HW-Movies-2

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Apr 3, 2024

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The Movie Homework 2 Statistical Graphics Yash Goel 5193 9756 1. We want to gain insights from the data through the means of the following two questions: • What is the gross revenue accumulated by various distributors in the year 2017 and 2018, and how does the trend continue throughout the year? • What is the number of tickets sold by various distributors in the years 2017 and 2018? We achieve the goal of analyzing the data with the help of the following visualizations: • Box and whisker plots • Histograms • Bar charts with error bars 2. We utilize the ‘Top Grossing 2017” and “Top Grossing 2018” which contain information about Movies, Release dates, Distributor names, Genre, Gross revenue, and Tickets sold. 3. Visualizations: Figure – 1: Box and Whisker plot to compare Gross Revenue in the years 2017 and 2018 for various distributors (Tableau)

Figure – 2: Histogram plot to compare Gross Revenue in the years 2017 and 2018 for various distributors (Tableau) Figure – 3: Bar graph with error bars to compare Gross Revenue across months in the years 2017 and 2018 for various distributors (Tableau)

Ffigure – 4: Box and Whisker plot to compare Tickets sold in the years 2017 and 2018 for various distributors (Microsoft Excel) Ffigure – 5: Histogram plot to compare Tickets sold in the years 2017 and 2018 for various distributors (Microsoft Excel)

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Figure – 6: Bar graph with error bars to compare Tickets sold in the years 2017 and 2018 for various distributors (Microsoft Excel)

Inference from the visualizations: • Figure 1: Box and whisker plots help us determine the data's skewness. The data for 2017 and 2018 are normally distributed as seen in the visualization. The median revenue in the year 2017 was around 1000M whereas in 2018 it was around 900M. However, the movies collected more revenue in 2018 as the box covers a larger area in the 2018 visualization. • Figure 2: Histogram allows us to compare data across fixed intervals. We can infer that a greater number of movies earned between 1100M-1120M in the year 2017 as compared to 2018 where most of the movies earned between 1300M-1320M. • Figure 3: Error bars guide in validating the accuracy of the data and determining the adequacy of the measured values to the actual values. As depicted in the error bar chart for the year 2017, the month of March has the largest deviation meaning that the average value is far away from the actual values whereas in October the bar length is minimal showing that the average value is close to the actual value. • Figure 4: Box and whisker plots help us determine the data's skewness. It can be observed that the maximum number of tickets sold in the year 2018 was higher than the maximum number of tickets sold in the year 2017. • Figure 5: Histogram allows us to compare data across fixed intervals. It can be inferred that in the year most distributors sold between 10-50M tickets. • Figure 6: Error bars guide in validating the accuracy of the data and determining the adequacy of the measured values to the actual values. The number of tickets sold by Walt Disney is highly distributed and less compact meaning that the actual value is far from the calculated average value, whereas the data is compact for Focus features depicting that the calculated average value is approximately closer to the actual value. Tableau’s configuration for visualizations:

Microsoft Excel’s configuration for visualizations: 4. Reflection points: a) The assignment went smoothly, and the overall difficulty was a little higher than I would have expected as certain tasks required prior statistical knowledge which I had to learn to understand and visualize the data. In comparison, the previous assignments were a lot more manageable compared to this. b) Tableau and Microsoft Excel were used to visualize the datasets. In comparison, Tableau offers easier workflow and could manage large datasets with different parameters as compared to Microsoft Excel. Plotting error bar graphs was relatively easy in Tableau as compared to Microsoft Excel which required certain calculations. c) The most challenging bit about the assignment was visualizing the error bar graph as that required certain statistical knowledge to accurately infer the data. d) The easier part of the assignment would be the visualization of the histogram. e) Visualizing error bar plots was relatively more challenging than I thought as I had to go through several documentation and tutorials to get the desired chart. f) The overall difficulty of the assignment was as I expected, and nothing was easier than it looked. g) This assignment helped teach the core concepts of visualization and how to infer various plots. The comparative analysis of the data for two years helps us understand the underlying problems and suggest some solutions. Yes, the assignment was relatively time-consuming as I had to go through several tutorials and documentation to accurately visualize some of the charts.

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