Regression Analysis (15 hours)

Description

Regression analysis provides a quantitative relationship between two or more variables that can be used for making predictions. This course covers the basic elements of performing and interpreting regressions. Students will learn how to choose the right sort of regression for different data sets, how to derive a regression from raw data, how to check to see if the regression accurately reflects the relationship in the data, and how to make statistically relevant predictions from the regression. Managers and employees responsible for the interpretation of information used in process improvement efforts will benefit most from this course.

The course length is 15 hours and will include detailed instruction, periodic assessments, and a certificate of completion upon successful review.

Lesson 1: Linear Regression

• List the steps you take in performing a linear regression
• Define the goal of linear regression
• Distinguish between the independent variable and the dependent variable in a regression
• Interpret a scatter diagram
• Define the linear regression formula
• Define the “line of best fit”
• Use the Method of Least Squares to find the “line of best fit”

• Calculate and interpret the correlation coefficient
• Calculate and interpret the coefficient of determination
• Calculate and interpret the standard error of estimate
• Calculate and interpret the significance of slope
• Plot and examine residuals, and make decisions about the validity of the model based upon them

• Calculate and interpret the confidence interval for the individual value
• Calculate and interpret the confidence interval for the mean value
• Graphically display confidence intervals and predict the value of the dependent variable to a given degree of confidence

• Describe two factors to consider when deciding whether to use linear or polynomial regression
• Use the polynomial regression model to predict a dependent variable
• Test the significance of the polynomial regression model
• Calculate and interpret the correlation coefficient and the coefficient of determination for a polynomial regression
• Test for the significance of slope for a polynomial regression
• Calculate and interpret the standard error of estimate for a polynomial regression
• Check residuals to judge the accuracy of your model
• Construct confidence intervals around a polynomial regression model to estimate the precision of an estimated value

• Identify when the use of multiple regression is appropriate
• Use the multiple regression model to predict a dependent variable
• Test the significance of the multiple regression model
• Calculate and interpret the correlation coefficient and the coefficient of determination for a multiple regression
• Test for the significance of slope, and calculate the standard error of estimate for a multiple regression
• Check residuals to judge the accuracy of your model
• Construct confidence intervals around a multiple regression model
• Identify the various multiple regression techniques that enable you to choose which independent variables will be included in your model

Target Audience

• Operations Manager
• General Manager
• Plant Manager
• Person with bottom-line responsibility

UL University's workshops are designed for and intended to serve individuals using and relying upon UL services. UL University reserves the right to limit workshop attendance strictly to the foregoing. In addition, UL University reserves the right to change, reschedule or cancel any workshop at any time. UL shall not be responsible for any consequential or other losses resulting from the cancellation or postponement of this workshop such as airline and other travel/personal expenses.

UL University accepts the following forms of payment for registration: Visa, MasterCard, American Express and Invoice/PO. Please note that Invoice/PO is not accepted for online courses.