A study utilized a one-time questionnaire to collect data from 600 people to identify factors associated with body mass index (BMI). The variables for the analysis are described in the table below.

Variable Name	Variable Description	Value
ID	Participant ID	1 to 600
age	Age (in years)	Continuous
sex	Sex assigned at birth	1=Females 0=Males
smoke	Current smoking status	1=Yes 0=No
bmi	Body mass index (kg/m2)	Continuous

> summary(lm(bmi~sex+smoke+age))

Call:

lm(formula = bmi ~ sex + smoke + age)

Residuals:

Min 1Q Median 3Q Max

-10.1280 -2.9205 -0.4545 2.3458 17.5262

Coefficients:

Estimate Std. Error t value Pr(>|t|)

(Intercept) 31.53865 1.46909 21.468 <2e-16 ***

sex -1.04099 0.41222 -2.525 0.0118 *

smoke -1.32750 0.51584 -2.573 0.0103 *

age -0.03989 0.02211 -1.804 0.0717 .

---

Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 4.542 on 596 degrees of freedom

Multiple R-squared: 0.02771, Adjusted R-squared: 0.02282

F-statistic: 5.662 on 3 and 596 DF, p-value: 0.0007911

Does the multiple linear regression model explain most of the variability in BMI? Briefly justify your response.