SAMPLE Statistics

Statistics Chapter 3: Regression and Correlation

Regression and correlation analysis are used to determine the relationship between two quantitative variables. This section will begin with a description of common terms followed by an in-depth explanation of each concept, regression and correlation. As we progress, do not forget to refer back to Chapters 1 and 2. Learning Objectives

After reading Chapter 3 and completing the workbook, you should be able to: ● Know the two types variables.

● Know how to calculate slope, intercept and regression. ● Know how to interpret graphs and determine correlations. ● Know the definitions of regression and correlation ● Apply the basic statistical concepts and understanding of data to draw conclusions and interpretations. A clear understanding of the basic statistical terms and concepts presented in Chapter 3 will help prepare you to advance in this course and learn more complex statistical calculations and specific statistical tests. You should refer back to Chapters 1 and 2 and understand all concepts presented thus far. As you study, you should pay particular attention to the definitions and you should have an understanding of how a graph is laid out. You should be able to locate the x-axis and y-axis and know which represents the dependent and independent variable. You should pay particular attention to the equations and calculations for regression analysis. 3.1 Basic Terms ● Variable: A mathematical function that may change with time. It is the item or set of items being investigated and compared in the data set. Examples of variable include: effect, time, days, scores, weights, and grades. ● Independent variable: This is a variable that stands alone and is not subject to change. Example of independent variable would be time and gender. ● Dependent variable: This variable is dependent upon the independent variable. The dependent variable most often changes in response to the independent variable. Examples of a dependent variable include exam scores or weight. Both may be subject to change based on an independent variable such as time or gender. ● Normal distribution: The distribution of several random variables, it is most often seen as a symmetrical bell-shaped graph; recall the histogram described in Chapter 2. ● Random variable: As the name suggests, it is a randomly assigned quantity that has a numerical value for each member of a group. Its value has an equal number of opportunities Study Clues

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