Answer
of Q.N.1(a).
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.
Methods of collecting primary data:
(a) Direct Personal Observation
(b) Indirect
Oral Investigation
(c) Schedule
and questionnaire
(d) Local
reports
Indirect Oral Investigation: Under this
method, the investigator collects the data from third parties capable of
supplying the necessary information. Sometimes the
information’s refuse to give answer to some direct questions, the information’s
are then collected by putting some indirect questions on informants OR by
interviewing several third persons or witnesses who are expected to know the
full knowledge about the problems under study. E.g. when the businessman are
reluctant to give information’s about their income to income tax authorities
then the authorities (i.e. officers) can get the required information’s from
the persons like salesman, clerks etc who directly involve in that business.
Advantages:This methods
saves time and money because only those persons are interviewed who know the
full facts. Proper training and tactfulness of the investigator may produce
good results.
Disadvantages:The investigator takes too much time in
convincing the persons to supply information’s. In many cases people do not
cooperate and refuse to supply the needed information’s.
Answer of Question no.
1(d).
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).
Methods of
collecting primary data:
(a) Direct Personal Observation
(b) Indirect
Oral Investigation
(c) Schedule
and questionnaire
(d) Local
reports
Direct personal
investigation:In this method the investigation interview. The concerned persons on the
spot about the problems under study and record the required information’s
personally.
Advantage: The data obtained
by this method are highly accurate and reliable and reliable. The accuracy of
the results depends on the efficiency and proper training of the investigator.
The investigator should be polite, tactful and conversant. He should mix
himself with the people and speak the language of the people. In this way he
can get maximum information’s about the problem under study.
Disadvantages: This methods is
very slow, expensive and time consuming and particularly suitable for small scale
and secret inquiries. The personal like and dislike of the investigator may
surely affect the result.
Indirect Oral Investigation: Under this
method, the investigator collects the data from third parties capable of
supplying the necessary information. Sometimes the
information’s refuse to give answer to some direct questions, the information’s
are then collected by putting some indirect questions on informants OR by
interviewing several third persons or witnesses who are expected to know the
full knowledge about the problems under study. E.g. when the businessman are
reluctant to give information’s about their income to income tax authorities
then the authorities (i.e. officers) can get the required information’s from
the persons like salesman, clerks etc who directly involve in that business.
Advantages:This methods
saves time and money because only those persons are interviewed who know the
full facts. Proper training and tactfulness of the investigator may produce
good results.
Disadvantages:The investigator takes too much time in
convincing the persons to supply information’s. In many cases people do not
cooperate and refuse to supply the needed information’s.
Answer of Question no. 2(c)
Properties
of the Correlation Coefficient
a) The correlation
coefficient is symmetrical with respect to X and Y i.e. r_{XY}=r_{YX}
b) The Correlation
coefficient is a pure number and it does not depend upon the units in which the
variables are measure.
c) The correlation
coefficient is the geometric mean of the two regression coefficients. Thus if
the two regression lines of Y on X and X on Y are written as Y=a+bx and X=c+dy respectively then bd=r^{2}.
d) The correlation
coefficient is independent of the choice of origin and scale of measurement of
the variables, i.e. r remains
unchanged if constants are added to or subtracted from the variables and if the
variables having same size are multiplied or divided by the class interval
size.
e) The correlation
coefficient lies between 1 and +1, symbolically 1≤r≤1.
Importance of Statistics
a) It presents fact in a definite form. Numerical
expressions are convincing and, therefore, one of the most important functions
of statistics is to present statement in a precise and definite form.
b) It simplifies mass of figures. The data presented in the form of table,
graph or diagram, average or coefficients are simple to understand.
c) It facilitates comparison. Once the data are simplified they can be
compared with other similar data. Without such comparison the figures would
have been useless.
d)
It helps in
prediction. Plans
and policies of organisations are invariably formulated in advance at the time
of their implementation. knowledge of future trends is very useful in framing
suitable policies and plans.
e) It helps in formulating and testing hypothesis. Statistical
methods like ztest, ttest, X^{2}test are extremely helpful in
formulating and testing hypothesis and to develop new theories.
f) It helps in the formulation of suitable policies. Statistics
provide the basic material for framing suitable policies. It helps in
estimating export, import or production programmes in the light of changes that
may occur.
g) Statistics indicates trend behavior. Statistical
techniques such as Correlation, Regression, Time series analysis etc. are
useful in forecasting future events.
Answer of Question no. 4(a).
Utility of Time Series Analysis
The
analysis of Time Series is of great significance not only to the economist and
businessman but also to the scientist, geologist, biologist, research worker,
etc., for the reasons given below:
a) It helps in
understanding past behaviors: By observing data
over a period of time one can easily understanding what changes have taken
place in the past, Such analysis will be extremely helpful in producing future
behavior.
b) It helps in
planning future operations: Plans for the future
cannot be made without forecasting events and relationship they will have.
Statistical techniques have been evolved which enable time series to be
analyzed in such a way that the influences which have determined the form of
that series to be analyzed in such a way that the influences which have
determined the form of that series may be ascertained.
c) It helps in
evaluating current accomplishments: The
performance can be compared with the expected performance and the cause of
variation analyzed. For example, if expected sale for 1995 was 10,000
refrigerators and the actual sale was only 9,000, one can investigate the cause
for the shortfall in achievement. Time series analysis will enable us to apply
the scientific procedure for such analysis.
d)
It facilitates comparison: Different
time series are often compared and important conclusions drawn there from.
However, one should not be led to believe that
by time series analysis one can foretell with 100percnet accuracy the course of
future events.
Answer
of Question no. 4(d).
(i) 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.
Merits:
i) This is a mathematical method of measuring trend.
ii) Trend values can be obtained for all the given time periods in the
series.
Demerits:
i) This method is more tedious and time consuming.
ii) This method cannot be used to fit the
growth curves.
(ii) Simpleaverage 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 midpoint 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.
Merits:
i) This method is simple to understand as compared to the moving
average method and the method of least square.
ii) This is an objective method of measuring trend as everyone who
applies this method gets the same result.
Demerits:
i) It is affected by extreme values.
ii) This method assumes straight
relationship between the plotted points whether this exist or not.
(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.
Merits:
i) This method is simple to understand and apply.
ii) It is particularly effective if the trend of a series is very
irregular.
iii) It is a flexible method of measuring trend because all figures
are not changed if a few figures are added to the data.
Demerits:
i) Trend values cannot be computed for all years.
ii) No there is no hard and fast rule for selecting the period of
moving average.
iii) this method is not appropriate if the trend situation is not
linear.
Answer of
Question no. 5(b).
Steps in forecasting
The forecasting of business
fluctuations consists of the following steps:
a) Understanding why changes in the past
have occurred: One of
the basic principles of statistical forecasting is that the forecaster should
use the data on past performance. The current rate and changes in the rate
constitute the basis of forecasting. Once they are known, various mathematical
techniques can develop projections from them. If an attempt is made to forecast
business fluctuations without understanding why past changes have taken place,
the forecast will be purely mechanical.
b) Determining which phases of business
activity must be measured: After
understanding the reasons of occurrence of business fluctuations, it is
necessary to measure certain phases of business activity in order to predict
what changes will probably follow the present level of activity.
c) Selecting and compiling data to be used
as measuring devices: There
is an independent relationship between the selection of statistical data and
determination of why business fluctuations occur. Statistical data cannot be
collected and analysed in an intelligent manner unless there is a sufficient
understanding of business fluctuations. It is important that reasons for
business fluctuations be stated in such a manner that is possible to secure
data that are related to the reasons.
d) Analysing the data: Lastly, the data are analysed in the
light of understanding of the reason why change occurs. For example, if it is
reasoned that a certain combination of forces will result in a given change,
the statistical part of the problem is to measure these forces, from the data
available, to draw conclusions on the future course of action. The methods of
drawing conclusions may be called forecasting techniques.
Answer
of Question no. 5(c)
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.
Merits and demerits of time series analysis
Merits

