PRC 02 QM BY SIR ALI SHAHID DAY 7 SESSION 2

Data Classification and Types
📊 Data is categorized into Numerical (numbers) and Categorical (text). It is further classified as Discrete (gaps exist, cannot be broken down, e.g., number of people) or Continuous (no gaps, measurable on a scale, e.g., age).
📈 Cross-sectional data views a subject at a specific point in time (like a balance sheet), whereas Time-series data compares variables over multiple time periods.
📁 Primary data is collected directly by the researcher, while Secondary data is obtained from third-party sources. Structured data is refined and organized, while Unstructured data remains in raw form.

Data Organization and Summarization
📋 Arrays organize raw data in ascending or descending sequences to identify range and extremes. Frequency distributions further refine this by grouping data into classes, showing how often values occur.
📏 Tallies are used to count occurrences in groups of five, facilitating easier manual calculation of frequencies for large datasets.
🔍 Class boundaries and midpoints are essential for organizing large datasets into concise, readable tables, reducing complexity and cognitive load.

Graphical Representation of Data
📊 Bar Charts (Simple, Component, and Multiple) effectively visualize categorical comparisons. Pie Charts display data as percentages of a whole.
📉 Histograms represent continuous frequency distributions, while Frequency Polygons connect the midpoints of histogram bars to visualize trends.
📦 Box and Whisker plots provide a snapshot of a dataset using five key metrics: the minimum, maximum, median, first quartile ($Q₁$), and third quartile ($Q₃$).
📈 Ogive graphs are used to plot cumulative frequency, showing the running total of data points.

Key Points & Insights
➡️ Skewness Analysis: A distribution is symmetrical when the mean, median, and mode are clustered. It is positively skewed (right-skewed) if the tail extends to the right (Mean > Median) and negatively skewed (left-skewed) if the tail extends to the left (Mean < Median).
➡️ Skewness Formula: The relationship between these metrics is defined by the formula: $\text{Skewness} = \frac{3(\text{Median} - \text{Mode})}{\text{Standard Deviation}}$.
➡️ Fractile Concepts: Data can be segmented for analysis using Percentiles (100 parts), Deciles (10 parts), or Quartiles (4 parts), which are fundamental for statistical positioning.
➡️ Quality Standards: For data to be useful, it must be complete, relevant, and timely. Utilizing proper graphical tools ensures that data is easy to interpret and reduces errors in business decision-making.

📸 Video summarized with SummaryTube.com on Mar 24, 2026, 11:34 UTC