Urgent Healthcare Clinic problem set
This assignment is intended to help you learn to do the following:
Review basic statistical concepts and econometric procedures and develop and analyze an estimated regression equation for demand.
Develop null and alternative hypotheses for each estimated coefficient of the demand equation.
Develop a t-test to test the statistical significance of an estimated parameter and determine the acceptance or rejection of the null and alternative hypotheses.
Develop an F-test to test the statistical significance of the estimated demand equation.
Interpret the results of the regression estimates.
Review this module’s readings and media.
Compose a document in which you:
.You will need the Urgent Healthcare Clinic Results spreadsheetDownload Urgent Healthcare Clinic Results spreadsheet.
Show all the steps used to arrive at your answer for each of your answers
Demand Estimation for the Urgent Healthcare Clinic
Consider the hypothetical example of the Urgent Healthcare Clinic (UHC), a chain of urgent care
facilities in 35 regional areas across the U.S. Management of the Urgent Healthcare Clinics has
initiated an empirical estimation of customer traffic at their 35 regional locations to help the
clinics formulate updates to patient pricing and possible expansion plans for the coming year. a network of urgent care centers in 35 regional areas across the United States The Urgent Healthcare Clinics’ management has begun an empirical estimation of customer traffic at their 35 regional locations to assist the clinics in formulating updates to patient pricing and potential expansion plans for the coming year.
The attached spreadsheet contains annual operating data for 35 regions (Table 1). Regression
Annual operating data for 35 regions appear in the attached spreadsheet (Table 1). Regression
results also in the spreadsheet (Results/Table 2).
The following regression equation was fit to these data:
Where: Q is the number of annual patients serviced,
Px is the average price per patient charged by competing facilities (in $)
Ad is the local advertising budget for facilities in each region (in $),
I is the average income per household in each region’s service area,
ui is a residual (or disturbance) term.
The subscript indicates each of the 35 regional markets (i = 1,…, 35) from which the observation
was taken. Least squares estimation of the regression equation on the basis of the 35 data cross-
sectional observations resulted in the estimated regression coefficients and other statistics as
shown in the results and in Table 2.
A. Describe the economic meaning for the individual independent variables included in the
Urgent Healthcare Clinic demand equation. Interpret each estimated coefficient and its
impact on the dependent variable (number of patients serviced)?
sales and average sales revenue for a typical region? (Assume that all independent
variables are statistically significant in your computations).
C. From the regression estimates develop a demand equation for Urgent Healthcare Clinic.
Use each coefficient average (at the bottom on Table 1) for the non-price variables to
develop the demand equation. (Again, assume that all independent variables are
statistically significant in your demand equation computation and the variables Px, Ad and
I are held constant in the development of the demand curve). Q = f(P | Px, Ad, I)
D. Develop the null and alternative hypothesis for the b1 coefficient (average price per
patient – one-tail test), the b2 coefficient (average price charged by competition – one tail
test) and the b3 coefficient (advertising variable – two tail test). Briefly describe when it
is appropriate to use a one-tail test relative compared to a two-tailed t-test? Use a t-test to
determine the level statistical significance for each individual independent variables at
the 95 and 99 percent confidence levels.
E. Briefly explain the terminology of the coefficient of determination (R2). If one of the
independent variables was found to not be statistically significant, what changes might
you perform to the original regression equation?
F. Develop the null and alternative hypothesis and conduct an F-test for the complete set of
coefficients in the equation to determine the significance at the 95 and 99 percent levels.