Question

Country CM FLR 6.66 6.15 UWO00 OWN PGNP TFR 1870 130 310 570 2050 200 670 300 120 290 1180 900 1730 1150 1160 1270 580 165 6.25 3.81 6.44 6.19 5.89 5.89 2.36 3.93 5.99 3.5 7.41 4.21 5 5.27 5.21 6.5 6.12 6.19 5.05 6.16 1.8 4.75 660 96 148 98 161 118 269 189 420 1080 290 270 560 4240 240 81 WWWWMNNNNNNNE 63 3020 1420 420 19830 420 530 8640 350 230 1620 6.66 7.28 8.12 5.23 5.79 6.5 7.17 6.6 152 104 287 41 312 190 2090 77 142 900 3.91 6.7 4.2 5.43 6.5 6.25 7.1 7.1 262 SSW 215 246 31 191 182 230 140 330 1010 300 1730 780 1300 37 3.46 5.66 35 4.82 8&9& 85 78 85 930 5 4.74 83 690

Answer 1

1.

The given model is a Multiple Linear Regression model. A Multiple Linear Regression model makes 4 key assumptions. These are –

1. Linearity: The relation of the independent and dependent variables is assumed to be linear.

2. Normality: The residuals are assumed to be normally distributed.

3. No Multicollinearity: There is no multicollinearity present in the data set. Multicollinearity basically occurs when the correlation among the independent variables is too high.

4. Homoscedasticity: This assumption refers to that the variance of error terms is similar across the values of the independent variables.