Correlation is a statistical measure that indicates the extent to which two or more variables fluctuate together. A positive correlation indicates that the variables increase or decrease in parallel, while a negative correlation indicates that one variable increases as the other decreases. A correlation of -0.97 represents a strong negative correlation, while 0.10 represents a weak positive correlation. Correlations above 0.4 are generally considered relatively strong. Even if there is no linear relationship between two variables, it does not necessarily mean that no relationship exists at all.

1. What do you mean by correlation?
Correlation is a statistical measure that indicates the extent to which two or more variables
fluctuate together. A positive correlation indicates the extent to which those variables increase
or decrease in parallel; a negative correlation indicates the extent to which one variable
increases as the other decreases.
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2. What is a correlation example?
Positive correlation exists when two variables move in the same direction. A basic example of
positive correlation is height and weight—taller people tend to be heavier, and vice versa. ...
In other cases, the two variables are independent from one another and are influenced by a
third variable.
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3. What is strong correlation?
When the r value is closer to +1 or -1, it indicates that there is a stronger linear relationship
between the two variables. A correlation of -0.97 is a strong negative correlation while a
correlation of 0.10 would be a weak positive correlation.
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4. Is 0.4 is a strong correlation?
This is a strong positive relationship; the correlation coefficient is 0.9408. ... For this kind of
data, we generally consider correlations above 0.4 to be relatively strong; correlations between
0.2 and 0.4 are moderate, and those below 0.2 are considered weak.
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5. How do you interpret correlation?
Interpret SPSS output for correlations: Pearson's r - YouTube
https://www.youtube.com/watch?v=pgQ9T_SdzoQ
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6. APPLICATIONS
Correlation is used to describe the linear relationship between two continuous
variables (e.g., height and weight). In general, correlation tends to be used when there
is no identified response variable. It measures the strength (qualitatively) and direction
of the linear relationship between two or more variables.
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7. Pearson's Correlation Coefficient was designed just for
linear relationships. Technically, if we get a low value,
or a zero value, it does not mean that there is no
relationship. It just means that there is no linear
relationship
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8. QUIZ
1. If the Pearson Product Moment Correlation Coefficient shows
zero value, this definitely means that there is no
relationship between the two variables
a) True
B) False
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9. What is degree of correlation?
The degree of intensity of relationship between two variables is measured with
the coefficient of correlation. They are as follows: Perfect correlation: If two
variables change in the same proportion (increase or decrease), then the
correlation between them is perfect correlation.
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18. Links
1. https://www.youtube.com/watch?v=Ypgo4qUBt5oA
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19. Problem 1: Based on simple method of
finding out Correlation Coefficient
( with average)
Calculate karl Pearson’s coefficient of correlation from
the following data and interprets it value
Marks in Pharmacognosy: 48 35 17 23 47
Marks in Statistics : 45 20 40 25 45
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20. Problem 2: Based on Direct method of
finding out Correlation Coefficient
Calculate coefficient of correlation from the data given
below by the direct method, i.e ., without taking the
deviations of items from actual or assumed mean.
X 9 8 7 6 5 4 3 2 1
Y 15 16 14 13 11 12 10 8 9
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21. Problem 3: Based on indirect method of finding out
Correlation Coefficient( Assumed mean)
Calculate the coefficient of correlation between X and Y
From the following data.
X 78 89 99 60 59 79 68 61
Y 125 137 156 112 107 136 123 108
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22. Problem1. based on regression analysis ( By direct method)
From the following data, obtain two regression equations
X 6 2 10 4 8
Y 9 11 5 8 7
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23. Problem2. Based on regression analysis ( by actual mean method)
The following data relate to the scores obtained by 9 salesmen of a company
in an intelligence test and their weekly sales in thousands rupees:
a. Obtain the regression equation of sales on intelligence test scores of
salesmen.
b. If the intelligence score of a salesmen is 65, what would be his
expected weekly sales
Sal. Int A B C D E F G H I
Test [X]
Scores
50 60 50 60 80 50 80 40 70
Weekly
Sales [Y]
30 60 40 50 60 30 70 50 60
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24. Problem3. Based on regression analysis ( by assumed mean method)
The following table shows ages [X] and blood pressure [Y] of 8
persons:
Obtain the regression equation of Y on X and find the expected
blood pressure of a person who is 49 years old
Ages [X] 52 63 45 36 72 65 47 25
BP [Y] 62 53 51 25 79 43 60 33
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TERMINOLOGY IN STATISTICS
1.Predictor variable or Explanatory variable or Regressor
variable is also known as independent variable used in
regression analyses.
2. Response variable is also known as dependent variable. Other
names are
a. Regressand variable
b. Predictand variable
c. Explained variable

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QUIZ
1. In regression analysis, the variable that is being
predicted is the
A. Response, or dependent, variable
b. Independent variable
c. Intervening variable
d. Is usually x

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2. The correlation coefficient is used to determine:
a. A specific value of the y-variable given a specific value
of the x-variable
b. A specific value of the x-variable given a specific value
of the y-variable
C. The strength of the relationship between the x and y
variables
d. None of these