###### Quantiles and Related Visualizations

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Measures of central tendency are values that tend to occur at the center of a well-ordered data set. As such, some analysts call them measures of central location. The mean, the median, and the mode are all measures of central tendency, though there are situations where one of them is more appropriate than the others. Among the three measures, the mean is the most common and can also be subdivided into smaller sub-types, as we shall see shortly.

A population includes all of the elements from a set of data. A sample, on the other hand, consists of a few observations drawn from the population. For example, all domestic equity mutual funds available in a country’s market is an example of population. If 15 domestic equity mutual funds are selected from among all the domestic equity mutual funds, then this is a sample.

- Because it is very difficult to collect data from every element in the population, samples can be used to represent the population.
- The use of a sample saves time and makes the analysis of a large population manageable.

A **parameter** refers to a measure that is used to describe a characteristic of the **population. **It’s a numerical quantity that describes a given aspect of the population as a whole.

A **statistic**, on the contrary, is a measure that describes a characteristic of a **sample. **This could be the average value or the sample standard deviation of the sampled items. Researchers use sample statistics to estimate the unknown population parameters. As an example, we often estimate the actual population mean using the sample mean.

The **population mean** is the summation of all the observed values in the population, \(\sum{X_i}\)_{ }divided by the total number of observations, \(N\). The population mean defers from the **sample mean**, which is based on a few observed values ‘\(n\)’ that are chosen from the population. Thus:

$$\begin{align} \text{Population mean} &=\cfrac { \sum { { X }_{ i } } }{ N }\\ \text{Sample mean} &=\cfrac { \sum { { X }_{ i } } }{ n } \end{align}$$

Analysts use the sample mean to ** estimate **the actual population mean.

The population mean and the sample mean are both arithmetic means. The arithmetic mean for any data set is unique and is computed using all the data values. Among all the measures of central tendency, it is the only measure for which the sum of the deviations from the mean is **zero**.

The following are the annual returns on a given asset realized between 2005 and 2015.

{ 12% 13% 11.5% 14% 9.8% 17% 16.1% 13% 11% 14% }

1. Calculate the population mean.

2. Compute the sample mean assuming the returns for the first 7 years are unknown, i.e., we only have 13%, 11%, and 14%.

**Solution**

$$ \begin{align*} \text{Population mean} & =\cfrac {(0.12 + 0.13 + 0.115 + 0.14 + 0.098 + 0.17 + 0.161 + 0.13 + 0.11 + 0.14)}{10} \\ & = 0.1314 \text{ or } 13.14\% \\ \\ \text {Sample mean} & = \cfrac {(0.13 + 0.11 + 0.14)}{3} \\ & = 0.1267 \text{ or } 12.67\% \\ \end{align*} $$

A commonly-sighted demerit of the arithmetic mean is that it’s not resistant to the effects of extreme observations or what we call ‘outsider values.’ For instance, consider the following data set:

{1 3 4 5 34}

The arithmetic mean is 9.4, which is greater than most of the values. This is due to the last extreme observation, i.e., 34.

- The sum of the deviations around the mean always equals 0.
- The arithmetic mean is highly sensitive to extremely large or small observations (outliers).

The trimmed mean is a measure of central tendency in which we calculate the mean by excluding a small percentage of lowest and highest values. For example, we calculate the mean in a 5% trimmed mean by removing the lowest 2.5% and the highest 2.5% of values.

The Winsorized mean is a measure of central tendency calculated by assigning a stated percentage of the lowest values equal to one specified low value and a stated percentage of the highest values equal to one specified high value. In the same way as the trimmed mean, this approach removes a significant number of outliers from the data set.

The weighted mean takes into account the weight of every observation. It recognizes that different observations may have **disproportionate effect**s on the arithmetic mean. Thus:

$$ \text{Weighted mean} = \sum { { X }_{ i }{ W }_{ i } } $$

A portfolio consists of 30% ordinary shares, 25% T-bills, and 45% preference shares with returns of 7%, 4%, and 6% respectively. The portfolio return si *closest* to:

**Solution**

The return of any portfolio is always the weighted average of the returns of individual assets. Thus:

$$ \text{Portfolio return} = (0.07 × 0.3) + (0.04 × 0.25) + (0.06 × 0.45) = 5.8\% $$

The geometric mean is a measure of central tendency, mainly used to measure growth rates. We define it as the n^{th} root of the product of n observations:

$$ \text{GM} ={ \left( { X }_{ 1 }\ast { X }_{ 2 }\ast { X }_{ 3 }\ast { X }_{ 4 }\ast …\ast { X }_{ n-1 }\ast { X }_{ n } \right) }^{ \frac { 1 }{ n } } $$

The formula above only works when we have **non-negative** values. To solve this problem, especially when dealing with percentage returns, we add 1 to every value and then subtract 1 from the final result.

