I used R software to solve this question.
R codes and output:
> d=read.table('sbp.csv',header=T,sep=',')
> head(d)
weight age SBP
1 135 3 89
2 120 4 90
3 100 3 83
4 105 2 77
5 130 4 92
6 125 5 98
> attach(d)
> fit=lm(SBP~weight+age)
> summary(fit)
Call:
lm(formula = SBP ~ weight + age)
Residuals:
Min 1Q Median 3Q Max
-4.0438 -1.3481 -0.2395 0.9688 6.6964
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 53.45019 4.53189 11.794 2.57e-08 ***
weight 0.12558 0.03434 3.657 0.0029 **
age 5.88772 0.68021 8.656 9.34e-07 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 2.479 on 13 degrees of freedom
Multiple R-squared: 0.8809, Adjusted R-squared: 0.8626
F-statistic: 48.08 on 2 and 13 DF, p-value: 9.844e-07
Equation of least square regression line is,
Systolic BP = 53.4501 + 0.12558 weight + 5.88772 age
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