#
Demand Estimation and Forecasting
**Topics:**
The Walt Disney Company,
Walt Disney,
Walt Disney Parks and Resorts,
Regression analysis,
Walt Disney World Resort,
Mickey Mouse /
**Pages:** 11 (2594 words) /
**Published:** Sep 12th, 2013

**Topics:**The Walt Disney Company, Walt Disney, Walt Disney Parks and Resorts, Regression analysis, Walt Disney World Resort, Mickey Mouse /

**Pages:**11 (2594 words) /

**Published:**Sep 12th, 2013

Demand Curve Estimation

■ Simple Linear Demand Curves

■ The best estimation method balances marginal costs and marginal benefits.

■ Simple linear relations are useful for demand estimation.

■ Using Simple Linear Demand Curves

■ Straight-line relations give useful approximations.

Identification Problem

■ Changing Nature of Demand Relations

■ Demand relations are dynamic.

■ Interplay of Supply and Demand

■ Economic conditions affect demand and supply.

■ Shifts in Demand and Supply

■ Curve shifts can be estimated.

Simultaneous Relations

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Interview and Experimental Methods

■ Consumer Interviews

■ Interviews can solicit useful information when market data is scarce.

■ Interview opinions often differ from actual market transaction data.

■ Market Experiments

■ Controlled experiments can generate useful insight.

Experiments can become expensive

Regression Analysis

■ What Is a Statistical Relation?

■ A statistical relation exists when averages are related.

■ A deterministic relation is true by definition.

■ Specifying the Regression Model

■ Dependent variable Y is caused by X.

■ X variables are independently determined from Y.

■ Least Squares Method

■ Minimize sum of squared residuals.

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Measuring Regression Model Significance

■ Standard Error of the Estimate SEE) increases with scatter about the regression line.

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Goodness of Fit, r and R2

■ r = 1 means perfect correlation; r = 0 means no correlation.

■ R2 = 1 means perfect fit; R2 = 0 means no relation.

■ Corrected Coefficient of Determination, R2

Adjusts R2 downward for small samples

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F statistic

Tells if R2 is statistically significant

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Measures of Individual Variable Significance

t statistics

■ t statistics compare a sample