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Data Cleaning

Data cleaning ensures data collection is accurate, data cleaning or data scrubbing can help detect and correct inaccurate records in database. A known error within the database affects the results in the statistics and findings within the file and is misleading in the results. Performing a data cleaning identifies any inaccurate piece of the data that needs modification or deletion of data set. Several steps occur through the process of data cleaning, which include data auditing, workflow specifications, workflow execution, post-processing, and controlling.

The use of data auditing will identify any abnormalities within the data set and location, such as parsing. Workflow specification detects and removes abnormalities within dataset, which is vital to achieving the product of high quality data. The workflow execution carry’s out once specification is completed, and corrections in dataset have been verified. The post-processing and controlling completed after the execution of data cleaning. The process is to continue to check for data errors and controlling database files going forward.

Variance and Standard Deviation

According the Lind, Marchal, and Wathen (2011) mean deviation, variance, and “standard deviation use all the values in a data set and are all based on deviations from the arithmetic mean” (Statistics for Business and Economics, p. 73). Mean deviation “measures the mean amount by which the values in a population” vary from the mean (Lind et al., 2011, p. 73). The population standard deviation and variance are also based on mean deviations, but squares

the deviations rather than using the total value of deviations. The “arithmetic mean of the squared deviations from the mean” is the population variance and the “square root of the variance” is the population standard deviation (Lind et al., 2011, p. 76).

The...