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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