Answer Happy

4. For this last question, you will create an interaction"variable using the Indicator variable for Southem. The details of how these work are in the class notes on using categorical variables in regression Use Jamovito estimate the model: Robbery_Rate + Pincome Southern + BCIncome Southern b. Enter the estimated coalicients and corresponding p-values for each term in this model Intercept: -54310 Income 0028 Southern D25555 Income Southern X Xp-value007 XD-valo 054 p-value X what the predicted change in robbery rate for another states income is by What is the predicted change in robbery for the state when median income increase ty sorted to the police 100.000 What is the predicted robbery rate for another state with minime (no note that prices and were much lowerin 1900 there today base uported to the police 100,000 We hadded to the other with a manica 50000 2. Supervetustate wie median in.com 33000 robbery atos reported to the poc per 100.000 pasta prothes fones reported the 100.000 ton

c. Robbery_Rate; = Bo + B, Southern, + B2Expend1960 + $; bo= 1.195 ba= 15.592 ✓ p-value= 0.100 b2= 0.985 p-value= 0.001 R2=0.501

103 12.1 986 113 Robbery_Rate Southem Expend 1960 Expend 1959 MalesPerFemales State_Population income Years Schooling 78.8 1 58 56 950 33 3940 9.1 1632 이 95 1012 13 5570 11.3 168.4, 1 45 44 969 18 3180 8.9 196.8 이 149 141 994 157 6730 123 이 109 101 985 18 5780 12.1 66.4 이 118 115 964 25 6890 11 96.1 1 82 79 982 4 6200 11.1 155.2 1 115 109 969 50 4720 10.9 86.2 1 65 62 955 39 4210 9 69 어 71 68 1029 7 5260 11.8 165.2 이 121 116 966 101 6570 10.5 87.3 0 1755 71 972 47 5800 10.8 51.3 이 67 60 972 28 5070 11.3 67.9 ol 62 61 22 5290 11.7 81.1 1 57 53 986 30 4050 8.7 94.5 11 81 77 956 33 4270 86 52.5 이 166 63 977 10 4870 11 91.7 1 123 115 978 31 6310 10.4 74.4 이 128 128 934 51 6270 11.6 124.2 이 105 985 78 6260 10.8 73.9 이 74 67 984 345570 10.8 42.7 1 47 44 962 22 2880 8.9 118.3 0 87 83 953 43 5130 9.6 97.3 이 78 73 1038 7 5400 11.6 52.3 이 63 57 984 14 4860 11.6 199.6 이 160 143 1071 3 6740 12.1 33.8 이 69 71 965 6 5640 10.9 121.8 0 82 76 1018 10 5370 11.2 104.5 이 166 157 938 168 6370 10.7 168.4 1 58 54 973 46 3960 8.9 37 이 55 54 1045 4530 9.3 75.9 이 SO 81 964 97 6170 10.9 107.5 1 63 64 972 23 4620 10.4 92.7 97 97 990 18 5890 11.8 65.5 이 97 87 948 113 5720 10.2 126.8 ol 109 98 964 95590 10 83.6 1 58 56 974 243820 8.7 57.1 이 61 47 1024 7 4250 10.4 81.2 1 61 54 953 36 3950 8.8 114,7 1 82 74 981 96 4880 10.4 86.4 0 72 66 998 9 5900 122 53.1 이 56 54 968 4 4890 10.9 1 75 70 996 40 4960 9.9 104 0 95 96 1012 29 6220 12.1 45.6 1 46 41 968 19 4570 8.8 49.2 이 106 97 989 40 5930 10.4 83.1 이 91 1049 3 5880 12.1 이이이 82.4

Construct approximate 95% confidence intervals for the estimated slope coefficients and in this model (at), using the usual approximate vertical Slope coefficient for Southern Margin of errot ct lower bound 20545 xet upper bound Stope coefficient for Expend 1960 Margin of error at lower bound 0684 Cupper bound - 1206 IX