Scatter plot

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What is Scatter plot?

Scatter plots show the relationship between two variables by displaying data points on a two-dimensional graph. The variable that might be considered an explanatory variable is plotted on the x axis, and the response variable is plotted on the y axis.
Scatter plots are especially useful when there is a large number of data points. They provide the following information about the relationship between two variables:
•Shape – linear, curved, etc.
•Direction – positive or negative
•Presence of outliers

Case corresponding

1)Relationship between the results and the factors
2)Relationship between some characteristics and other characteristics
3)Relationship between Two characteristics for some results

Creating a scatter plot

We Measure data of two Data name is X,Y
We need the data of more than 30 sets n>30
Seek maximum value, the minimum value
Create the horizontal axis, vertical axis.
Plot the data
Fill out the number of data, date and time, purpose, product name, process name, department,
and the person in charge

The point of view of the scatter plot

Sample Scatter Plot File→scatter plot

(a) Positive correlation              %title
Increases x and y at the same time
Therefore Positive correlation

(b)Negative correlation
X increases Y decreases
Therefore Negative correlation

(c)Likely a Positive correlation


(d)Likely a Negative correlation

(e)No correlation

Notes scatter plot


(a)Check of abnormal points

If the Cause is measurement errors and entry errorsis delete
then The Data must delete
(b)Require stratification

Correlation coefficient

In statistics, the Pearson product-moment correlation coefficient is a measure of thecorrelation (linear dependence) between two variables X and Y, giving a value between +1 and −1 inclusive. It is widely used in the sciences as a measure of the strength of linear dependence between two variables.
Formula as follows:

-1 ≦ r ≦ +1

The point of view of Correlation coefficient

Judgment criteria of correlation coefficient is as follows:
0.8 ≦| r | → Close correlation
0.6 ≦| r | <0.8 → Usual correlation
0.4 ≦| r | <0.6 → Not close correlation
| r |<0.4 → Not correlation
Correlation coefficient 
r=0.975665 %title
Close correlation

%titler= 0.8
Usual correlation


r= 0.39
Not correlation

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