correlation analyis

8/13/2019 Correlation analyis

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A

Project report

On

Information Communication Technical Lab

Project on Excel Sheet

Submitted by:- Submitted To :-

DEEPA GURNANI NAVEEN SHARMA SIR

MBA 1ST

Semester

2013-2015

Engineering college, Bikaner

(An autonomous institute of Govt of Rajasthan)


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CORRELATIONCorrelation is a measure of the statistical relationship between two comparable time series. For

investors, these series may be two commodities, two stocks, a stock and an index or even a stock

and a commodity. The relationship, which can be causal, complementary, parallel or reciprocal, is

stated as the correlation coefficient and always reflects the simultaneous change in value of the

pairs of numerical values over time

The standard deviation must be converted into a relative measure of dispersion for the purpose of

Comparison measure is known as the COFFICIENT of variation.

The coefficient of variation is the ratio to the standard deviation to the mean expressed in

Percentage and is denoted by c.v. symbolically:

Coefficient of variation (c.v.)=/x*100

ACCORDING TO KARL PEARSON," coefficient of variation is the percentage variation

In Mean, standard deviation being considered as the variation in the mean."

X Y

55 23

34 66

67 55

90 89

78 99 CORRAL= 0.748643

45 46

23 3344 22

22 11


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PRICE IN C PRICE IN CITY B

20 10

22 20

19 18 CORRAL= - 0.04428

23 12

16 15

REGRESSION

In statistics, regression analysis is a statistical process for estimating the relationships among

variables. It includes many techniques for modeling and analyzing several variables, when the focus

is on the relationship between a dependent variable and one or more independent variables. More

specifically, regression analysis helps one understand how the typical value of the dependent

variable (or 'Criterion Variable') changes when any one of the independent variables is varied, while

the other independent variables are held fixed. Most commonly, regression analysis estimates the

conditional expectation of the dependent variable given the independent variables that is, the

average value of the dependent variable when the independent variables are fixed. Less commonly,

the focus is on a quantile, or other location parameter of the conditional distribution of the

dependent variable given the independent variables. In all cases, the estimation target is a function

of the independent variables called the regression function. In regression analysis, it is also of

interest to characterize the variation of the dependent variable around the regression function

which can be described by a probability distribution

FOR EXAMPLE,IF one knows that the yield of rice and rainfall are closely related then one want to

know the amount of rain required to achieve a certain production.

DEPENDENT VARIABLEis the single variable being explained / predicted by the regression model.

INDEPENDENT VARIABLE is the explanatory variable(S) used to predict the dependent variable.

According to Blair," regression is the measure of the average relationship between two or more

variables in term of the original units of the data.


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70 78

80 98

90 99

SUMMARY OUTPUT

Regression Statistics

Multiple R 0.989584

R Square 0.979276

Adjusted R 0.976315

Standard E 4.755949

Observatio 9

ANOVA

df SS MS F gnificance F

Regression 1 7481.667 7481.667 330.7684 3.76E-07

Residual 7 158.3333 22.61905

Total 8 7640

Coefficien Standard E t Stat P-value Lower 95 Upper 95 Lower 95. Upper 95.0%

Intercept 1.833333 3.455117 0.530614 0.612097 -6.33672 10.00339 -6.33672 10.00339


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STANDARD DEVIATION

The standard deviation concept was introduced by KARL PEARSON 1823. it is most important andwidely used measure of studying dispersion.

Standard deviation also knows as root mean square deviation.

1. A measure of the dispersion of a set of data from its mean. The more spread apart the data, the

higher the deviation. Standard deviation is calculated as the square root of variance.

2. In finance, standard deviation is applied to the annual rate of return of an investment to measure

the investment's volatility. Standard deviation is also known as historical volatility and is used by

investors as a gauge for the amount of expected volatility

STANDARD DEVIATION

item no.

1 72 9

3 16

4 24 Standers D 9.141481

5 26

N=15 X= 82

STANDARD DEVIATION

MARKS No. Of Stu fx

10 8 80

20 12 240

30 20 600

40 10 400 standard d 18.56438

50 7 350

60 3 180

X= 210 f=60 fx=1850


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Quartile

In descriptive statistics, the quartiles of a ranked set of data values are the three points that

divide the data set into four equal groups, each group comprising a quarter of the data. Aquartile is a type of quantile. The first quartile (Q1) is defined as the middle number

between the smallest number and the median of the data set. The second quartile (Q2) is

the median of the data. The third quartile (Q3) is the middle value between the median and

the highest value of the data set.

In applications of statistics such as epidemiology, sociology and finance, the quartiles of a

ranked set of data values are the four subsets whose boundaries are the three quartile

points. Thus an individual item might be described as being "in the upper quartile".

QUARTILES

X F

22 1

25 1

26 1

28 2 QRT. 28

30 3

31 1

34 1

10

QUARTILES

X F

1 1

2 1

3 1

4 2 QRT. 4

5 3

6 1

7 1


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Percentile

A percentile (or a centile) is a measure used in statistics indicating the value below which a

given percentage of observations in a group of observations fall. For example, the 20th

percentile is the value (or score) below which 20 percent of the observations may be found.

The term percentile and the related term percentile rank are often used in the reporting of

scores from norm-referenced tests. For example, if a score is in the 86th percentile, it is

higher than 86% of the other scores.

The 25th percentile is also known as the first quartile (Q1), the 50th percentile as the

median or second quartile (Q2), and the 75th percentile as the third quartile (Q3). In

general, percentiles and quartiles are specific types of quantiles.

PERCENTAILE

AGE NO. OF PERSONS

10 15

20 30

30 50

40 75 PER. 75

50 100

60 110

70 115

80 125

PERCENTAILE

AGE NO. OF PERSONS

11 15

22 30

33 50

44 75 PER. 85

55 100

66 110

77 115

88 125


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Decile

A method of splitting up a set of ranked data into 10 equally large subsections. This type of

data ranking is performed as part of many academic and statistical studies in the finance

field. The data may be ranked from largest to smallest values, or vice versa.

DECILES

X

70

80

90 DEC. 2521.429

60

50

40

95

DECILES

X F

11 20

10 30

9 40 DEC. 9002

8 50

7 60

6 70

5 80


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MEDIANIf a group of n observation is arranged in ascending or descending order of magnitude,

then the middle value is called medianof these observation and is denoted by M or ME.

SERIAL NU MONTHLY EXPENDITURE (IN RUPEES)

1 132

2 140

3 144

4 136

5 148

N=5 X= 700

MEDIAN 140

R.NOS. MARKS(X)

1 40

2 50

3 55

4 78

5 58 MEDIAN 31.5

6 60

7 73

8 35

9 43

10 48

N=10


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MODE

The word mode is made from the FRENCH LANGUAGE LA MODE which means fashion

of system. The value of the variation for which the frequency is maximum is called mode or

modal value and is denoted by z or mo.

St. No. Marks Obtained

1 10

2 27

3 24

4 12

5 27 MODE 27

6 27

7 20

8 18

9 15

value of it frequency

8 5

9 6

10 8

11 7 mode 812 9

13 8

14 9

15 6


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BAR CHART


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PIE CHART


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LINE CHARAT


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SCATTER CHART

product item producte sales

salt 10

sugar 20

oil 50

colgate 40

pepsodent 30

BAJAJ OIL 20

0

10

20

30

40

50

60

0 1 2 3 4 5 6 7

producte sales

producte sales

correlation analyis

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