This document discusses scatter plots and their uses: - A scatter plot displays values for two variables using Cartesian coordinates to show their relationship. It can identify if variables are correlated and the strength of their relationship. - Scatter plots are used to analyze paired numerical data and determine if variables are related or have a cause-and-effect relationship. They are also used in regression model building. - Positive correlation means variables increase together, while negative correlation means one decreases as the other increases. No correlation means there is no relationship between the variables.

1. SCATTER PLOT DIAGRAM
Karthik M

2. Scatter plot diagram
• A scatter plot is a mathematical diagram using Cartesian
coordinates to display values for typically two variables for a set of
data.

3. When to use Scatter diagram
• Paired numerical data
• To determine whether the two variable are related
• To identify potential root-cause of problems (whether cause and
effect are related)
• In regression model building, to decide whether to consider
variables for regression

5. Positive correlation
Correlation describes the type of relationship between two data sets.
Positive correlation is a relationship between two variables in which
both variables move in the same direction

6. Age of car (years)
Valueofcar(₹)
Negative correlation is a relationship
between two variables in which one
variable increases as the other decreases,
and vice versa.
Example:
1. As a car gets older it is worth less
2. The weight of a truck (T) and mileage
(kmpl)
Negative correlation

7. No correlation
no relationship, connection
between the two variables
Correlation = 0

8. Why to use Scatter diagram
• Plotting a scatter diagram is first step in looking for relationship
between two variables
• Assessing strength of relationship (strong, neutral, weak)
• Exploring trend and making prediction
• To find out any outliers

9. Trend
• The line of best fit is the line that
comes closest to all the points
on a scatter plot.
• Lines of best fit can be used to
make predictions
• How much money we can
predict to collect if 60 visitors
attended?
No correlation

10. Finding outliers
Outliers are observations which are far
enough away from the mean that they are
noticeably different.
Away from the cluster
Application:
• Might be errors in data entry
• They indicators that something is wrong
with our model.
• To detect fraudlet
We can discard them without harm to study
With outlier: average = 2.8
Without outlier: average = 2.1

11. Positive correlation Negative correlation
as one data set
increase
decreases.
No correlation
• If variables are correlated, points fall in a line or curve
• Better the correlation, tighter the points will hug the line
Reading a Scatter diagram