Demerits

It is an easy method of forecasting.

This method is expensive, difficult and time
taking.

By this method a comparative study of variations can be made.

This method deals with past data only.

Reliable results of forecasting are obtained as this method is based
on mathematical model.

This method can only be used when the data for
several years are available.

Regression
analysis
Regression
analysis is the most popular method of forecastin. It 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.
Merits and demerits of Regression analysis
Merits

Demerits

The study of
regression helps the statisticians to estimate the most probable value of one
variable of a series for the given values of the other related variables of
the series.

Regression
analysis is based on the assumption that while computing regression equation;
the relationship between variables will not change.

Regression is
useful in describing the nature of the relationship between two variables .

The
application of regression analysis is based on certain conditions like, for
existence of linear relationship between the variables; exact values are
needed for the independent variable.

Regression
analysis is widely used for the measurement and estimation of relationship
among economic variables.

There
may be nonsense and spurious regression relationships. In such case, the
regression analysis is of no use.

Answer of Question no. 5(d).
Theories of Business Forecasting
There
are a few theories that are followed while making business forecasts. Some of
them are:
a.
Sequence
or timelag theory
b.
Action
and reaction theory
c.
Economic
rhythm theory
d.
Specific
historical analogy
e.
Crosscut
analysis theory
Sequence or timelag theory:
This is the most important theory of business forecasting. It is based
on the assumption that most of the business data have the lag and lead
relationships, that is, changes in business are successive and not simultaneous.
There is timelag between different movements. The table 13.5 lists the merits
and demerits of sequence or timelag theory.
Merits and demerits of
sequence or timelag theory
Merits

Demerits

This method is largely used for business forecasting because of the
accuracy.

This method studies only the action not the reaction.

Though this theory is based on statistical techniques, yet it is
easy to understand.

This method cannot be regarded as accurate because by using
statistical techniques the results can be up to the truth but not an accurate
one.

Timeinterval between two events can be ascertained.


Government can use this technique for the purpose of economic
stability of the economy by exercising control over possible losses.

Action and reaction theory: This theory is based on the following two assumptions.
a) Every action has a reaction
b) Magnitude of the original action
influences the reaction
Thus, if the price of rice has gone up above a certain level in a
certain period, there is a likelihood that after some time it will go down
below the normal level. Thus, according to this theory a certain level of
business activity is normal or abnormal; conditions cannot remain so for ever.
Thus, we find four phases of a business cycle. They are:
i. Prosperity
ii. Decline
iii. Depression
iv. Improvement
Merits and demerits of
action and reaction theory
Merits

Demerits

This is better than other theories.

The determination of normal level is very difficult.

By this theory more reliable results can be obtained because this
theory gives attention to action and reaction of an event.

It is not necessary that reaction is equal to the action.

Economic rhythm theory:
The basic assumption of this theory is that history repeats itself and
hence assumes that all economic and business events behave in a rhythmic order.
According to this theory, the speed and time of all business cycles
are more or less the same and by using statistical and mathematical methods, a
trend is obtained which will represent a long term tendency of growth or
decline. It is done on the basis of the assumption that the trend line denotes
the normal growth or decline of business events.
Merits and demerits of
economic rhythm theory
Merits

Demerits

Forecasting is made on the basis of past conditions, hence they are
more reliable.

The business events are not strictly periodic and prediction of
business cycle on the basis of statistical method is not satisfactory.

This method is helpful in longterm forecasting.

Past conditions are given more weightage than the present
conditions.
