Decision Point: Using t-Statistics and P-Values Now that you've categorized the data, you need to assess the validity of each variable. You run two different regressions: one for the group of demand variables, where quantity is regressed, and one for the group of supply variables, where average cost is regressed. To help you determine statistical significance, you analyze the t-statistics (t-stats) and P-values in these reports. Rank the variables by category: Supply and Demand. Place the most statistically significant variables in order, starting with the strongest at the top (ignore the intercept term). Drag and drop the variables included in the tables to the Supply and Demand boxes. When you are finished, click the Submit button. Supply Regression Results: Dependent Variable = Average Cost Variable Coefficients Standard Error t-Stat P-Value Intercept -1.97389 1.848769 -1.06768 0.299777 Quantity sold -0.001 0.001361 -0.73792 0.470078 Rent 0.002956 0.001922 1.538356 0.141355 Electricity -0.00216 0.003477 -0.62023 0.542877 Average Wage 0.259641 0.026586 9.766108 0.0000000128 Cost of Vegetables 0.050973 0.110885 0.459691 0.651242 Cost of Grains 0.064819 0.064097 1.011265 0.325291 Non-Food supplies 0.002129 0.003681 0.578562 0.570054

Demand Regression Results: Dependent Variable = Quantity Sold Coefficients Standard Error | Stat Lower 95% Upper 95% Price of Salad -13.4255655 | 3.948617104 -3.400067705 0.0026976989 | -21.6371643 -5.21139667 Income 9.021179907 | 1.756005597 2.859432747 0.009387191 | 1.369366357 8.672993458 Population -0.258377901 | 0.356681274 -0.724394353 0.416817477 | -1.000137217 0.483381415 Advertising 0.060673699 | 0.039028271 1.954608951 0.134981577 | -0.020490033 0.141837431 Demand Advertising 1 Population KR WON ( Price | Income