Ms. Patricia Waldo, city manager of Big Cat, Texas (you met Ms. Waldo in Problem 18.22), is working on policy interventions designed to discourage loitering in her town. In her regression analysis, the number of individuals receiving either warnings or citations for loitering (LOIT) is the dependent variable. The first independent variable (examined in Problem 18.22) is a dummy variable coded 1 for days when classical music was played on the town’s outdoor speaker system and coded 0 for days when music was not played (MUSIC). Big Cat has installed a speaker system on a few main streets downtown.

Ms. Waldo wants to find other factors that can explain loitering activity. Because she believes that rain decreases loitering activity, she creates a new  dummy variable coded 1 for days when it rained and coded 0 for all other days (RAIN). Ms. Waldo also includes an independent variable for the daily high temperature (TEMP), because she believes that loitering activity decreases on extremely hot days (because people are more likely to stay indoors when it is uncomfortably hot). After running the revised regression, what conclusions can Ms. Waldo draw?