Birth Rates and Characteristics of Nations (n = 153, k = 5)
Nation	BirthRate	LifeExp	InfMort	Density	GDPCap	Literate
Afghanistan	41.03	46.6	144.8	42.87	800	36
Albania	18.59	72.1	38.6	123.31	3800	93
Algeria	22.34	70.2	39.2	13.56	5600	62
Angola	46.18	38.9	191.7	8.50	1330	42
Argentina	18.23	75.5	17.2	13.82	12000	96
Armenia	12.00	66.6	41.1	111.75	3350	99
Australia	12.71	80.0	4.9	2.54	24000	100
Austria	9.58	78.0	4.4	97.43	27000	98
Azerbaijan	18.84	63.1	82.7	90.05	3100	97
Bangladesh	25.12	60.9	68.1	926.23	1750	56
Belarus	9.86	68.3	14.1	49.79	8200	98
Belgium	10.58	78.1	4.6	336.76	26100	98
Benin	43.66	49.7	88.5	60.27	1040	38
Bhutan	35.26	53.2	106.8	44.56	1200	42
Bolivia	26.41	64.4	57.5	7.69	2600	83
Bosnia/Herzeg.	12.76	72.0	23.5	77.54	1800	93
Botswana	28.04	35.9	64.7	2.65	7800	70
Brazil	18.08	63.6	35.9	20.68	7400	83
Bulgaria	8.05	71.5	14.2	68.72	6200	98
Burkina Faso	44.34	46.1	105.3	45.96	1040	36
Burundi	39.87	45.9	70.0	229.00	600	36
Cambodia	32.93	57.1	64.0	70.57	1500	35
Cameroon	35.66	54.4	68.8	34.04	1700	63
Canada	11.09	79.7	5.0	3.20	27700	97
Central Africa	36.60	43.6	103.8	5.85	1300	60
Chad	47.74	51.3	93.5	7.01	1030	40
Chile	16.46	76.1	9.1	20.48	10000	95
China	15.85	71.9	27.3	133.82	4300	82
China, Hong Kong	10.92	79.8	5.7	6688.03	25000	92
Colombia	21.99	70.9	23.2	36.01	6300	91
Congo, Republic of	37.91	47.7	97.9	8.65	900	75
Congo, Democratic Rep. of	45.55	49.1	98.1	23.55	590	77
Costa Rica	19.83	76.2	10.9	75.05	8500	96
Cote d'Ivoire	39.99	44.7	92.2	52.11	1550	49
Croatia	12.80	74.1	7.1	77.65	8300	97
Cuba	12.08	76.6	7.3	101.25	2300	96
Czech Republic	9.08	75.0	5.5	130.05	14400	100
Denmark	11.74	76.9	5.0	124.58	28000	100
Dom Republic	24.40	73.7	33.4	178.98	5800	82
Ecuador	25.47	71.6	33.0	47.42	3000	90
Egypt	24.41	64.1	58.6	70.61	3700	51
El Salvador	28.30	70.3	27.6	301.98	4600	72
Eritrea	42.25	56.6	73.6	36.81	740	25
Estonia	8.96	70.0	12.3	31.30	10000	100
Ethiopia	44.31	45.1	98.6	60.04	700	36
Finland	10.60	77.8	3.8	15.38	25800	100
France	11.94	79.1	4.4	109.26	25400	99
Gabon	27.24	49.1	93.5	4.61	5500	63
Gambia	41.25	54.0	76.4	128.84	1770	48
Georgia	11.48	64.7	51.8	71.18	3100	99
Germany	8.99	77.8	4.7	233.18	26200	99
Ghana	28.08	57.1	55.6	84.54	1980	65
Greece	9.82	78.7	6.3	80.68	17900	97
Guatemala	34.17	66.9	44.6	122.27	3700	64
Guinea	39.49	46.3	127.1	31.62	1970	36
Guinea-Bissau	38.95	52.2	108.5	37.25	900	34
Haiti	31.42	49.6	93.4	254.55	1700	45
Honduras	31.24	68.8	30.5	58.53	2600	74
Hungary	9.34	71.9	8.8	108.30	12000	99
India	23.79	63.2	61.5	318.12	2500	52
Indonesia	21.87	68.6	39.4	120.52	3000	84
Iran	17.54	70.3	28.1	40.43	6400	72
Iraq	34.20	67.4	51.7	54.92	2500	58
Ireland	14.62	77.2	5.4	55.25	27300	98
Israel	18.91	78.9	7.6	290.30	20000	95
Italy	8.93	79.3	5.8	191.60	24300	98
Jamaica	17.74	75.6	13.7	243.84	3700	85
Japan	10.03	80.9	3.8	336.06	27200	99
Jordan	24.58	77.7	19.6	57.50	4200	87
Kazakhstan	17.83	63.4	59.0	6.16	5900	98
