Analyzing Scatter Plots Concepts

Mr. Holcomb introduces scatter plots, explaining their use in identifying trends in bivariate data. He guides students through analyzing relationships in scatter plots, distinguishing between linear and non-linear, positive and negative relationships. The lesson includes examples and exercises to practice these concepts. Advanced analysis covers real data, focusing on clusters and outliers. The lesson concludes with a summary of key points.

What is the first question you should ask when analyzing a scatter plot?

• Is there a relationship between the variables?

• What is the title of the plot?

• How many data points are there?

• What is the color of the plot?

In scatter plot one, what type of relationship is identified?

• Positive linear

• Exponential

• Negative linear

• No relationship

Which scatter plot shows a positive linear relationship?

• Scatter plot one

• Scatter plot two

• Scatter plot three

• Scatter plot four

What is the nature of the relationship in scatter plot three?

• Positive linear

• Curved

• Negative linear

• No relationship

In the scatter plot of car weight and fuel efficiency, what type of relationship is observed?

• Positive linear

• Negative linear

• No relationship

• Curved

What is an outlier in a scatter plot?

• A point that fits the general pattern

• A point that is far from other points

• A point that is at the center of the plot

• A point that is the same as others

What are clusters in a scatter plot?

• Distinct clouds of points

• Lines of points

• Single points

• Randomly scattered points

In the elk weight versus chest girth scatter plot, what is identified as an outlier?

• A point at the maximum value

• A point at the average

• A point far from other points

• A point within a cluster

What might cause clusters in the elk weight versus chest girth scatter plot?

• Measurement errors

• Different age groups

• Random chance

• Weather conditions

How can clusters in a scatter plot be useful?

• They help understand data patterns

• They indicate the plot is incorrect

• They are irrelevant

• They show errors in data