Question
You have purchased a laundry and dry-cleaning shop, and are interested in forecasting demand for your services. After some analysis using past data, you generate
You have purchased a laundry and dry-cleaning shop, and are interested in forecasting demand for your services. After some analysis using past data, you generate the following, where sales (Q) is the dependent variable:
Regression Statistics | |||
Multiple R | 0.945298 | ||
R Square | 0.893588 | ||
Adjusted R Square | 0.88131 | ||
Standard Error | 19.92731 | ||
Observations | 50 | ||
ANOVA | |||
Df | SS | ||
Regression | 4 | 86699.63 | |
Residual | 45 | 10324.54 | |
Total | 49 | 97024.17 | |
Coefficients | Standard Error | ||
Intercept | 5.55 | 0.57 | |
P | -10.69 | 3.15 | |
Y | 0.92 | 0.39 | |
Pc | 5.25 | 2.08 |
where Q=number of shirts laundered per week in hundreds, P=price in dollars per laundered shirt, Y=per capita income (in thousands of dollars), and Pc = price charged by another launderer two blocks away.
- Interpret the price coefficient. Show the original form of the equation implied by the regression. Predict sales when P=$2, Y=40, and PC=$1.50.
- Suppose you raise the price by $1. Predict the change in sales. Will total revenue rise or fall? Calculate.
- At the 95% confidence level, are the variables significant? Use a critical t-stat of 2.0, and calculate your t-statistics.
Step by Step Solution
There are 3 Steps involved in it
Step: 1
Get Instant Access to Expert-Tailored Solutions
See step-by-step solutions with expert insights and AI powered tools for academic success
Step: 2
Step: 3
Ace Your Homework with AI
Get the answers you need in no time with our AI-driven, step-by-step assistance
Get Started