common sense and care are needed when interpreting the scatter diagrams.
1) Mathematically they may appear to be a relationship, but this does not imply that there is a relationship in reality.
2) The appearance of a mathematical relationship does not imply that there is a casual relationship. An increase in one variable does not necessarily cause an increase or a decrease in the other variable.
1.1.1 Bivariate data
Data connecting two variables are known as bivariate data
1.1.2 Dependent Independent variables
18.104.22.168 Independent data
if one of the variables has been controlled , it is called the independent or explanatory variable
22.214.171.124 Dependent data
The other variable is then dependent or response variable
1.2 Regression function
Having drawn a scatter diagram, you can then look for a mathematical relationship between the variables, y = f(x), where the function of f, known as the regression function
1.2.1 Linear correlation and regression lines
Simplest type of regression function, where y = f(x) is a straight line.
If the points on the scatter diagram appear to lie near a straight line, called a regression line. You would say that there is linear correlation between x and y.
126.96.36.199 Positive linear correlation
y tends to increase as x increases
188.8.131.52 Negative linear correlation
y tends to decrease as x increases
184.108.40.206 No correlation
no relationship between x and y
2 Standard Deviation
The standard deviation (s) is very important and useful measure of spread. It gives a measure of the deviations of the readings from the mean
1) for each reading x, calculate x-mean (its deviation from the mean)
2) square this deviation to give (x-mean)^2. (note that irrespective whether the deviation is negative or positive, this is now positive.
3) find sum of (x-mean)^2
4) find the average by dividing the sum by n, the number of readings. This gives variance.
5) Finally take the positive square root of the variance to obtain the standard deviation.