Assignment: Descriptive and Predictive Statistics
Walden University
WMBA-6401: Human Resources Analytics
13:22:22 GMT -05:00
, Assignment: Descriptive and Predictive Statistics
Raw data does not help explain a phenomenon under investigation. However, after
aggregation of the data, it may provide insights into the data. Descriptive statistics is a type of
statistics that is used to describe the many characteristics of data utilized in a study. It takes
historical data and summarizes it into an easily understandable form. The primary purpose of
descriptive statistics is to offer a quick overview of the samples and measures used in a study.
Coupled with several graphic analyses, descriptive statistics play a major role in quantitative data
analysis. Descriptive statistics are significantly different from inferential statistics in that
descriptive statistics are used to describe the data used in a study. On the other hand, inferential
statistics are used to conclude the data used in the study. Descriptive statistics are used to provide
quantitative data analysis in a straightforward manner. Since several variables are usually
measured in a study, descriptive statistics are used to break down the vast amount of data into a
simple form that makes it easy to understand the data. The HR department of an organization can
use various descriptive statistics. For instance, the headcount of all employees is an example of
descriptive statistics. HR may go a step further and break down the employee data by
demographics, which would also be a form of descriptive statistics. Time-to-fill is also a form of
descriptive statistics that can be used by the HR department of an organization. Time-to-fill is a
form of descriptive statistics that shows the number of days it takes to fill a certain vacant
position.
According to Pease, Byerly, and Fitz-enz (2012), combining information from two data
sets that are not usually related may lead to a deeper understanding of the data. The intersection
of the data would provide more insights into the data being analyzed. For instance, using the
above example of the demographics of employees, the organization may decide to structure the
13:22:22 GMT -05:00
Walden University
WMBA-6401: Human Resources Analytics
13:22:22 GMT -05:00
, Assignment: Descriptive and Predictive Statistics
Raw data does not help explain a phenomenon under investigation. However, after
aggregation of the data, it may provide insights into the data. Descriptive statistics is a type of
statistics that is used to describe the many characteristics of data utilized in a study. It takes
historical data and summarizes it into an easily understandable form. The primary purpose of
descriptive statistics is to offer a quick overview of the samples and measures used in a study.
Coupled with several graphic analyses, descriptive statistics play a major role in quantitative data
analysis. Descriptive statistics are significantly different from inferential statistics in that
descriptive statistics are used to describe the data used in a study. On the other hand, inferential
statistics are used to conclude the data used in the study. Descriptive statistics are used to provide
quantitative data analysis in a straightforward manner. Since several variables are usually
measured in a study, descriptive statistics are used to break down the vast amount of data into a
simple form that makes it easy to understand the data. The HR department of an organization can
use various descriptive statistics. For instance, the headcount of all employees is an example of
descriptive statistics. HR may go a step further and break down the employee data by
demographics, which would also be a form of descriptive statistics. Time-to-fill is also a form of
descriptive statistics that can be used by the HR department of an organization. Time-to-fill is a
form of descriptive statistics that shows the number of days it takes to fill a certain vacant
position.
According to Pease, Byerly, and Fitz-enz (2012), combining information from two data
sets that are not usually related may lead to a deeper understanding of the data. The intersection
of the data would provide more insights into the data being analyzed. For instance, using the
above example of the demographics of employees, the organization may decide to structure the
13:22:22 GMT -05:00
