Techniques of Collecting Data: || Graphical Method of Presenting Data:
Outline:
Introduction
What is the purpose of summarizing? We summarize data to "simplify" it and easily identify what appears "normal" and what appears "abnormal." The distribution of a variable tells what values it accepts and how frequently it accepts these values.
For summarizing data, these are the most widely used basic numerical measurements.
Relative Frequency
The ratio (fraction or proportion) of the number of times a data value occurs in the set of all outcomes to the total number of outcomes is called relative frequency. Fractions, percents, and decimals can all be used to express relative frequencies. When you're looking to compare categories within a table, this is what you'll utilize it for.Mathematically, this can be written below:
Relative Frequency = (Frequency / Total (N))
Percentage (%)
The word 'percent' literally means 'out of a hundred.' Percentages, like fractions and decimals, are used in mathematics to describe components of a whole. When working with percentages, the entire is divided into one hundred equal pieces. To signify that a number is a percentage, the symbol percent is used, and the abbreviation ‘pct' is used less frequently. The formula is shown below:
Percent (%) = (Frequency / Total (N)*100)
Ratio
The ratio is a comparison of two quantities of the same units that shows how much of one quantity is contained in the other. The ratio can be determined with the aid of the formula below:
Ratio = Number of cases of the first category / Number of cases of the second category
Rates
The number of actual occurrences of an event divided by the number of possible occurrences per unit of time is the definition of rates. The formula is as follows:
Rates = (Number of Actual Occurrences / Number of Occurrences per unit Time) * 10^n
Rate of Change
The rate of change can be used to compare the actual change over time. The formula is:
Rate of Change = ((Frequency of New Cases - Frequency of Old cases) / Frequency of Old cases) * 100
Common method of summarizing ratio or interval data
We can decide how to best summarize or explain a variable by first understanding what type of data it contains. The two most helpful approaches of expressing the distribution of data, Measures of Central Tendency and Measures of Variability, can be used to summarize the continuous and interval scales.
RELATED POST: «Types of Variables»
The typical: The data's center–or middle–is described in this manner. A "measure of central tendency" is another term for this approach of describing the center. The density of the data distribution around the center is described by the spread of values around the center. This is also known as a "measure of dispersion."
Measure of Central Tendency
A single number that seeks to represent a set of data by identifying the center position within that set of data is referred to as a measure of central tendency. As a result, central tendency measures are also known as central location measures. They're classified as summary statistics as well.
Types of Measure of Central Tendency
Measure of Dispersion
A summary statistic that indicates the amount of dispersion in a dataset is known as a measure of variability. What is the degree of dispersion of the values? Measurements of variability define how far away the data points tend to fall from the center, whereas measures of central tendency characterize the mean value. Variability is discussed in the context of a value distribution. A low dispersion value shows that the data points are firmly grouped around the center. They tend to fall further apart when there is a lot of dispersion.
Types of Measure of Variability/Dispersion
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Thank you for reading! Any thoughts about method of summarizing data. I'd love to hear your comment.
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