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CHAPTER FOUR. Statistics and Sampling

STATISTICS INVOLVES THE STUDY of research designs, the collection of the data, describing the data, analyzing the data, and then forming a conclusion. We are interested mainly in the analysis of data that has already been collected for us by various business systems. We hope to be able to arrive at various conclusions after analyzing the data.

Understanding some basic statistics allows you to understand the makeup or distribution of your data files. This is especially useful when your data file is large and contains millions of records.

There are various types of statistical analysis, but the two major categories are descriptive statistics and inferential statistics.

DESCRIPTIVE STATISTICS

Descriptive statistics is where you describe information from the data set. It is used to summarize the data. Where the data have categories, they can be summarized in each group as to frequency or as a percentage that is a relative frequency. With numerical data, we determine the middle of the data or spread of how close or far the numbers are from that middle. We can determine ranges and possibly determine relationships between two variables. Data can also be summarized to ranges.

There are two main types of data: categorical (qualitative data) or numerical (quantitative data). Categorical data in a record describes qualities or characteristics of the record. For example, in a payroll record, the division or area field that the employee works in is categorical even if the division is coded as a number. Whether the employee is salaried or paid hourly is another categorical data field. Using pivot tables is a great way to see two categorical variables at once in a summarized fashion.

Numerical data includes items such as counts, amounts, or quantities in the record fields. Only actual numerical or quantitative data represent real numbers where calculations are done that make sense. It would not make sense to perform any mathematical operations on items represented by numbers in categorical fields.

Ordinal data is a third type that is a hybrid. The data is in categories, but the categories have a meaningful order. These can be analyzed as categorical data or, if the categories are represented by meaningful numeric values, basic calculations may be performed also. An example would be ranking or ratings such as 1 for poor, 2 for average, and 3 for superior.

 
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