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This article introduces how to understand the simple linear regression in R language. The content is very detailed. Interested friends can use it for reference. I hope it will be helpful to you.

Basic knowledge of R language:

Simple linear regression

> fit summary (fit)

Call:

Lm (formula = weight ~ height, data = women)

Residuals:

Min 1Q Median 3Q Max

-1.7333-1.1333-0.3833 0.7417 3.1167

Coefficients:

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

(Intercept)-87.51667 5.93694-14.74 1.71e-09 * *

Height 3.45000 0.09114 37.85 1.09e-14 * *

-

Signif. Codes: 0'* * '0.001'.

Residual standard error: 1.525 on 13 degrees of freedom

Multiple R-squared: 0.991, Adjusted R-squared: 0.9903

F-statistic: 1433 on 1 and 13 DF, p-value: 1.091e-14

> women$weight

[1] 115 117 120 123 126 129 132 135 139 142 146 150 154 159 164

> fitted (fit)

1 2 3 4 5 6 7 8 9 10

112.5833 116.0333 119.4833 122.9333 126.3833 129.8333 133.2833 136.7333 140.1833 143.6333

11 12 13 14 15

147.0833 150.5333 153.9833 157.4333 160.8833

> residuals (fit)

1 2 3 4 5 6 7

2.41666667 0.96666667 0.51666667 0.06666667-0.38333333-0.83333333-1.28333333

8 9 10 11 12 13 14

-1.73333333-1.18333333-1.63333333-1.08333333-0.53333333 0.01666667 1.56666667

fifteen

3.11666667

> plot (women$height,women$weight,xlab= "Height (in inpches", ylab = "Weight (in pounds"))

> abline (fit)

Formula:

Because the height cannot be zero, it is just a constant adjustment. In Pr (> | t |), you can see that the regression coefficient (3.45) is significantly less than 0 (p

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