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.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:

Q_i= b₀+ b₁P_i+ b₂P_xi+ b₃Ad_i+ b₄I_i+ u_it.

Where:Q is the number of annual patients serviced,

P is the average price charged per patient visit (patient amount, in dollars),

P_xis 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,

u_iis 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)?

The variables included have economic meaning. Higher the average price per patient charged, lesser should be the quantity of services demanded. Higher the price charged by competitors, more will be the quantity demanded at a specific clinic. Higher the local advertising budget, higher will be the quantity demanded and higher the average income per household, higher will be the quantity of services demanded.

B.Using the estimates from the regression analysis, compute the expected (average) unit 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 variablesP_x, AdandIare held constant in the development of the demand curve).Q= f(P | P_x, Ad, I)

D.Develop the null and alternative hypothesis for the b₁coefficient (average price per patient - one-tail test), the b₂coefficient (average price charged by competition - one tail test) and the b₃coefficient (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.

Preview File Edit View Go Tools Window Help M Q 8 Sat Jun 19 7:59 PM AutoSave OFF v ... Urgent Healthcare Clinic - Regression Results.xIs - Compatibility Mode + Home Insert Draw Page Layout Formulas Data Review View ? Tell me Share Comments SUMMARY of the O.. ab, Arial 10 AA General Conditional Formatting Insert v Format as Table X Delete v Paste B IUV A $ ~ % " Editing Analyze Sensitivity X Cell Styles v Format v Data 122 X V fx B C D E K L M N O P Q R SUMMARY of the OUTPUT for the URGENT HEALTHCARE CLINIC Regression Statistics Multiple R 0.948226547 R Square 0.899133584 Adjusted R Square 0.885684728 Standard Error 839.1882756 Observations 35 10 11 SS MS F Significance F uck 12 Regression 188329215.7 47082303.9 66.8557694 1.64884E-14 13 Residual 30 21127108.85 704236.962 14 Total 24 209456324.6 15 16 Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Lower 95.0% Upper 95.0% 17 Intercept 11188.81987 3812.381291 2.93486381 0.00634506 3402.898568 18974.74118 3402.8986 18974.74118 18 X Variable 1 - P -43.1383301 10.8664516 -3.96986355 5 0.00041479 -65.33058494 -20.9460753 -65.330585 -20.9460753 19 X Variable 2 - PX 6.851398997 8.494944815 0.8065266 0.42628708 -10.49759282 24.20039081 -10.497593 24.2003908 20 X Variable 3 - Ad 0.022803572 .006907087 3.30147451 0.00248972 0.008697418 0.036909726 0.0086974 0.036909726 21 X Variable 4 - 1 0. 106554043 0.051012565 2.08878036 0.0453132 0.002372487 0.210735599 0.0023725 0.210735599 22 23 24 25 26 27 28 29 30 34 38 D Results Table 1 Table 2 + Select destination and press ENTER or choose Paste Average: 8486541.865 Count: 84 Sum: 466759802.6 100% 2. Select the best subject Related Answer JUN 19 4 W