Question 2 Draw a scatterplot of U5MR and FemaleYouthLR , making sure you use an

Question

Question 2

Draw a scatterplot of U5MR and FemaleYouthLR, making sure you use an appropriate title for the plot and label the x-axis and the y-axis. Draw another scatterplot for U5MR and Income.

Question 3

Now estimate two regression models:

(1)Model A: First with U5MR as the dependent variable and Income as the independent variable, and

(2)Model B: Second with U5MR as the dependent variable and FemaleYouthLR as the independent variable Question 4

Interpret the slope estimates in Model A and Model B. That is, interpret the impact of Income on U5MR, and the impact of FemaleYouthLR on U5MR

Queston 5

Interpret the 2 from Model A and Model B, respectively.

Question 6

Is Model A or Model B doing a better job of predicting U5MR? Why do you conclude as you do?

Question 7

Calculate predicted values of U5MR for the following:

(1)Using your results for Model A, the predicted value of U5MR when Income = $10,000

(2)Using your results for Model B, the predicted value of U5MR when FemaleYouthLR = 80

Question 8

Draw the following plots, making sure to use an appropriate title and to label the x-axis and the y-axis: (1) Using Model A, a scatterplot of U5MR versus Income with the estimated regression line (2) Using Model B, a scatterplot of U5MR versus FemaleYouthLR with the estimated regression line

U5MR 17 20 164 16 35 10 41 5 90 45 41 7 53 14 12 102 104 22 40 95 150 9 14 18 78 10 108 5 3 146 27 23 21 16 100 52 4 68 62 73 20 72 5 32 101 129 35 76 23 6 56 31 34 4 17 19 19 73 11 27 72 9 9 100 75 5 58 71 9 11 128 7 84 15 16 28 6 31 90 39 42 24 114 124 12 86 19 63 22 18 30 5 4 7 18 12 10 55 53 9 60 7 13 182 3 3 45 5 10 21 80 15 58 13 7 57 96 13 21 16 14 53 69 11 8 54 7 40 18 15 23 60 89 FemaleYouthLR 98.8562401 89.1382395 66.05586 99.82573 99.9457877 97.5823393 80.41 99.8473948 30.7865523 67.964261 99.0843005 99.70686 96.9742 98.3187371 97.6510618 33.1253249 88.1134538 99.25232 85.8686757 76.4209334 42.18648 98.8900182 99.5930387 98.7243399 85.93516 98.71574 62.74958 99.67076 99.8383116 53.2502202 98.0536431 98.8053167 86.0515899 96.385741 98.40987 87.72 99.84824 47.0411271 96.96772 63.62212 99.85464 83.2345286 99.28285 85.55317 21.7967324 67.08296 93.6702236 70.4807777 96.9261127 99.03245 74.3557316 98.7517952 80.53792 99.91647 98.47963 99.2532228 99.8651051 81.6325718 98.7393674 99.7995877 78.7398425 99.73778 99.0822982 92.0938948 37.1703075 99.7795 63.9670836 69.9817354 98.4582627 99.3606486 38.8110704 99.1107347 66.18019 97.82863 98.5066648 97.25485 99.2996 74.0342903 56.540052 90.615427 77.4686582 88.8424817 23.1974298 57.9538903 98.1650738 61.4626556 97.3368677 74.8106 98.7288192 96.7052348 98.4915213 99.99996 99.77894 98.2611518 100 97.35514 99.7580229 77.9724632 77.3372791 97.00867 56.1914351 99.24942 99.3743779 52.0556 99.7666672 99.89812 99.1623662 99.686359 98.5889721 98.8021543 95.30996 94.10913 99.88021 97.8789299 98.50427 78.5684358 72.7081203 99.5550376 99.57594 96.0935599 97.9362991 99.89059 85.471464 99.81509 97.0017404 72.7709988 99.2254067 99.99011 94.78533 98.8049947 96.65253 76.02248 58.4775491 Income 4090 4110 4580 3720 6050 16050 840 6530 750 2420 2220 4650 7720 11630 6870 670 240 3810 880 1170 740 14280 5740 6990 840 8740 1220 13290 26000 220 5470 5190 3000 3580 13560 450 15830 410 10070 510 3280 1550 23260 3120 460 550 3410 760 2070 12390 1530 3420 5870 33840 5140 4720 9730 840 44730 990 1260 14180 9190 1380 370 13850 430 320 9800 5750 660 19760 1110 8570 9740 3160 6940 2940 510 5670 700 1650 370 1430 19120 1260 9910 1790 3290 5880 2470 12670 20580 78720 2070 8420 12700 560 1320 18030 1040 5280 11640 580 47210 22710 7610 30110 2920 8480 2860 2610 860 5210 4690 3670 500 4240 14400 4150 10830 5550 440 3500 36040 570 13510 1720 3080 12470 1400 1110 1350

Explanation / Answer

3)

Model A

y^ = 53.2358 - 0.001631 *Income

Model B

y^ = 183.42643 -1.63655 *FemaleYouthR

4)

slope estimate in income is -0.0016

that is we increase income by 1 unit , the U5MR will decrease by -0.00163 unit

similarly if we increase 1 FemaleYouthR , then U5MR will decrease by -1.63655 unit

5)

R^2 for model A is 0.19456

hence 19.456 % of variability in UMR is explained by MODEL A

whereas

R^2 for Model B is 0.616195

hence 61,62 % of variability in UMR is explained by MODEL B

6) clearly Model B is doing better job as R^2 for model B is higher

7)

(1)Using your results for Model A, the predicted value of U5MR when Income = $10,000

y^ = 53.2358 - 0.001631 *10000 = 36.9258

(2)Using your results for Model B, the predicted value of U5MR when FemaleYouthLR = 80

y^ = 183.42643 -1.63655 *80 = 52.50243

Make plot yourself using Insert Recommended charts in excel

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SUMMARY OUTPUT Regression Statistics Multiple R 0.441098395 R Square 0.194567794 Adjusted R Square 0.188419457 Standard Error 35.08684368 Observations 133 ANOVA df SS MS F Significance F Regression 1 38958.46749 38958.47 31.64559 1.07E-07 Residual 131 161272.3445 1231.087 Total 132 200230.812 Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Lower 95.0% Intercept 53.23582312 3.714480479 14.33197 1.32E-28 45.88769 60.58395 45.88769 Income -0.001630796 0.000289897 -5.62544 1.07E-07 -0.0022 -0.00106 -0.0022

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