Rstudio programming
a)
R code
b)
R-output Model fitting summary
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> summary(fit) Call: lm(formula = sys_bp ~ weight) Residuals: Min 1Q Median 3Q Max -17.182 -6.485 -2.519 8.926 12.143 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 69.10437 12.91013 5.353 1.71e-05 *** weight 0.41942 0.07015 5.979 3.59e-06 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 8.681 on 24 degrees of freedom Multiple R-squared: 0.5983, Adjusted R-squared: 0.5815 F-statistic: 35.74 on 1 and 24 DF, p-value: 3.591e-06 |
c)
Fitted linear regression model is
sys_bp =69.10437 + 0.41942 * weight
Interpretation :The Values of multiple R-squared: 0.5983 and Adjusted R-squared: 0.5815 are not closed to 1 so we can conclude that fitted model is not good linear fit between sys_bp and weight.
d)
Ho : B1 =0 ie. weight is not significant to predict sys_bp
VS
H1 : B1 0 i.e. weight is statistically significant.
P-value : 3.59e-06 = 0.00000359 < 0.05
so we reject null hypothesis (H0)
Conclusion : B1 0 so weight is statistically significant with =0.05
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