Correlation Flashcards
Correlation
Association between two variables
Understanding associations between variables
Monitoring performance
Predictive modelling
Decision Making Process
Association
Correlation analysis (r)
Strength of relationship
Continuous Variables
X and Y are independent of each other
Regression Analysis (R2)
Line of best fit
Cause and effect
X and Y are not independent
Correlation Analysis
Measures the strength of association between two variables
Correlation Coefficient:
Denoted by r or R
Always has a value between
0 and +1 or 0 and -1
Coefficient of Determination:
Denoted by r2 or R2
Always has a value between
0 and 1 or 0% and 100%
Correlation Analysis
The value of the correlation coefficient indicates the sense and the strength of the association
A value close to +1 shows a strong positive linear association between two factors
High values of one variable commonly occur when there are high values of the other
Correlation Analysis
A value close to -1 (-0.9 for example) shows a string negative linear association
High values of one factor commonly occur when there are low values of the other
Correlation Analysis
The Excel function (fx)
=correl(range of variable 1, range of variable 2)
Relationships between Variables
Y = a + bx
Y is the variable plotted vertically
X is the variable plotted horizontally
A is a number ( the “constant”)
Which is the intercept with y axis
B is also a number - this number measures the slope of the line
Calculate values for a and b for the Line if ‘best fit’
Regression Analysis
R = 0.96
Slope = 9.5
Intercept = 4.5
Regression Model
Estimated sales next month = y = a + bx, y = 4.5 + 9.5x
Factors which can influence demand
Price
Competitors’ prices
Time of year
Availability of substitutes
Advertising expenditure
Quality of the product
Weather
Sporting Events
