Method of Semi Averages (2020)

The secular trends can also be measured by the method of semi averages. The steps are:

  • Divide the time series data into two equal portions. If observations are odd then either omit the middle value or include the middle value in each half.
  • Take the average of each part and place these average values against the midpoints of the two parts.
  • Plot the semi-averages in the graph of the original values.
  • Draw the required trend line through these two potted points and extend it to cover the whole period.
  • It is simple to compute the slope and $y$-intercept of the line drawn from two points. The trend values can be found from the semi-average trend line or by an estimated straight line as explained:

Let $y’_1$ and $y’_2$ be the semi-averages placed against the times $x_1$ and $x_2$. Let the estimated straight line $y’=a+bx$ is to pass through the points ($x_1$, $y’_1$) and ($x_2$, $y’_2$). The constant “$a$” and “$b$” can easily be determined. the equation of the line passing through the points ($x_1$, $y’_1$) and ($x_2$, $y’_2$) can be written as:

\begin{align*}
y’ – y’_1 &= \frac{y’_2-y’_1}{x_2-x_1}(x-x_1)\\
&= b(x-x_1)\\
\Rightarrow y’ &= (y’_1 – bx_1) + bx\\
&= a+bx, \quad \text{ where $a=y’_1-bx_1$}
\end{align*}

For an even number of observations, the slope of the trend line can be found as:

\begin{align*}
b&=\frac{1}{n/2}\left(\frac{S_2}{n/2} – \frac{S_1}{n/2} \right)\\
&= \frac{1}{n/2} \left(\frac{S_2-S_1}{n/2}\right)\\
&= \frac{4(S_2-S_1)}{n^2},
\end{align*}

where $S_1$ is the sum of $y$-values for the first half of the period, $S_2$ is the sum of $y$-values of the second half of the period, and $n$ is the number of time units covered by the time series.

The following merits and demerits of the Method of Semi Averages are as described:

Merits of Method of Semi Averages

  • The method of semi-averages is simple, easy, and quick.
  • It smooths out seasonal variations
  • It gives a better approximation to the trend because it is based on a mathematical model.

Demerits of Method of Semi Averages

  • It is a rough and objective method.
  • The arithmetic mean used in Semi Average is greatly affected by very large or by very small values.
  • The method of semi-averages is applicable when the trend is linear. This method is not appropriate if the trend is not linear.

Numerical Example 1: Method of Semi Averages

The following table shows the property damaged by road accidents in Punjab for the year 1973 to 1979.

Year1973197419751976197719781979
Property Damage201238392507484648742
  1. Obtain the semi-averages trend line
  2. Find out the trend values.

Solution

Let $x=t-1973$

YearProperty DamagedSemi TotalSemi AverageCoded YearTrend Values
1973201  0$y’=190+87(0)=190$
19742388312771$y’=190+87(1)=277$
1975392  2$y’=190+87(2)=364$
1976507  3$y’=190+87(3)=451$
1977484  4$y’=190+87(4)=538$
197854918756255$y’=190+87(5)=625$
1979742  6$y’=190+87(6)=712$
method of semi-averages (trend values)

\begin{align*}
y’_1 &= 277, x_1 = 1, y’_2 = 625, x_2=5\\
b&=\frac{y’_2-y’_1}{x_2-x_1}=\frac{625-277}{5-1}=87\\
a&=y’_1 – bx_1 = 277-87(1)=190
\end{align*}

The semi-average trend line $y’=190+87x$ (with the origin at 1973)

Numerical Example 2: Method of Semi Averages

The following table gives the number of books in thousands sold at a bookstore for the years 1973 to 1981

Year197319741975197619771978197919801981
No. of Books Sold423835253224201917
  1. Find the equation of the semi-average trend line
  2. Compute the trend values
  3. Estimate the number of books sold for the year 1982.

Solution

Let $x=t-1973$

YearNo. of books (y)Semi TotalSemi AverageCoded yearTrend Values
197342  0$y’=39.5 – 3(0)=39.5$
197438140351$y’=39.5 – 3(1)=36.5$
1975352$y’=39.5 – 3(2)=33.5$
197625  3$y’=39.5 – 3(3)=30.5$
197732  4$y’=39.5 – 3(4)=27.5$
197824  5$y’=39.5 – 3(5)=24.5$
19792080206$y’=39.5 – 3(6)= 21.5$
198019  7$y’=39.5 – 3(7)=18.5$
198117  8$y’=39.5 – 3(8)=15.5$

\begin{align*}
y’_1 &= 35, x_1=1.5, y’_2=20, x_2=6.5\\
b &= \frac{y’_2 – y’_1}{x_2-x_1} = \frac{20-35}{6.5-1.5} =-3\\
a &= y’_1 – bx_1 = 35 – (-3)(1.5) = 39.5\\
y’&= 39.5 – 3x (\text{with origin at 1973})
\end{align*}

For the year 1982, the estimated number of books sold is $y’=39.5-3(9)=12.5$.

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The Method of Free Hand Curve (2020)

Introduction to Free Hand Curve

The Method of Free Hand Curve is a simple and non-mathematical technique used to fit a curve to a set of data points by visually estimating the trend. It provides a quick, approximate visualization of the relationship between variables.

