**Statistic on aiR**, and kindly contributed to R-bloggers)

We use the **regression analysis** when, from the data sample, we want to derive a statistical model that predicts the values of a variable (Y, *dependent*) from the values of another variable (X, *independent*). The **linear regression**, which is the simplest and most frequent relationship between two quantitative variables, can be *positive* (when X increase, Y increase too) or *negative* (when X increase, Y decrease): this is indicated by the sign of the coefficient `b`

.

To build the line that describes the distribution of points, we might refer to different principles. The most common is the **least squares method** (or *Model I*), and this is the method used by the statistical software R.

Suppose you want to obtain a linear relationship between weight (kg) and height (cm) of 10 subjects.

Weight: 80, 68, 72, 75, 70, 65, 62, 60, 85, 90

The first problem is to decide what is the dependent variable Y and waht is the independent variable X. In general, the independent variable is not affected by an error during the measurement (or affected by random error), while the dependent variable is affected by error. In our case we can assume that the variable weight is the independent variable (X), and the dependent variable height (Y).

So our problem is to find a linear relationship (formula) that allows us to calculate the height, known as the weight of an individual. The simplest formula is that of a broad line of type `Y = a + bX`

. The simple regression line in R is calculated as follows:

height = c(175, 168, 170, 171, 169, 165, 165, 160, 180, 186)

weight = c(80, 68, 72, 75, 70, 65, 62, 60, 85, 90)

model = lm(formula = height ~ weight, x=TRUE, y=TRUE)

model

Call:

lm(formula = height ~ weight, x = TRUE, y = TRUE)

Coefficients:

(Intercept) weight

115.2002 0.7662

The correct syntax of the formula stated in lm is: Y ~ X, then you declare first the dependent variable, and after the independent variable (or variables).

The output of the function is represented by two parameters **a** and **b**: `a=115.2002`

(intercept), `b=0.7662`

(the slope).

The simple calculation of the line is not enough. We must assess the significance of the line, ie if the slope`b`

differs from zero significantly. This may be done with a **Student’s t.test** or with a **Fisher’s F-test**.

In R both can be retrieved very quickly, with the function summary(). Here’s how:

model <- lm(height ~ weight)

summary(model)

Call:

lm(formula = height ~ weight)

Residuals:

Min 1Q Median 3Q Max

-1.6622 -0.9683 -0.1622 0.5679 2.2979

Coefficients:

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

(Intercept) 115.20021 3.48450 33.06 7.64e-10 ***

weight 0.76616 0.04754 16.12 2.21e-07 ***

---

Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 1.405 on 8 degrees of freedom

Multiple R-squared: 0.9701, Adjusted R-squared: 0.9664

F-statistic: 259.7 on 1 and 8 DF, p-value: 2.206e-07

Here too there are the values of the parameters **a** and **b**.

The **Student’s t-test** on the slope in this case has the value 16.12; the **Student’s t-test** on the intercept has value 16.12; the value of the **Fisher’s F test** is 259.7 (is the same value would be achieved by performing an ANOVA on the same data: `anova(model)`

). The p-values of the t-tests and the F-test are less then 0.05, so the model we found is significant.

The **Multiple R-squared** is the **coefficient of determination**. It provides a measure of how well future outcomes are likely to be predicted by the model. In this case, the 97.01% of the data are well predicted (with 95% of significance) by our model.

We can plot on a graph the data points and the regression line, in this way:

plot(weight, height)

abline(model)

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