Aleast squares regression line was calculated to relate the length (cm) of newborn boys to their weight in kg. the line is weight equals negative 5.33 plus 0.1926 length. a newborn was 48 cm long and weighed 3 kg. according to the regression model, what was his residual? what does that say about him?

The least squares regression line minimizes the vertical distance between

the data points and the regression line

Reasons:

Known parameters;

The least squares regression line equation is

Given length of the newborn baby = 48 cm

Weight of the newborn baby = 3 kg

Given regression line equation; Weight = -5.33 + 0.1926 × Length

Required:

The residual (weight) of the newborn baby boy

Solution:

The given equation can be presented as; = -5.33 + 0.1926·x

The predicted weight of the baby is   = -5.33 + 0.1926 × 48 = 3.9148

The residual, =

∴ The residual of the weight of the newborn is 3 kg - 3.9148 kg = -0.9148 kg

From the negative residual, what can be said is that the

A negative residual is an indication of a too high predicted value

The negative residual means that the baby's weight is lower than predicted

Learn more here:

link

1. -0.9148

2. the weight predicted of newborn was too high for that length.

Step-by-step explanation:

The regression equation relates the length (cm) of newborn boys to their weight in kg.

W=-5.33+0.1926l where W is the weight in kg and l is length in cm

To get the residual, you calculate the difference between the observed value and the predicted value

Residual=observed value- predicted value

Given the newborn was 48 cm long and 3kg in weight then predicted weight is;

W=-5.33+0.1926l

W=-5.33+0.1926*48

W=-5.33+9.2448

W=3.9148 kg

Residual= 3-3.9148 = -0.9148

A negative residual mean that the prediction for the weight was too high with the corresponding length of the newborn.

21.4 i did it already for s1

step-by-step explanation:

its 4 because 2 times 2 is 4, if you double the number two you get four