Answer of Question no. 1 (a).
Difference between
Primary Data and Secondary Data:
Primary Data: Data which are collected for the first time for a
specific purpose are known as Primary data. For example: Population census,
National income collected by government, Textile Bulletin (Monthly), Reserve
bank of India Bulletin (Monthly) etc.
Secondary Data: Data which are collected by someone else, used in
investigation are knows as Secondary data. Data are primary to the collector,
but secondary to the user. For example:
Statistical abstract of the Indian Union, Monthly abstract of statistics,
Monthly statistical digest, International Labour Bulletin (Monthly).
Some of the differences of Primary Data and Secondary Data
are given below:
a)
Primary data are those which are collected for the first time and thus
original in character. While Secondary data are those which are already
collected by someone else.
b)
Primary data are in the form of raw-material, whereas Secondary data
are in the form of finished products.
c)
Primary data are collected directly from the people related to enquiry
while Secondary data are collected from published materials.
d)
Data are primary in the hands of institutions collecting it while they
are secondary for all others.
Answer of Question no. 1 (c).
Measure of dispersion may be broadly classified into
two types:
a)
Absolute measures of dispersion: It is classified into
(i)
Range
(ii)
Mean
Deviation
(iii)
Standard
Deviation
(iv)
Quartile
Deviation
b)
Relative measures of dispersion: It is classified into
(i)
Coefficient
of Range
(ii)
Coefficient
of Mean Deviation
(iii)
Coefficient
of Variation
(iv)
Coefficient
of Quartile Deviation.
Difference between
absolute and relative measure of dispersion:
a)
Absolute
measures are dependent on the unit of the variable under consideration whereas
the relative measures of dispersion are unit free.
b)
For
comparing two or more distributions, relative measures and not absolute
measures of dispersion are considered.
c)
As compared
to absolute measures of dispersion, relative measures of dispersion are
difficult to compute and comprehend.
Answer of Question no.2(a).
Regression line
A line of
regression by the method of “least square” shows an average relationship
between variables under study. This regression line can be drawn graphically or
derived algebraically. A line fitted by method of least square is known as the
line of best fit. There are two regression lines:-
Regression
line of x on y: - Regression line of x on y is used to predict x for a given
value of y. The regression equation of x on y is x=a+by.
Regression line
of y on x: - Regression line of y on x is used to predict y for a given value
of x. The regression equation of y on x is y=a+bx
Two regression
lines:
There are
two lines of regression: - x on y and y on x. For these lines, the sum of the
square of the deviations between the given values and their corresponding
estimated values obtained from the line is least as compared to other line. One
regression line cannot minimise the sum of squares for both the variables that
is why we are getting two regression lines. (We get one regression line when r
= +1 and Two regression lines will be at right angles when r = 0.)
Answer of Question no. 2(c)
Correlation and Regression
Correlation
is the degree of the relationship between two or more variables. It does not
explain the cause behind the relationship.
Regression is the measure of the average relationship between two or
more variable in terms of the original units of the data. It is a statistical
tool with the help of which the unknown values of one variable can be estimated
from known values of another variable.
There
are some basis difference between correlation and regression:
(1) Nature of relationship: - Correlation explains the degree of
relationship, whereas regression explains the nature of the relationship.
(2) Causal relationship: - Correlation does not explain the cause
behind the relationship whereas regression studies the cause and effect
relationship.
(3) Prediction: - Correlation does not help in making prediction
whereas regression enable us to make prediction.
(4) Origin and scale: - Correlation coefficient is independent of the
change of origin and scale, whereas regression coefficient is independent of
change of origin but not of scale.
Answer of Question no. 3(a)
Index Number
Index number is an indicator of
changes in prices and quantities. It is a specialized average designed to
measure the change in a group of related variables over a period of time. It is
also an indicator of inflationary or deflationary tendencies.
Uses of Index number:
a)
Measurement
of change in the price level or the value of money: - Index number can be used
to know the impact of the change in the value of money on different sections of
the society.
b)
Knowledge of
the change in standard of living: - Index number helps to ascertain the living
standards of people. Money income may increase but if index number show a
decrease in the value if money. Living standard may even decline.
c)
Adjustment
in salaries and allowances: - Cost of living index number is a useful guide to
the government and private enterprises to make necessary adjustment in salaries
and allowances of the workers.
d)
Useful to
business community: - Price index numbers serve as a useful guide to the
business community in planning.
e)
Information
regarding foreign trade: - Index of exports and imports provides useful
information regarding foreign trade.
