Basic Data Analysis with Regression
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The document discusses standardizing estimated task times by analyzing 2018 project data. Pivot tables and charts were used to analyze time spent by project and task type. Scatter plots and best-fitting curves helped create estimated time formulas. The formulas allow estimated times to be easily evaluated when opening new tasks, and are updated biquarterly. Realistic estimations are now available for new task owners.
Basic Data Analysis with Regression
- 1. Indus Tasks Data Analysis & Estimated Time Standardization 1 Idea: • The estimated vs. resolution time differences in some projects and/or tasks. • Standardization of the estimated time for each project and task type. Analysis & Methodology: • Gathered 2018 data by the help of CQ queries and team planning table. • Created a pivot table and pivot chart to see the projects’ data in terms of time and task type. • Analyzed the data for each project to create estimated time formulas via scatter plots and best fitting curves. What is Improved? • The progress of the time spent can easily be evaluated via dynamic pivot charts. (See table) • Estimated time for each new Indus task can easily be evaluated via formulas while opening a CR in JIRA. Future and Optional Effects/Benefits? • Estimation formulas are being updated based on latest status biquarterly. • Realistic time estimations is available for new-starter Indus task owners for current projects. • Similar methodology with different parameters might be used for different tasks. M.Buğra KANMAZ Time Spent per Dataset (hrs) Project/Year 2019* 2018 Progress SYMC 0,79 1,26 -37% PSA 0,81 1,15 -30% DAG 0,72 0,74 -2% HMC 3,00 1,98 52% * as of W25