3. Data presentation
Data presentation is an extension of data cleaning, as it involves arranging the data for easy analysis. Here, you can use descriptive statistics tools to summarize the data. Data presentation can also help you determine the best way to present the data based on its arrangement.
4. Data analysis
Data analysis involves manipulating data sets to identify patterns, trends and relationships using statistical techniques, such as inferential and associational statistical analysis. You can use computer software like spreadsheets to automate this process and reduce the likelihood of human error in the statistical analysis process. This can allow you to analyze data efficiently.
Related: What Is Data Analytics?
5. Data interpretation
The last step is data interpretation, which provides conclusive results regarding the purpose of the analysis. After analysis, you can present the result as charts, reports, scorecards and dashboards to make it accessible to nonprofessionals. For example, the interpretation of the analysis of the impact of a 6,000-worker factory on crime rate in a small town with a population of 13,000 residents can show a declining rate of criminal activities. You may use a line graph to display this decline.
4 common statistical analysis methods
Here are four common methods for performing statistical analysis:
Mean
You can calculate the mean, or average, by finding the sum of a list of numbers and then dividing the answer by the number of items in the list. It is the simplest form of statistical analysis, allowing the user to determine the central point of a data set. The formula for calculating mean is:
Mean = Set of numbers / Number of items in the set
Example: You can find the mean of the numbers 1, 2, 3, 4, 5 and 6 by first adding the numbers together, then dividing the answer from the first step by the number of figures in the list, which is six. The mean of the numbers is 3.5.
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