Sol:
perform linear regression with lm fucntion in R
Rcode:
x <- c(-2,0,5,8,12)
y <- c(-3,2,4,7,10)
lmod <- lm(y~x)
summary(lmod)
Output:
Call:
lm(formula = y ~ x)
Residuals:
1 2 3 4 5
-1.4665 1.8567 -0.3354 0.1494 -0.2043
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.1433 0.8339 0.172 0.87451
x 0.8384 0.1211 6.922 0.00618 **
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 1.387 on 3 degrees of freedom
Multiple R-squared: 0.9411, Adjusted R-squared:
0.9214
F-statistic: 47.91 on 1 and 3 DF, p-value: 0.006181
ANSWER:
test statistic,t=6.922
p value=0.00618
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