Scatter Plot

Skill level: Intermediate

Description

The scatter plot provides a visual representation of the relationship between two variables; it lets you “see” the patterns in data and confirm or negate some assumptions.

Benefits

  • Easy to see patterns in the data
  • Easily created with basic software applications, such as spreadsheets
  • Simple and practical analysis of data
  • Applicable to many problems in the service industry (finance, sales, marketing, etc.)

How to Use

  • Step 1.  Collect the data.
  • Step 2.  Structure the data for analysis, preferably in columns.
  • Step 3.  Use the appropriate menu tool to create the graphic (scatter diagram).
  • Step 4.  Draw your conclusion based on the graph and data pattern. Determine if there is a correlation and what type of correlation there is (see charts below).

Relevant Definitions

Correlation: Data are correlated when the result of one item depends on another value. For example, the stronger you press on the brake pedal, the faster the car will stop. Braking distance is correlated with force applied on the brake pedal.

Types of correlation:
Scatter_Plot_Figure1

Example

A large retail store is considering a major expansion in many towns and is preparing a hiring plan. The human resources manager wants to know what strategy to use, considering that the corporation will require that the new stores become profitable within a few months after opening.

Based on past experience and tribal knowledge, management wants to hire sales people experienced with similar merchandise rather than young people without experience. Hiring experienced people will require more screening, research, and recruitment time and will increase the payroll.

The management team seeks to learn if the level of sales really depends on employee experience, or if it is just an assumption. The entire recruiting strategy depends on this information.

They decide to pull all available data from the accounting system and analyze it to determine if experience matters.

Below is a summary of the data showing average sales (represented in units of $100) based on years of experience in retail sales. The average number of years of experience comes from more than 1,000 employees working the floor at various locations.

Scatter_Plot_Figure2

The scatter plot shows a very strong correlation between the years of experience and the level of sales. But it also indicates that beyond 14 years, the level of sales does not increase much. It tends to stay flat from that point on. Therefore, hiring more experienced people will increase sales, with the optimal amount of experience being 12 to 15 years.

 

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