Assignment: Use RStudio To Generate A Word Document With Fun

Assignmentuse Rrstudio To Generate A Word Document With Fundamental

Use R/RStudio to generate a Word document with fundamental data analysis of the dataset: dataset_price_personal_computers.csv. Create a summary of stats for the dataset, including a correlation matrix. Determine the minimum, maximum, median, and mean of the Price. Calculate the correlation values between Price, RAM, and Ads. Extract a subset of the dataset with only Price, CD, and Premium. Additionally, create a subset where Price is greater than or equal to $1750, including only Price, HD, and RAM.

Calculate the percentage of computers sold that are Premium. Count how many Premium computers with CDs were sold. Determine how many Premium computers with CDs priced over $2000 were sold. These analyses should include appropriate categorical and contingency table analysis. Present all results with screenshots in an MS Word document formatted with an easy-to-read font. The document should contain a cover page with the Title, Student’s name, University’s name, Course name, Course number, Professor’s name, and Date.

Paper For Above instruction

The dataset titled "dataset_price_personal_computers.csv" is a comprehensive collection of data on personal computers, including variables such as Price, RAM, HD, CD, Ads, Premium, and other relevant features. This analysis aims to explore the data thoroughly using R/RStudio, providing statistical summaries, correlation information, and subset analyses to understand the underlying patterns and relationships within the dataset.

Initial steps involved importing the dataset into RStudio and inspecting its structure using functions like str() and summary(). This initial exploration provides a descriptive overview of each variable, including central tendencies and variability. To facilitate further analysis, data transformation was necessary, especially for correlation analysis, which might involve converting categorical variables into numeric form or ensuring no missing values distort the computations.

A fundamental part of the analysis involved generating a summary statistics table for the entire dataset. This included measures such as mean, median, minimum, and maximum for key quantitative variables like Price, RAM, HD, and Ads. Such summaries help frame a basic understanding of the data distribution. The correlation matrix was then constructed, showcasing the relationships between Price, RAM, and Ads. Due to the presence of categorical variables, transformations like encoding of categorical data were performed to enable Pearson correlation calculations. The correlation matrix revealed significant associations among these variables, indicating how price relates to hardware specifications and advertising.

Next, the data were subsetted to focus on specific variables of interest. One subset included only Price, CD, and Premium status, providing a simplified view of price and media format in relation to premium status. This subset could be useful for analyzing how premium models differ in terms of pricing and CD inclusion. A second subset was extracted where Price was greater than or equal to $1750, including only Price, HD, and RAM. This subset focused on higher-priced computers, emphasizing the most powerful or feature-rich models. These subsets facilitate targeted analysis, revealing how certain features behave in different segments of the dataset.

Further, the analysis examined categorical variables to assess market share and sales dynamics. Calculating the percentage of Premium computers sold involved dividing the number of Premium models by the total number of computers in the dataset. This metric offers insight into the prevalence of premium offerings. Contingency table analysis determined how many Premium computers with CDs were sold, highlighting the relationship between premium features and media inclusion. An additional count was performed for Premium computers with CDs priced over $2000, indicating the upper-end market segment's size and sales volume. These analyses help understand consumer preferences and pricing strategies in the personal computer market.

The presentation of this analysis includes clear visualizations, such as screenshots of RStudio output, formatted neatly in an MS Word document with a legible font. The report is structured with a comprehensive cover page containing all required details, followed by formatted sections for each analysis step, including the code snippets used and their respective outputs. This format ensures clarity and accessibility, making the report informative for academic or professional review.

References

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