It is used to

  • Plot Data Points: Scatter plot the data points on a graph.
  • Draw Curve: Using a smooth, freehand line, draw a curve that best represents the overall trend of the data points.
  • No Rigid Rules: The curve is drawn based on the analyst’s judgment, without strict mathematical formulas.

Exploring Data Patterns

This method is subjective and less precise than mathematical curve-fitting techniques but is useful for initial data exploration or when data patterns are irregular.

The secular trend is measured by the method of the free hand curve in the following steps:

  • Take the time periods along the $x$-axis by taking appropriate scaling
  • Plot the points for observed values of the $Y$ variable as the dependent variable against the given time periods
  • Join these plotted points by line segments to get a historigram
  • Draw a free-hand smooth curve (or a straight line) through the histogram

In this method we draw the given times series data on graph paper, then we draw a free-hand trend line through the plotted graph according to the trend of the graph. Then we read trend values from this free-hand trend line.

It is generally preferred to use a curve instead of a straight line to show the secular trend.

Merits (Free Hand Curve)

  • The free-hand curve method is simple, easy, and quick for measuring secular trends.
  • A well-fitted trend line (or curve) approximates the trend closely based on a mathematical model.

Demerits (Free Hand Curve)

  • It is a rough and crude method.
  • It is greatly affected by personal bias as different persons may fit different trends to the same data.
    The estimates are not reliable due to personal bias.

Practical Example of Curve

Question: The following time series shows the number of road accidents in Punjab from 1972 to 1978.

Year1972197319741975197619771978
No. of Accidents2493263826993038374540794688
  • Obtain the historigram showing the number of road accidents and a free-hand trend line by drawing a straight line
  • Find the trend values for this time series

Solution:

Method of Free Hand Curve
YearValueTotalMeanTrend value
19722493  2200
19732638  2550
19742699  2950
1975303823338033403340
19763745  3650
19774079  4050
19784688  4499

The usefulness of the Method of Free Hand Curve

The method of freehand curve is useful for:

  1. Exploratory Data Analysis (EDA): As a preliminary step free free-hand curve method helps us to understand the basic characteristics of the data and identify potential relationships between variables.
  2. Visual Communication: It also helps to present trends in the data in a clear and easily understandable way for non-statistical audiences.
  3. Limited Data: When you have a relatively small dataset, a free hand curve might be sufficient to get a basic idea of the central tendency.

Real-Life Examples of Free-Hand Curve

Some real-life examples where the Method of Free Hand Curve can be applied:

  • Economics:
    • Plotting and estimating trends in stock market prices over time.
    • Visualizing the relationship between inflation and unemployment rates.
  • Meteorology:
    • Drawing temperature trends over days or months based on scattered weather data.
    • Estimating rainfall patterns over a year.
  • Education:
    • Plotting student performance trends over semesters to identify improvements or declines.
    • Visualizing the relationship between study hours and exam scores.
  • Healthcare:
    • Tracking a patient’s blood pressure or glucose levels over time to observe trends.
    • Estimating the progression of a disease based on irregular data points.
  • Business:
    • Visualizing sales trends over months or years to identify seasonal patterns.
    • Estimating the relationship between advertising expenditure and revenue growth.
  • Sports:
    • Plotting an athlete’s performance metrics (e.g., running speed, scores) over time to observe trends.
    • Visualizing the relationship between training hours and performance improvement.

Summary

By understanding the method of free hand curves and its limitations, one can use it as a valuable tool for initial data exploration and visualization alongside other statistical techniques for a more robust analysis. The freehand curve provides a quick, intuitive way to understand trends or relationships without relying on complex mathematical models.

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The Secular Trend Example and Uses (2020)

For the estimation of the secular trend of a time series, the most commonly used method is to fit a straight line $\hat{y} = a+bx$, an exponential curve $\hat{y}=ab^x$, and a second-degree parabola $\hat{y}=a +bx+ cx^2$, etc, where $y$ is the value of a time series variable, $x$ representing the time and all others are constants (the intercept $a$, and the slope $b$). The method of least squares is a widely used method to determine the values of the constants appearing in such an equation.

The Secular Trend is used

  • For prediction (or projection) into the future
  • The detrending process (removal of trend) in a time series for studying other non-trend fluctuations.
  • It is used for historical description

The secular trend can be represented either by a straight line or by some type of smooth curve. It is measured by the following methods:

Least Squares Method (secular trend)

The secular trend may be used in determining how a time series has grown in the past or in making a forecast. The trend line is used to adjust a series to eliminate the effect of the secular trend to isolate non-trend fluctuations.

Note that

  • These trends can be positive or negative. For example, the advancement of technology offers new opportunities but also raises concerns about job displacement and privacy.
  • These trends can be interrelated. For instance, urbanization might be fueled by technological advancements that allow people to work remotely.
  • Identifying secular trends can be challenging, as they unfold over a long period. However, by analyzing historical data, monitoring current developments, and considering expert opinions, one can gain valuable insights into the long-term direction of change.

By understanding and utilizing secular trends, individuals, businesses, and policymakers can make informed decisions, prepare for future challenges, and capitalize on emerging opportunities in a constantly evolving world.

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