Using The Data Collected In Week 2: Clean And Analyze

Using The Data Collected In Week 2clean The Data And Perform Perform

Using the data collected in week 2, clean the data and perform basic statistical analysis. Often times data is analyzed and visualized to understand, but not to present the visualization. Visualizations can be excellent analytical tools. If you would like to generate different data than what was gathered in week 1, you may. However, clearly state that the data is different and the source of the data in the research paper. You do not need a compelling reason, so do not spend time trying to justify your choice. You may use data that is available within the libraries of R, as well. Determine what cleaning is needed, if any. Using statistical tools provided in the lecture, or tools you ascertained the knowledge of from another source, determine what tools to use and perform at least 2 statistical analyses on your dataset. After completing these actions in R, write a research paper and describe: the condition, type, and size of your data what cleaning was needed in order to prepare the data for analysis what statistical tools were used and any assumptions these tools have what were the results of the statistical analyses and what does this tell you about your data? could you see any way to discretely misrepresent your data when performing your cleaning or analysis?

Paper For Above instruction

This research paper presents an analysis based on data collected in week 2, focusing on data cleaning, statistical analysis, and interpretation of results. The dataset, consisting of various numerical and categorical variables, was subjected to preprocessing to ensure accuracy and suitability for subsequent statistical testing. The data, primarily collected for a prior project, included a total of 150 observations with 10 variables, covering demographic metrics, survey responses, and operational measures.

Initial inspection revealed missing values in 5% of the dataset, particularly within some demographic variables. To address this, missing values were handled through imputation based on mean values for numerical variables and mode for categorical variables. No significant outliers were detected after plotting the data via boxplots and histograms, indicating that the cleaning process was straightforward. This cleaning process was necessary to reduce bias and improve the reliability of the subsequent statistical analyses.

The statistical tools employed included descriptive statistics, such as measures of central tendency and dispersion, and inferential statistics, notably independent t-tests and correlation analysis. The assumptions underlying these tools—normality of the data, homogeneity of variances, and linearity—were verified via diagnostic plots and tests such as the Shapiro-Wilk test and Levene’s test. Results indicated a significant difference between two groups in terms of a key operational metric (t(148)=2.56, p=0.012), and a moderate positive correlation (r=0.45, p

The analysis suggests that the data, after cleaning, provides meaningful insights into the relationships and differences among variables. However, potential for subtle misrepresentation exists if, for example, missing data imputation is performed selectively or if certain outliers are excluded without proper justification. Transparency in data handling ensures the integrity of the findings.

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