Using Dummy Variable Regression in Excel To Perform Conjoint Analysis
Step-By-Step Video Showing How To Perform Conjoint
Analysis Using Dummy Variable Regression in Excel
In Order To Find Out Which Product Attributes
Your Customers Value The Most
The 6 Steps of Using Dummy Variable Regression to Perform Conjoint Analysis
Step 1) List All Product Attributes
For 1 Product
Step 2) Make a List of All Possible Combinations of Those
Attributes
Step 3) Have Consumer Rate Each Attribute Combination
Step 4) Prepare Completed Survey for Regression
Dummy Variables to Be Removed From Input Data To
Prevent Collinearity
Step 5) Run Regression in Excel
Step 6) Derive Attribute Utilities From Regression Output
An Example of Using a Dummy Variable
The Problem of Collinearity - and How To Solve It
The Product Utilities - The Measure of Customer Liking
How To Quickly Read the Output of Regression in Excel
Step-By-Step Video About How To Quickly Read and
Understand the Output of Excel Regression
The 4 Most Important Parts of Regression Output
1) Overall Regression’s Accuracy
R Square
Adjusted R Square
2) Probability That This Output Was Not By Chance
Significance of F
3) Individual Regression Coefficient Accuracy
P-value of each coefficient and the Y-intercept
4) Visual Analysis of Residuals
Charting the Residuals
The Residual Chart
Logistic Regression Analysis in Excel
Customer Quality Scores Are Created With
Logistic Regression
Step-By-Step Video Showing How To Predict if a Prospect
Will Buy Using Logistic Regression in Excel:
What is Logistic Regression?
An Example of Logistic Regression In Action
Create the Predictive Equation
The Logit
Calculating the Logit Variables - A, B, and Constant
Optimizing the Logit Variables in the Excel Solver
The Final, Most Accurate Predictive Equation
You'll Have To Tweek the Constraints in the Excel Solver
The Four Steps of Regression in Excel (Including 2 Crucial Ones Always Skipped)
Step-By-Step Video Showing How To Do All 4
Steps of Regression in Excel, Including the 2 Crucial Initial Steps That No One
Does
Crucial Step 1) Graphing the Data
Crucial Step 2) Running Correlation Analysis on All Variables
Simultaneously
Remove Input Variables That Have Low Correlation
With Output Variable
Remove Inputs Variables Highly Correlated With
Other Input Variables
Adding New Input Variables To The Regression
Analysi
Step 3) – Run the Regression in Excel
Step 4) Analysis of Excel Output
How To Do Nonlinear Regression Using the Excel Solver
The Solver dialogue box has the following 4
parameters that need to be set:
Objective:
Decision Variables:
Constraints:
Selection of Solving Method: GRG Nonlinear
Solver Tips
Initial Solver Settings:
Show Iteration Results:
Use Automatic Scaling:
Assume Non-Negative:
Bypass Solver Reports:
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