Answer of Question no. 3(c)
Limitation of index number
a)
Not completely true: - Index number
not fully true. The index number simply indicate arithmetical tendency of the
temporal changes in the variable.
b)
International comparison is not
possible: - Different countries have different bass of index numbers; these do
not help international comparisons.
c)
Difference of time: - With the passage
of time, it is difficult to make comparison of index number. With the changing
time man’s habits.
d)
Limited use: - Index numbers are
prepared with certain specific objective. If they are used for another purpose
they may lead to wrong conclusion.
e)
Lack of retail price index number: -
Most of the index numbers are prepared on the basis of wholesaler prices. But
in real life, retail prices are most relevant, but it is difficult to collect
retail prices.
Answer of Question no. 4(b)
Components of time
series
1. Secular trend: - Secular trend is a long term trend which has the
basic tendency to grow or decline over a period of time. It may be due to
population change technological progress, large scale shifts in consumer
tastes, discovery of new things, etc.
2. Seasonal variation: - Seasonal variations are those periodic
movements in business activity, which occur regularly every year and have their
origin in the nature of the year itself. It may be due to climate weather
conditions, customs, traditions and habits, festivals, etc.
3. Cyclical variation: - The term cycle refers to the recurrent
variations in time series that usually last longer than a year and are regular
neither in amplitude nor in length. Cyclical fluctuations are long-term
movements that represent consistently recurring rises and declines in activity.
It has four important characteristics:
i) Prosperity
ii) Decline
iii) Depression
iv) Improvement
4. Irregular variation or irratic movement: - It is the variation in business activities, which do not repeat in a definite pattern. Floods, earthquakes, strikes and wars cause it.
Answer of Question no. 4(d).
Methods for Measuring trends
The following four methods
are commonly used for measuring trends:-
i)
Graphic method
ii)
Semi-average method
iii)
Moving average method
iv)
Method of least squares.
i) Graphic method: - This is the simplest method of studying trend. The procedure of
obtaining a straight line trend is:
a) Plot
the time series on a Graph.
b)
Examine the direction of the trend based on the plotted information.
c) Draw
a straight line which shows the direction of the trend.
The
trend line thus obtained can be extended to predict future values.
ii) Semi-average method: - Under this method, the given data is divided into two parts. After
that an average of each part is obtained which gives two points. Each point is
plotted at the mid-point of the class interval covered by the respective part
and then the two points are joined by a straight line which gives the required
trend line.
iii) Method of moving average: - Under this method the average value for a certain time span is
secured and this average is taken as the trend value for the unit of time
falling at the middle of the period covered in the calculation of the average.
While using this method it is necessary to select a period for moving average.
iv) Method of Least Square: - This method is most commonly used method of measuring trend. It is a
mathematical method and a trend line is fitted to the data in such a manner
that the following two conditions are satisfied:-
i) the
sum of deviation of the actual values from their respective mean is zero.
ii) the
sum of square of the deviations of the actual and compute values is least from
this line. That is why this method is called method of least square.
The straight line trend is represented by the equation:
Y = a + bx
Where, y = denotes the trend values
a
= represents the intercept on y axis.
b=
represents slope of the trend line.
Answer of Question no. 5(a).
Purpose of Business
Forecasting
Business forecasting refers to
the analysis of past and present economic conditions with the object of drawing
inferences about probable future business conditions. The process of making
definite estimates of future course of events is referred to as forecasting and
the figure or statements obtained from the process is known as ‘forecast’
future course of events is rarely known. In order to be assured of coming
course of events, help is taken of an organised system of forecasting.
(a) Future Prediction: Forecasting is a part of human conduct.
Businessmen also need to look to the future. Success in business depends on
correct predictions. In fact when a man enters business, he automatically takes
with it the responsibility for attempting to forecast the future.
To a very large extent, his success or failure would depend upon the
ability to successfully forecast the future course of events. Without some
element of continuity between past, present and future, there would be little
possibility of successful prediction. But history is not likely to repeat
itself and we would hardly expect economic conditions next year or over the
next ten years to follow a clear cut prediction. Yet, frequently past patterns
prevail sufficiently to justify using the past as a basis for predicting the
future.
(b) Reducing Uncertainty: A
businessman cannot afford to base his decisions on guesses. Forecasting helps a
businessman in reducing the areas of uncertainty that surround management
decision making with respect to costs, sales, production, profits, capital
investment, pricing, expansion of production, extension of credit, development
of markets, increase of inventories and curtailment of loans. These decisions
cannot be made off-hand. They are to be based on present indications of future
conditions.
(c) Measurement of Risk: However, we should know that it is impossible
to forecast the future precisely. There is a possibility of occurrence of some
range of error in the forecast. Statistical forecasts are the methods in which
we can use the mathematical theory of probability to measure the risks of
errors in predictions.
There are two
assumptions:
i) There is
no sudden jump in figures from one period to another
ii) There is
regularity in fluctuations and the rise and fall is uniform
Answer of Question no.
5(b).
Methods of Business Forecasting: The following are
the main methods of business forecasting.
1.
Business
barometers
2.
Time series
analysis
3.
Extrapolation
4.
Regression
analysis
5.
Modern
econometric methods
6.