Kenya	27.61	47.0	67.2	53.44	1000	78
Korea N	17.95	71.3	22.8	184.37	1000	99
Korea S	14.55	74.9	7.6	490.70	18000	98
Kuwait	21.84	79.5	10.9	118.49	15100	79
Kyrgyzstan	26.11	63.6	75.9	24.29	2800	97
Laos	37.39	53.9	91.0	24.40	1630	57
Latvia	8.27	69.0	15.0	36.64	7800	100
Lebanon	19.96	71.8	27.4	353.63	5200	86
Lesotho	30.72	47.0	82.6	72.74	2450	83
Liberia	45.95	21.8	130.2	29.52	1100	38
Libya	27.59	75.9	27.9	3.05	7600	76
Lithuania	10.22	69.4	14.3	55.23	7600	98
Macedonia	13.35	74.3	12.5	81.11	4400	89
Madagascar	42.41	55.7	81.9	28.06	870	80
Malawi	37.13	36.6	120.0	90.33	660	58
Malaysia	24.22	71.4	19.7	68.73	9000	84
Mali	48.37	47.4	119.6	9.15	840	38
Mauritiana	42.54	54.5	75.3	2.74	1800	41
Mauritius	16.34	71.5	16.7	588.34	10800	83
Mexico	22.36	72.0	24.5	52.42	9000	90
Moldova	13.82	64.7	42.2	131.03	2550	96
Mongolia	21.80	64.6	52.0	1.72	1770	98
Morocco	23.69	69.7	46.5	69.80	3700	44
Mozambique	36.41	35.5	138.6	24.46	900	42
Myanmar (Burma)	19.65	55.4	72.1	62.25	1500	83
Namibia	34.17	39.0	72.4	2.21	4500	38
Nepal	32.94	58.6	72.4	183.76	1400	28
Netherlands	11.58	78.6	4.3	386.93	25800	99
New Zealand	14.23	78.2	6.2	14.55	19500	99
Nicaragua	26.98	69.4	32.5	38.80	2500	68
Niger	49.95	41.9	122.2	8.40	820	15
Nigeria	39.22	50.6	72.5	140.66	840	57
Norway	12.39	78.9	3.9	13.96	30800	100
Oman	37.76	72.3	21.8	12.77	8200	80
Pakistan	30.40	62.7	78.5	183.67	2100	43
Panama	18.60	75.9	19.6	36.86	5900	91
Papua N.G.	31.61	63.8	56.5	11.17	2400	65
Paraguay	30.50	74.2	28.8	14.47	4600	92
Peru	23.36	70.6	38.2	21.75	4800	88
Philippines	26.88	68.1	27.9	281.75	4000	95
Poland	10.29	73.7	9.2	123.53	8800	99
Portugal	11.50	76.1	5.8	109.15	17300	87
Puerto Rico*	15.04	76.0	9.3	434.75	11200	89
Romania	10.81	70.4	18.9	93.97	6800	97
Russia	9.71	67.5	19.8	8.49	8300	98
Rwanda	33.28	38.7	117.8	280.89	1000	48
Saudi Arabia	37.25	68.4	49.6	11.99	10600	78
Senegal	36.99	62.9	55.4	53.98	1580	39
Serbia and Montenegro	12.80	73.7	17.4	270.13	2370	93
Sierra Leone	44.58	46.0	144.4	78.27	500	31
Singapore	12.78	80.3	3.6	6428.08	24700	94
Slovakia	10.09	74.2	8.8	111.01	11500	97
Slovenia	9.27	75.3	4.5	95.34	16000	99
Somalia	46.83	47.0	122.2	12.16	550	38
South Africa	20.63	45.4	61.8	35.78	9400	85
Spain	9.29	79.1	4.9	79.39	18900	97
Sri Lanka	16.36	72.4	15.7	298.38	3250	90
Sudan	37.21	57.3	67.1	14.80	1360	46
Swaziland	39.59	37.0	109.4	64.71	4200	78
Sweden	9.81	79.8	3.4	19.73	24700	99
Switzerland	9.84	79.9	4.4	176.85	31100	99
Syria	30.11	69.1	32.7	92.64	3200	71
Taiwan	14.21	76.7	6.8	1622.40	17200	94
Tajikistan	32.99	64.3	114.8	46.96	1140	98
Tanzania	39.12	51.7	77.9	39.35	610	68
Thailand	16.39	69.2	29.5	121.31	6600	94
Togo	36.11	54.0	69.3	93.08	1500	52
Trinidad&Tobago	13.66	68.6	24.2	226.94	9000	94
Tunisia	16.83	74.2	28.0	59.99	6600	67
Turkey	17.95	71.5	45.8	86.23	6700	85
Turkmenistan	28.27	61.1	73.2	9.61	4700	98
Uganda	47.15	43.8	89.4	104.64	1200	63
