A retailer aims to make the optimal decision on the retail price to maximize the revenue in the next season. The retailer knows that the retail price will have a significant impact on the demand. To figure out the relationship between the retail price and the demand, the retailer collects the data of the retail price and the corresponding demand in the previous seasons and conducts a regression analysis. The following is the output from excel regression analysis.

SUMMARY OUTPUT
Regression Statistics
Multiple R	0.96920785
R Square	0.939363856
Adjusted R Square	0.938100603
Standard Error	48.3025667
Observations	50
ANOVA
	df	SS	MS	F
Regression	1	1734937.878	1734938	743.6070716
Residual	48	111990.6216	2333.138
Total	49	1846928.5
	Coefficients	Standard Error	t Stat	P-value
Intercept	101.43	23.98385938	-8.64879	2.34522E-11
Price	-1.2	0.05873312	27.26916	7.20509E-31

1. What can you say about the above regression model: is it a good model or not, and why can you say so? Describe how you should use the regression model to predict the demand.
2. Suppose the retailer chooses the retail price that is equal to $40. If the retailer also wants to know the risk in his revenue (for example, the standard deviation of the revenue), please describe conceptually how you can do the risk analysis (you need to clearly describe the steps of the analysis and provide the excel functions in the key steps).