IDS - Investigating Data & Statistical Reasoning Overview

Investigating Data & Statistical Reasoning Overview

In this module, we will learn to solve practical, linear problems involving situations using bivariate quantitative data.  We will analyze graphs that represent relationships among two variables. This means we are determining if the graph is linear or non-linear, where is the graph increasing, or where is the graph decreasing. All the while, this information will help us make predictions from the original data we were given. Sometimes data doesn't fall in a nice straight line, but we can still use linear functions to help us find the line of best fit and use this line to interpret the slope and y intercept. 

Essential Questions

  1. How do we determine rate of change from a graph or from a table of values?
  2. How do we plot and interpret bivariate data?
  3. How can we use straight lines to model relationships between two quantitative variables?
  4. How can we represent the relationships between paired data and use that representation to make predictions?

Key Terms

The following key terms will help you understand the content in this module.

Model: A mathematical representation of a process, device, or concept by means of a number of variables.

Interpret: To establish or explain the meaning or significance of something.

Initial Value: y-intercept.

Linear: A relationship or function that can be represented by a straight line.

Non-linear: A relationship which does not create a straight line.

Slope: The measure of steepness of a line.

Rate of Change: The ratio of the change in the output value and change in the input value of a function.

Bivariate Data: Two different response variables that are from the same population.

Quantitative Variables: A variable whose values are numerical.

Scatter Plot: The graph of a collection of ordered pairs that allows an exploration of the relationship between the points.

Line of Best Fit: A straight line drawn through the center of a group of data points plotted on a scatter plot.

Outlier: An element of a data set that distinctly stands out from the rest of the data.

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