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📊 B.Com Business Statistics 1 – Complete Theory Lecture for Degree Students Welcome to this detailed lecture on Business Statistics 1, an important subject in the B.Com degree program. This video is designed to help students understand the basic statistical concepts used in business decision-making in a simple, clear, and exam-oriented manner. Business Statistics is the application of statistical methods and techniques to collect, analyze, interpret, and present business data. It helps managers and organizations make informed decisions based on numerical evidence rather than assumptions. The subject forms the foundation for data analysis, forecasting, and strategic planning in business environments. In this course, students learn about data collection methods, including primary and secondary data, and different types of data such as qualitative and quantitative data. The subject also explains the classification and tabulation of data, which helps in organizing large amounts of information into meaningful formats. Another important area of study is measures of central tendency, including mean, median, and mode, which represent the central value of a dataset. Students also learn about measures of dispersion, such as range, variance, and standard deviation, which indicate how data values are spread around the average. Business Statistics 1 also introduces graphical and diagrammatic representation of data, including bar charts, pie charts, histograms, and frequency polygons. These visual tools help in presenting complex data in a clear and understandable way. The subject further covers index numbers, time series analysis, and basic probability concepts, which are essential for forecasting trends and analyzing business performance. Understanding these statistical tools helps students in financial analysis, market research, and operational planning. Overall, Business Statistics 1 provides a strong analytical foundation for commerce students, preparing them for advanced subjects in finance, economics, accounting, and business analytics.