MUSA T. GANIYU
MAY 21, 2016
Introduces a data set on birth weight of babies. Another variable we consider is parity, which is 0 if the child is the rst born, and 1 otherwise. The summary table below shows the results of a linear regression model for predicting the average birth weight of babies, measured in ounces, from parity.
A typical multiple linear regression equation looks like:
\( { Y }\quad =\quad { B }_{ 0 }\quad +\quad { B }_{ 1 }{ X }_{ 1 }\quad +\quad { B }_{ 2 }{ X }_{ 2 }\quad +\quad \) ………+\( \quad { B }_{ n }{ X }_{ n }\quad \) +\( \quad { e}\\ \)
The equation of regression line is therefore,
\( { BabyWeights}\quad =\quad { 120.07}\quad -\quad {1.93}*{parity}\quad \)
where the dependent variable is Baby_weight, Intercept is 120.07 , and slope or parameter is -1.93
Since 0 represent a parity with first born, while 1 represent others,
therefore,
First son parity:
\( { BabyWeights}\quad =\quad { 120.07}\quad -\quad {1.93}*{0}\quad \) = 120.07
Other parity:
\( { BabyWeights}\quad =\quad { 120.07}\quad -\quad {1.93}*{1}\quad \) = 118.14
from the above estimation, we can deduce that first son parity is 1.93 ounces greater than other parity.
Hypothesis:
\( H_0: \) \( \mu_1 \) = 0
\( H_a: \) Not \( H_0: \)
From the table, we found out that the T = -1.62, P-Value = 0.1052,
but \( \alpha \) is 0.05, we therefore accept \( H_0: \), and conclude that the data did not provide a strong evidence about the slope being different from 0, and the data didnt provide prove of association between the birth weight and parity.
THANK YOU FOR YOUR ATTENTION !.