A manager wants to know about the relationship between SALES (measured in Millions of $) and SQFT (square footage of the store, measure per 1,000 square footage).She also knows if the store is located in a metropolitan or nonmetropolitan county (METRO 1= metropolitan, 0 otherwise).Metropolitan stores are more densely populated.She looks at a random set of 14 stores and this is the Correlation Matrix and summary statistics.

Multivariate Correlations SALES SOFT METRO SALES 1.0000 0.9509 0.2422 SOFT 0,8509 1,0000 -0.0217 METRO 0.2422 -0.0217 1.0000 The correlations are estimated by Row-wise method. Univariate Simple Statistics Column N DF Mean Std Dev Sum Minimum Maximum SALES 14 13.00 5.8429 2.9994 81.8000 2.7000 11.8000 SOFT 14 13.00 2.9214 1.7080 40.9000 1.1000 5.8000 METRO 14 13.00 0.5000 0.5189 7.0000 0.0000 1.0000 Note: Statistics were calculated for each column independently without regard for missing values in other columns.Summary of Fit RSquare 0.9733 RSquare Adj 0.9684 Root Mean Square Error 0.5329 Mean of Response 5.8429 Observations (or Sum Wgts) 14.0000 Analysis of Variance Sum of Source DF Squares Mean Square F Ratio Model 2.0000 113.8301 56.9151 200.3940 Error 11.0000 3.1242 0.2840 Prob F C. Total 13.0000 116.9543 0.0000 Parameter Estimates Term Estimate Std Error t Ratio Prob It| Intercept 0.1752 0.3257 0.5379 0.6014 SOFT 1.6799 0.0866 19.4071 0.0000 METRO 1.5200 0.2849 5.3346 0.0002