Ukraine	9.59	66.3	21.1	80.17	4200	98
United Arab Em.	18.30	74.5	16.1	29.51	21100	79
United Kingdom	11.34	78.0	5.5	244.17	24700	99
United States	14.10	77.4	6.7	29.14	36300	97
Uruguay	17.28	75.7	14.3	19.22	9200	97
Uzbekistan	26.09	63.9	71.7	57.14	2500	99
Venezuela	20.22	73.6	24.6	26.63	6100	91
Vietnam	20.89	69.9	29.3	246.08	2100	94
Yemen	43.30	60.6	66.8	35.42	820	38
Zambia	41.01	37.4	89.4	13.23	870	79
Zimbabwe	24.59	36.5	63.0	29.13	2450	85

Transcribed Image Text:

A researcher used stepwise regression to create regression models to predict BirthRate (births per 1,000) using five predictors: LifeExp (life expectancy in years). InfMort (infant mortality rate). Density (population density per square kilometer). GDPCap (Gross Domestic Product per capita), and Literate (literacy percent). Interpret these results. Regression Analysis-Stepwise Selection (best model of each size) 153 observations BirthRate is the dependent variable p-values for the coefficients LifeExp Adj R2 .722 Nvar InfMort Density GDPCap Literate 6.318 .724 805 .0000 .eeee .0000 5.334 .802 .807 .806 .811 .0242 .0311 5.261 4 .5764 5.273 .812 5 .5937 .6289 .8440 5.287 .805 .812 Click here for the Excel Data File (a) Which model (Nvar 1, 2, 3. 4. or 5) best balances fit and parsimony? The three variable model best balances fit and parsimony. (b) Does the addition of LifeExp and Density improve the model with respect to the R adj? Yes No (c) Which two variables appear to be the most significant? (You may select more than one answer. Click the box with a check mark for the correct answer and double clck to empty the box for the wrong answer.) LifeExp InfMort Density GDPCap Literate A researcher used stepwise regression to create regression models to predict BirthRate (births per 1,000) using five predictors: LifeExp (life expectancy in years). InfMort (infant mortality rate). Density (population density per square kilometer). GDPCap (Gross Domestic Product per capita), and Literate (literacy percent). Interpret these results. Regression Analysis-Stepwise Selection (best model of each size) 153 observations BirthRate is the dependent variable p-values for the coefficients LifeExp Adj R2 .722 Nvar InfMort Density GDPCap Literate 6.318 .724 805 .0000 .eeee .0000 5.334 .802 .807 .806 .811 .0242 .0311 5.261 4 .5764 5.273 .812 5 .5937 .6289 .8440 5.287 .805 .812 Click here for the Excel Data File (a) Which model (Nvar 1, 2, 3. 4. or 5) best balances fit and parsimony? The three variable model best balances fit and parsimony. (b) Does the addition of LifeExp and Density improve the model with respect to the R adj? Yes No (c) Which two variables appear to be the most significant? (You may select more than one answer. Click the box with a check mark for the correct answer and double clck to empty the box for the wrong answer.) LifeExp InfMort Density GDPCap Literate