This document discusses various statistical concepts including correlation, regression, index numbers, and time series analysis. It defines key terms such as positive and negative correlation, simple and multiple regression, base and current years for index numbers, and the components of time series including trends, seasonal variations, and cyclical fluctuations. Various methods are also listed for studying these statistical concepts like scatter diagrams, least squares regression, and moving averages.

1. BUSINESS STATISTICS & OR - II

2. THEORY QUESTION & ANSWERS

3. CORRELATION

4. What is Correlation?
Correlation is a statistical tool which
studies the relationship between two
variables.

5. Define Correlation
According to Simpson & Kafka:
Correlation analysis deals with the
association between two or more
variables.

6. List out the types of correlation.
Positive & Negative Correlation
Simple, Partial and Multiple
Correlation
Linear and Non-linear
Correlation

7. What is Positive Correlation?
Positive correlation refers to the change
of variables in the same direction. Both
the variables are increased or decreased
in the same direction.

8. What is negative correlation?
Negative correlation refers to the change of
variables in the opposite direction.
The correlation is said to be negative or
inverse, if two variables X and Y deviate in the
opposite direction.

9. What is simple correlation?
Simple correlation is a measure used
to determine the strength and the
direction of the relationship between
two variables, X and Y.

10. What is multiple correlation?
A statistical technique that predicts values
of one variable on the basis of two or more
other variables.

11. What is partial correlation?
Partial correlation is a method used to
describe the relationship between two
variables whilst taking away the effects
of another variable, or several other
variables, on this relationship.

12. List out the methods of studying
correlation.
 Scatter Diagram
 Graphic method
 Karl Pearson’s co-efficient of correlation
 Spearman’s Rank correlation
 Concurrent deviation method

13. What do you mean by rank
correlation?
A rank correlation coefficient measures the
degree of similarity between two rankings, and
can be used to assess the significance of the
relation between them.

14. REGRESSION:

15. Define regression
According to Blair:
Regression is the measure of the
average relationship between two or
more variables in terms of the original
units of the data.

16. List out the uses of
regression.
It studies the relationship of two or
more variables.
It is widely useful for quality control in
corporate sector.

17. What is simple regression?
The regression analysis confined to the
study of only two variables at a time is
called the simple regression.

18. What is multiple regression?
The regression analysis for studying more
than two variables at a time is known as
multiple regression.

19. What is independent variable?
The known variable which is used to
estimate an unknown variable.

20. What is dependent variable?
The unknown variable for which the
value is to be predicted.

21. List out any two importance of
regression.
It can be applied in companies for
quality control.
To find out an unknown variable from
the group of known variables.

22. INDEX NUMBERS:

23. Define index numbers.
According to Croxton & Cowden:
Index numbers are devices for measuring
differences in the magnitude of a group
of related variables.

24. List out uses of index
numbers.
Index numbers help in studying trend.
Index numbers help management in
policy formulation.
Index numbers help in deflating
various values.

25. What is price index?
Price index numbers measure the
relative changes in prices of the
commodities between two periods.

26. What is quantity index
numbers?
These index numbers are considered
to measure changes in the physical
quantity of the goods produced,
consumed or sold of an item.

27. What are the methods of index
numbers?

28. What is base year & current
year?
The year selected for comparison is the
base year and the year for which
comparisons are required is the current
year.

29. TIME SERIES:

30. What is time series?
Time series is a series of statistical
data recorded in accordance with their
time of occurrence.

31. Define time series.
According to Croxton & Cowden:
A time series consists of data
arranged chronologically.

32. List out some of the uses of time
series.
 It helps in forecasting and planning.
 It helps in decision making.
 It helps in making comparative studies.
 It helps in the analysis of past behaviour
of a variable.

33. What are the components of
time series?
Secular trend.
Seasonal variations.
Cyclical fluctuations.
Irregular variations.

34. What do you mean by secular
trend?
The general tendency of the time series
data to increase or decrease or
stagnate during a long period of time is
called the secular trend.

35. What is seasonal variations?
Seasonal variation is variation in a time
series within one year that is repeated
more or less regularly. Seasonal
variation may be caused by the
temperature, rainfall, public holidays,
cycles of seasons or holidays.

36. What is cyclical variations?
Cyclical variations are the oscillatory
movements in a time series with period of
oscillation greater than one year. These
variations in a time series are due to ups
and downs recurring after a period greater
than one year.

37. What are the methods of
measuring trend?
Free-hand Method.
Semi-average Method
Moving Average Method.
Method of Lease Square.

38. What do you mean by semi
average?
The original values of time series are
divided in to two equal parts (if odd
number ignoring the middle value) and
averages are calculated both the parts,
are called semi averages.

39. What is meant by moving
average?
A moving average is defined as an
average of fixed number of items in the
time series which move through the
series by dropping the top items of the
previous averaged group and adding
the next in each successive average.

40. What do you mean by least
square?
Least Square is the method for finding
the best fit of a set of data points. It
minimizes the sum of the residuals of
points from the plotted curve. It gives
the trend line of best fit to a time series
data.