Statistics and Probability Module: Describing the Shape (Form), Trend (Direction), and Variation ( Strength) Based on a Scatter Plot

This Self-Learning Module (SLM) is prepared so that you, our dear learners, can continue your studies and learn while at home. Activities, questions, directions, exercises, and discussions are carefully stated for you to understand each lesson.

Each SLM is composed of different parts. Each part shall guide you step-by-step as you discover and understand the lesson prepared for you.

Pre-tests are provided to measure your prior knowledge on lessons in each SLM. This will tell you if you need to proceed on completing this module or if you need to ask your facilitator or your teacher’s assistance for better understanding of the lesson. At the end of each module, you need to answer the post-test to self-check your learning. Answer keys are provided for each activity and test. We trust that you will be honest in using these.

Please use this module with care. Do not put unnecessary marks on any part of this SLM. Use a separate sheet of paper in answering the exercises and tests. And read the instructions carefully before performing each task.

Using scatter plot, the relationship of the variables involved can be visualized. The scatter plot shows the shape, trend, and variation of the variables involved. In this ADM module, you will learn how to describe the relationship of variables of bi-variate data in using the scatter plot.

After going through this module, you are expected to:

1. describe the relationship of variables in terms of shape (form) of the scatter plot;

2. describe the relationship of variables in terms of trend (direction) of the scatter plot; and

3. describe the relationship of variables in terms of variation (strength of association) based on the scatter plot.

Are you ready now to study bi-variate data using your ADM module?

Good luck and may you find it helpful.

Statistics and Probability Quarter 4 Self-Learning Module: Describing the Shape (Form), Trend (Direction), and Variation ( Strength) Based on a Scatter Plot

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