Exponential
smoothing method
Business
barometers: Business indices are constructed to study and
analyse the business activities on the basis of which future conditions are
predetermined. As business indices are the indicators of future conditions, so
they are also known as “business barometers” or ‘economic barometers’. With the
help of these business barometers the trend of fluctuations in business
conditions are made known and by forecasting a decision can be taken relating
to the problem.
The
construction of business barometer consists of gross national product,
wholesale prices, consumer prices, industrial production, stock prices, and
bank deposits. These quantities may be converted into relatives on a certain
base. The relatives so obtained may be weighted and their average is computed.
The index thus arrived at in the business barometer.
Time
series analysis: Time series
analysis is also used for the purpose of making business forecasting. The
forecasting through time series analysis is possible only when the business
data of various years are available which reflects a definite trend and
seasonal variation. By time series analysis the long term trend, secular trend,
seasonal and cyclical variations are ascertained, analysed and separated from
the data of various years.
Extrapolation: Extrapolation is the simplest
method of business forecasting. By extrapolation, a businessman finds out the
possible trend of demand of his goods and also about the future price trends.
The accuracy of extrapolation depends on two factors:
i) Knowledge
about the fluctuations of the figures
ii)
Knowledge about the course of events relating to the problem under
consideration
Thus, there
are two assumptions on which extrapolations are based:
i) There is
no sudden jump in figures from one period to another
ii) There is
regularity in fluctuations and the rise and fall is uniform
In
extrapolation, we assume that the variable will follow the established pattern
of growth. For the purpose of business forecasting, it is to determine
accurately the appropriate trend curve and the values of its parameters.
Answer of Question no. 5(c)
Business forecasting
refers to the analysis of past and present economic conditions with the object
of drawing inferences about probable future business conditions. The process of
making definite estimates of future course of events is referred to as
forecasting and the figure or statements obtained from the process is known as
‘forecast’ future course of events is rarely known. In order to be assured of
coming course of events, help is taken of an organised system of forecasting.
The following are two aspects of scientific business forecasting.
a)
Analysis of past economic conditions
b)
Analysis of present economic conditions
Objectives of forecasting in business:
(a) Future Prediction
(b) Reducing Uncertainty
(c) Measurement of Risk
Characteristics of business forecasting
a) Based on past and present conditions: Business
forecasting is based on past and present economic condition of the business. To
forecast the future, various data, information and facts concerning to economic
condition of business for past and present are analysed.
b) Based on mathematical and statistical methods: The
process of forecasting includes the use of statistical and mathematical
methods. By using these methods, the actual trend which may take place in
future can be forecasted.
c) Period: The forecasting can be made for long
term, short term, medium term or any specific period.
d) Estimation of future: The
business forecasting is to forecast the future regarding probable economic
conditions.
e) Scope: The forecasting can be physical as
well as financial.
Answer of Question no. 5(d)
Sales Forecasting
In general terms, forecasting means “A statement made about the future”.
So, Sales forecasting is the estimation of sales made for the future. Sales
forecast is an estimate of sales in rupees or in units for future period. A
sales forecast is the prediction of sales volume that a company can estimate to
achieve in specified period of time in future.
According to American marketing Association, “Sales forecast is an
estimate of sales in dollars (Rupees in India) or physical units for a
specified future period”.
Sales forecasting is the basis of all business activities. All the
business activities may it a sales related matter, production related matter,
finance, advertising, etc depend on sales forecasting. Any business firm starts
its plan with sales forecasting .Sales forecasting is a self-assessment tool
for a company. The future
direction of the company depends on sales forecasting. The importance of Sales forecasting can be stated
as follows:
a)
Overstocking and under stocking of materials can be maintained by a good
inventory control.
b)
With the help of sales forecasting, sales opportunities can be found out
on the basis of the forecast.
c)
All the activities in an organization are controlled on the basis of
forecasting.
d)
Advertising and sales promotion expenses are based on sales forecasting.
e)
Sales forecasting is also important in the field of personnel
department. The number of sales persons, executives etc can be increased or
decreased on the basis of sales forecasting.
Demand Forecasting
Forecasting is the process to predict how the
future needs, which include the requirement in the size of the quantity,
quality, time and location that is required in order to meet the demand for
goods or services.
Demand Forecasting is the demand for products that
are expected to be realized for a certain period in the future. Inforecasting we must pay
attention to forecasting procedures should be implemented, namely :
Determining
the purpose of forecasting.
a)
Choose the
item “independent demand” to be predicted. Plot data intoscatter diagram.
b)
Selecting
“forecasting method” in accordance with the pattern of data for its intended
purpose.
c)
Counting
errors are to be performance of each method used, can be known.
d)
The
selection of the best method, which has the smallest error rate.
e)
Make
predictions of future demand, then perform the test verifies that the
forecasting results carried out a representative to the past data