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Lesson 2 of 19

Types of Measurement Scales

Depending on the information we want the data to represent, we can choose one of the four measurement scales.

Nominal Scale

  • Used to classify data
  • Observations are put into categories based on some criteria.
  • The category labels can be numbers but they don’t have any numeric value.
  • Example 1: Classifying stocks as small-cap, mid-cap, and large-cap
  • Example 2: Classifying funds as equity funds, debt funds, and balanced funds.

Ordinal Scale

  • Used to classify and order (Ranking)
  • Observations are not just classified but also ordered
  • Example: Ranking top 10 stocks based on their P/E ratio
  • The numbers only represent the order. They do not say anything about how much better or worst a stock is at a given number compared to one at a lower number.

Interval Scale

  • Used to classify and order with an equal interval scale
  • The intervals between adjacent scale values are equal.
  • Scale has an arbitrary zero point and as a result you cannot calculate ratios.
  • Example: Temperature scales. A temperature of 40 degrees is higher than 35 degrees and is higher by 5 degrees.
  • The problem is that a temperature of 0 degrees does not imply absence of temperature. Because of this, a temperature of 20 degrees does not necessarily mean twice as hot as a temperature of 10 degrees.

Ratio Scale

  • All the above features along with an absolute zero.
  • Equal units of measurements and a rational zero point for the scale.
  • Example: Income of a group of people in dollars. If you have 0 dollars that means complete absence of money (what we are measuring). However, if A has $10 and B has $20, then B has twice as much money as A has.
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Descriptive Vs. Inferential Statistics

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Parameter, Sample Statistic, and Frequency Distribution

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Statistical Concepts and Market Returns

19 lessons

Lessons

1
Descriptive Vs. Inferential Statistics
2
Types of Measurement Scales
3
Parameter, Sample Statistic, and Frequency Distribution
4
Relative Frequencies and Cumulative Relative Frequencies
5
Properties of a Data Set (Histogram / Frequency Polygon)
6
Measures of Central Tendency
7
Calculating Arithmetic Mean
8
Calculating Weighted Average Mean
9
Calculating Geometric Mean
10
Calculating Harmonic Mean
11
Calculating Median and Mode of a Data Set
12
Quartiles, Quintiles, Deciles, and Percentiles
13
Range and Mean Absolute Deviation
14
Variance and Standard Deviation
15
Chebyshev’s Inequality
16
Coefficient of Variation
17
Sharpe Ratio
18
Skewness and Kurtosis
19
Relative Locations of Mean, Median and Mode

Quizzes

Statistical Concepts and Market Returns
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