An ordinary share from a certain company registered the following rates of return over a 6-year period:

{ -4% 2% 8% 12% 14% 15% }

The compound annual rate of return for the period is *closest* to:

**Solution**

$$ \text{Geometric Mean} = (0.96 × 1.02 ×1.08 × 1.12 × 1.14 × 1.15)^{\frac{1}{6}} = 1.0761 – 1 = 0.0761 \text{ or } 7.61 % $$

**Computing geometric mean using BA II Plus™ Financial Calculator:**

Enter 1.55280 [y^{x}] 6 [1/x] [=]

Where 1.55280 = 0.96 × 1.02 ×1.08 × 1.12 × 1.14 × 1.15

- The geometric mean is always less than or equal to the arithmetic mean.
- The geometric mean is equal to the arithmetic mean when there is no variability in the observations (when all the observations in the series are the same).

- The gap between the geometric mean and the arithmetic widens as the variability of values increases.
- For estimating the average return over a one-period horizon, the arithmetic mean should be used.
- For estimating the average returns over more than one period, the geometric mean should be used.

The relationship between the arithmetic and geometric is given by:

$$\bar{X}_G \approx \bar{X}-\frac{s^2}{2}$$

Where:

\(\bar{X}_G\) = Geometric mean.

\(\bar{X}\) = Arithmetic mean.

\(s^2\) = Sample variance.

The above equation shows that the larger the variance of the sample, the wider the difference between the geometric mean and the arithmetic mean.

Analysts use the harmonic mean to determine the average growth rates of economies or assets. If we have \(N\) observations:

$$ \text{HM} = \cfrac {N}{ \left(\sum { \frac { 1 }{ { X }_{ i } } } \right)} $$

For the last three months of 2015, the price of a stock was $4, $5, and $7 respectively. The average cost per share is *closest* to:

**Solution**

$$\text{Harmonic Mean}=\frac{3}{\left(\frac{1}{4}+\frac{1}{5}+\frac{1}{7}\right)}=\$ 5.06$$

- \(\text{Harmonic mean < Geometric mean < Arithmetic mean when returns are variable.}\)
- \(\text{Harmonic mean = Geometric mean = Arithmetic mean when returns are constant.}\)
- \(\text{Arithmetic mean × Harmonic mean = Geometric mean}\)

The median is the statistical value located at the center of a set of data that has been organized in the order of magnitude. For an odd number of observations, the median is simply the **middle value**. If the number of observations is even, the median is the **middle point** (average) of the two middle values. Unlike the arithmetic mean, **the median is resistant to the effects of extreme observations**.

The following are the annual returns on a given asset realized between 2005 and 2015.

{ 12% 13% 11.5% 14% 9.8% 17% 16.1% 13% 11% 14% }

The median is *closest* to:

**Solution**

First, we arrange the returns in ascending order:

{ 9.8% 11% 11.5% 12% 13% 13% 14% 14% 16.1% 17% }

Since the number of observations is even, the median return will be the middle point of the two middle values:

$$\frac{13\%+13\%}{2}=13\%$$

The main advantage of of the median is that the median is less affected by outliers than the mean Therefore, the median is useful in describing data that follow a non-symmetric distribution, such as a skewed distribution, which we will see later in this reading.

- The median only focuses on the relative position of the ranked observation and ignores the rest of the information about the size of the observations.

The mode is simply the value that occurs most frequently in a set of data. On a histogram, it is the highest bar. A set of data may have a mode or none, e.g., the returns in the example above. One of its major merits is that it can be determined from incomplete data, provided we know the observations with the highest frequency.

If a distribution has two modes, it is called bimodal. If the distribution has the three most frequently occurring values, then it is called trimodal.

An interval with the highest frequency is called the modal interval (or intervals) in a frequency distribution. In a histogram, the modal interval always has the highest bar. The mode is the only measure of central tendency that can be used with nominal data.

Determine the mode from the following data set:

{ 20% 23% 20% 16% 21% 20% 16% 23% 25% 27% 20% }

**Solution**

The mode is simply 20%. It occurs 4 times, a frequency higher than that of any other value in the data set.