AIT 664 George Mason University Information Representation Processing Visualization Paper – Description
Respond to at least one classmate’s posting. Your response(s) should be no more than 250 words, and must be thoughtful, substantial, and more extensive than a simple “well done” or “I agree.” Consider points of agreement, disagreement, assumptions, and value judgments. If you disagree with a post, then say so, and explain your rationale tactfully.
Original instructions classmate needs to respond to below:
Your Information Modeling Report—Part 4 of your analysis project—is due this module. Tell us about your Information Modeling efforts. The following are some examples of the kinds of things you might want to discuss:
Describe your exploratory data analysis efforts.
What have you learned so far? For example, did you find any expected relationships among any of the variables?
Were there any surprising findings? Did you find that you needed to perform any additional data cleaning?
Do you need to gather any additional data? Do you need to perform any additional data normalization?
Have you prepared any visualizations so far? Feel free to post one or more of them, and summarize your interpretations of them.
Classmate Response to the question below:
Data loading and analysis:
A CSV file containing the dataset was imported into R Studio.
The head() function was used to display the first few rows in order to examine the dataset’s structure.
We checked the column names, data types, and any missing values.
Summary figures:
The describe() function was used to calculate the mean, median, standard deviation, minimum, and maximum values for numerical variables.
How to Manage Missing Data:
In the dataset, missing or null values were found.
The handling of missing data was consistent with how the rest of the data was handled.
Relationship Analysis:
Variable relationships were examined.
To investigate relationships between variables, scatter plots and correlation matrices were developed.
The z-score method was used to locate outliers.
Primary Findings:
Gender and Occupation: The dataset offers details on the number of employees and their weekly salaries broken down by gender and occupation. In some professions, one gender predominates over the other, whereas in others, there are more female employees than male employees.
Weekly Wages and Occupation: varied occupations had varied weekly wages. The average weekly income for some professions, including “Chief executives,” is higher than the average for all workers.
Occupation Groups: The information is grouped into more general occupation groups, illustrating variations in the average weekly salaries among industries.
Specific Job names and Categories: During analysis, it was taken into account that the “Occupation” column contains both specific job names and broad categories.
Higher Earning Disparities: Some jobs within the same category paid much more than others.
Further data cleaning
Missing values (NAs) in the “All_weekly” column needed to be handled in a way that was consistent with how the rest of the data was handled.
No more normalisation was required, and we are currently working on the visualisations. The visualisation will offer insightful information about the connections between occupation, gender, and income. The information regarding the mentioned variables, their statistics, and their correlations to one another will most likely be presented as a line or bar graph in the visualisation.
The post AIT 664 George Mason University Information Representation Processing Visualization Paper first appeared on .
