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Ogive ( cumulative frequency curve) it’s applications
An ogive is a graph that represents cumulative frequencies. It is constructed by plotting cumulative frequencies against class boundaries and then connecting the points with a smooth or straight line. Ogive is a cumulative frequency graph that shows the total number of percentage of data points that fall below a certain value in a data set .Anil Kumar Gupta, (2009).
Ogive curve is a cumulative frequency curve. This can be classified into less than ogive and More than ogive curve. Field, A,(2018)
The topic of the ogive, a cumulative frequency graph, is typically introduced in the context of descriptive statistics and data visualization. This section focuses on graphical tools for summarizing data, and the ogive is often presented as a method to display cumulative frequencies, helping in the analysis of distributions and percentile calculations.Carls & Throne ,(2020)
CONSTRUCTION OF AN OGIVE:
Data Preparation: Group data into class intervals.
Calculate Cumulative Frequency: Add up the frequencies for each class interval progressively.
Plot Points: Mark points for each class interval, with the cumulative frequency on the y-axis and the upper class boundaries on the x-axis.
Draw the Curve: Connect the plotted points to form the ogive curve.
S.k,Mangal (2004)
APPLICATIONS OF OGIVE:
• Understanding Data Distribution: Ogives help visualize the distribution and trends of data, showing how data accumulates across different ranges.
• Determining Percentiles: Ogives can be used to determine percentile values. For example, you can find the median or quartiles by locating the corresponding cumulative frequency on the graph. Field ,A(2018)
• Comparing Data Sets: Multiple ogives can be plotted on the same graph for comparison between different datasets.
• Identifying Skewness: The shape of the ogive (whether it’s steep at the beginning or end) can indicate the skewness of the data.
• Finding Median: The point where the cumulative frequency reaches half of the total frequency gives the median of the data.
• Quantitative Analysis: It’s widely used in quality control and process improvement to analyse and monitor data patterns, particularly in manufacturing and service .Robert B,Burns,2016
Reference
Field, A. (2018). Doing statistics with SPSS. (6th ed.). Sage Publications
Gupta, A. K. (2018). Research design. In Research methodology (3rd ed., pp. 123-145). Himalaya Publishing House.
Mangal, S. K. (2014). Statistics in psychology and education.
Newbold, P., Carlson, W. L., & Thorne, B. M. (2020). Statistics for business and economics (9th ed.). Pearson.
Robert B. Burns, (2016). Doing statistics with SPSS (6th ed.).
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