been analyzed by using statistical techniques. Statistics play a central role in business decision analysis. They help in acquiring scientific data and information as well as in accurate analysis of that information to yield more profits for companies. Such organizations have the likelihood of achieving great success of their stakeholders, they have lower chances of missing on lucrative opportunities and are less exposed to risks. Statistics help companies collect and analyze more data and information to arrive at optimal decisions given the fact that businesses operate amidst high uncertainties and probabilities.
Determining the quality grades of materials by QC department is an example of a problem situation in which statistics could be used. They use random sampling techniques in coming up with decisions. Financial ratios are good tools of business decision analysis where the internal figures of accounting can be used. They provide a crystallized picture of the organization whilst testing its performance against varying parameters. Such ratios are like Current Ratio, Profit to Sales ratio, Return on Capital Employed and Debt to Equity ratio.
References
G C Beri (2005). Business Statistics, 2E. Noida: Tata McGraw-Hill Education - Commercial statistics - 728 pages.
Susan E. Wyse (2011). What is the Difference between Qualitative Research and Quantitative Research? http://www.snapsurveys.com/blog/what-is-the-difference-between-qualitative-research-and-quantitative-research/. Retrieved on September 16, 2011.
