Model 1: Rate = Age Model 2: Rate = Age Education Expend_60 U2 INEQUAL South LFPR MALE_F TOT_POP NW_POP U1 Income Expend_59 Model 3: Rate = Age Education Expend_60 U2 INEQUAL South LFPR MALE_F TOT_POP NW_POP U1 Income (Same as Model 2 but drop Expend_59) Model 4: Rate = Age Education Expend_60 U2 INEQUAL South LFPR MALE_F TOT_POP NW_POP U1 3. Estimate Model 1 using Excels regression tool: a. What is the correlation coefficient? (Hint: need to check the sign on the coefficient.) b. What does the R-square tell us? c. Calculate the R-Square using the ANOVA Table. Explain this result. d. What is the hypothesis that is being tested using the t-Stat? 4. Estimate Model 2 using Excels regression tool: a. Which variables are significant at the 5% level? b. What does the F-test tell us? c. What are the coefficient estimates for Expend_59 and Expend_60? Interpret those. d. Do the signs make sense? 5. Estimate Model 3. Compare the estimate on Expend_60 to the result from Model 2. What might this mean? 6. Estimate Model 4: What has happened to the sign of LFPR relative to the results from Model 2 and 3? Interpret.

IST 16 19.1 163.5 I 0 LSS SES 696 2 3 4 3 I 0 INCOME Esed 394 95 315 673 141 101 115 620 472 109 421 SLS 699 9 1959 123.4 65.2 963 135.5 83.6 70.5 167.4 94.9 S 6 696 06 9 10 905 OLT 996 657 565 Its ! 0 9+ 11 12 13 14 19 16 17 18 19 VST 66.4 19.8 946 33.9 929 55 15 0 0 0 68 96 22 ARE Education Espend 602 INEQUAL South LFPR MALET TOT POP W_707 55 261 1 510 143 113 103 124 383 1012 99 43 230 1 185 101 149 197 904 141 109 20174 191 995 30 121 110 115 126 0 949 964 44 510 111 38 168 1 992 131 109 115 35 206 1 157 23 239 1 383 955 * 140 115 71 126 632 1029 124 103 121 550 134 105 73 172 94 123 113 206 0 10 117 190 $95 996 ST 264 1 30 996 . 142 247 1 491 996 143 110 166 0 537 917 133 104 110 169 1 937 978 130 116 11 139 $36 934 123 103 113 166 567 126 109 14 157 41 1 132 97 116 196 130 116 196 954 13: 121 152 0 431 1071 139 199 999 194 112 215 O 371 2015 119 107 154 936 166 99 201 1 921 913 140 139 1043 200 123 109 165 O 156 147 104 203 1 120 115 166 34 123 101 50159 150 100 153 0 331 97 99 $35 46 31 104 999 23 149 291 1 315 36 149 1 560 96 145 141 162 1 136 139 1 126 0 130 1223 742 43.9 121.6 96.8 923 1993 542 121.6 1043 0 0 0 26 0 29 29 30 31 16 33 34 373 194 1072 923 633 0 0 91 . fit 1 O 99 36 31 39 39 40 41 $3.1 966 $26 113.1 19 *5*2*8888888 95 SS 0 0 I 342 $23 103 0 41 44 45 40 47 9 54.9 16 Seych Table 2. Correlatios Coefficient Matrix AT Education Espand 1.0000-00093 0.3225 0.6576 ADS 1.0000 -0.3502 Education 1.0000 0.4830 Expand 1.0000 NEQUAL South LITR MALES TOT TOP WW_TOP INEQUAL LIPR MALET TOT DOP NW POP LI 0.1973 -0.1790 -0.0905 0.1859 0.2139 0.33 79 0.0326 -0.0303 -0.2445 0.6392 0.3544 -0.1609 -0.0257 -0.2506 0.5932 -0.2244 -0.2151 -0.7657 -0.7027 0.5612 0.4369 -0.0172 -0.6549 0.0151 0.1531 -0.6305 -0.3725 01213 0.0338 0.5263 -0.2137 -0.0437 1.0000 0.0159 0.0717 -0.4205 -0.0157 0.2704 0.0509 0.7459 10000 0.7572 -0.2699 -0.1671 -0.1263 0.6773 -0.0638 1.0000 -0.5053 -0.3147 -0.0499 0.7671 -0.1724 1.0000 0.5136 -0.1237 -0.3412 -0.2294 1.0000 -0.4106 -0.3273 0.3519 1.0000 0.0952 -0.0381 1.0000 -0.1365 1.0000 INCONE 0.4413 -0.6701 0.7360 0.7872 0.0921 -0.8540 -0.6369 0.2946 0.1996 0.3053 -0.5901 0.0449 1.0000 Espend 10 0.6667 -0.3132 0.4994 0.9936 0.1692 -0.6482 -0.3762 0.1063 0.0228 0.3138 -0.2185 -0.0317 0.7943 1.0000 INCOME IST 16 19.1 163.5 I 0 LSS SES 696 2 3 4 3 I 0 INCOME Esed 394 95 315 673 141 101 115 620 472 109 421 SLS 699 9 1959 123.4 65.2 963 135.5 83.6 70.5 167.4 94.9 S 6 696 06 9 10 905 OLT 996 657 565 Its ! 0 9+ 11 12 13 14 19 16 17 18 19 VST 66.4 19.8 946 33.9 929 55 15 0 0 0 68 96 22 ARE Education Espend 602 INEQUAL South LFPR MALET TOT POP W_707 55 261 1 510 143 113 103 124 383 1012 99 43 230 1 185 101 149 197 904 141 109 20174 191 995 30 121 110 115 126 0 949 964 44 510 111 38 168 1 992 131 109 115 35 206 1 157 23 239 1 383 955 * 140 115 71 126 632 1029 124 103 121 550 134 105 73 172 94 123 113 206 0 10 117 190 $95 996 ST 264 1 30 996 . 142 247 1 491 996 143 110 166 0 537 917 133 104 110 169 1 937 978 130 116 11 139 $36 934 123 103 113 166 567 126 109 14 157 41 1 132 97 116 196 130 116 196 954 13: 121 152 0 431 1071 139 199 999 194 112 215 O 371 2015 119 107 154 936 166 99 201 1 921 913 140 139 1043 200 123 109 165 O 156 147 104 203 1 120 115 166 34 123 101 50159 150 100 153 0 331 97 99 $35 46 31 104 999 23 149 291 1 315 36 149 1 560 96 145 141 162 1 136 139 1 126 0 130 1223 742 43.9 121.6 96.8 923 1993 542 121.6 1043 0 0 0 26 0 29 29 30 31 16 33 34 373 194 1072 923 633 0 0 91 . fit 1 O 99 36 31 39 39 40 41 $3.1 966 $26 113.1 19 *5*2*8888888 95 SS 0 0 I 342 $23 103 0 41 44 45 40 47 9 54.9 16 Seych Table 2. Correlatios Coefficient Matrix AT Education Espand 1.0000-00093 0.3225 0.6576 ADS 1.0000 -0.3502 Education 1.0000 0.4830 Expand 1.0000 NEQUAL South LITR MALES TOT TOP WW_TOP INEQUAL LIPR MALET TOT DOP NW POP LI 0.1973 -0.1790 -0.0905 0.1859 0.2139 0.33 79 0.0326 -0.0303 -0.2445 0.6392 0.3544 -0.1609 -0.0257 -0.2506 0.5932 -0.2244 -0.2151 -0.7657 -0.7027 0.5612 0.4369 -0.0172 -0.6549 0.0151 0.1531 -0.6305 -0.3725 01213 0.0338 0.5263 -0.2137 -0.0437 1.0000 0.0159 0.0717 -0.4205 -0.0157 0.2704 0.0509 0.7459 10000 0.7572 -0.2699 -0.1671 -0.1263 0.6773 -0.0638 1.0000 -0.5053 -0.3147 -0.0499 0.7671 -0.1724 1.0000 0.5136 -0.1237 -0.3412 -0.2294 1.0000 -0.4106 -0.3273 0.3519 1.0000 0.0952 -0.0381 1.0000 -0.1365 1.0000 INCONE 0.4413 -0.6701 0.7360 0.7872 0.0921 -0.8540 -0.6369 0.2946 0.1996 0.3053 -0.5901 0.0449 1.0000 Espend 10 0.6667 -0.3132 0.4994 0.9936 0.1692 -0.6482 -0.3762 0.1063 0.0228 0.3138 -0.2185 -0.0317 0.7943 1.0000 INCOME