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Introduction to Biostatistics

-statistics are simply a collection of tools that researchers employ to help answer research questions

INFERENTIAL STATISTICS: COMMON TESTS

CORRELATION

Correlation

  • Correlation refers to a type of statistic, to find out about a particular relationship between two variables.
  • The correlation coefficient is written as r.
  • Correlation requires that both variables be quantitative.
  • Correlation Coefficient, r measures the direction and the strength of the linear association between two numerical paired variables.
  • Values range between –1 and +1 and measure the strength and direction of the relationship.
  • A positive relationship exists when both variables increase and decrease at the same time. (Weight and height).
  • A negative relationship exist when one variable increases and the other variable decreases or vice versa. (Strength and age).
  • A scatter plot is used to explain the relationship between dependent and independent variable.
  • By convention, the independent variable is plotted on the horizontal x-axis.  The dependent variable is plotted on the vertical y-axis.
  • Independent variable: is a variable that can be controlled or manipulated.
  • Dependent variable: is a variable that cannot be controlled or manipulated.  Its values are predicted from the independent variable.

positive_correlation

negative correlation

no correlation

Strength of linear Relationship


r value

Interpretation

0

No correlation

+1

Perfect positive linear relationship

-1

Perfect negative linear relationship

Example

Student

Hours studied

% Grade

A

6

82

B

2

63

C

1

57

D

5

88

E

3

68

F

2

75

  1. Independent variable in this example is the number of hours studied.
  2. The grade the student receives is a dependent variable.
  3. The grade student receives depend upon the number of hours he or she will study.
  4. Are these two variables related?

scatter plot

 

 
     
   

 


 
 
 
 
           
 

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