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# How to Plot a Confidence Interval in R

A confidence interval is a range of values that is likely to contain a population parameter with a certain level of confidence.

This tutorial explains how to plot a confidence interval for a dataset in R.

### Example: Plotting a Confidence Interval in R

Suppose we have the following dataset in R with 100 rows and 2 columns:

```#make this example reproducible
set.seed(0)

#create dataset
x #view first six rows of dataset

x          y
1  1.2629543  3.3077678
2 -0.3262334 -1.4292433
3  1.3297993  2.0436086
4  1.2724293  2.5914389
5  0.4146414 -0.3011029
6 -1.5399500 -2.5031813
```

To create a plot of the relationship between x and y, we can first fit a linear regression model:

`model `

Next, we can create a plot of the estimated linear regression line using the abline() function and the lines() function to create the actual confidence bands:

```#get predicted y values using regression equation
newx #create plot of x vs. y, but don't display individual points (type='n')
plot(y ~ x, data = df, type = 'n')

abline(model)

#add dashed lines for confidence bands
lines(newx, preds[ ,3], lty = 'dashed', col = 'blue')
lines(newx, preds[ ,2], lty = 'dashed', col = 'blue')```

The black line displays the fitted linear regression line while the two dashed blue lines display the confidence intervals.

If youâ€™d like, you can also fill in the area between the confidence interval lines and the estimated linear regression line using the following code:

```#create plot of x vs. y
plot(y ~ x, data = df, type = 'n')

#fill in area between regression line and confidence interval
polygon(c(rev(newx), newx), c(rev(preds[ ,3]), preds[ ,2]), col = 'grey', border = NA)

abline(model)

#add dashed lines for confidence bands
lines(newx, preds[ ,3], lty = 'dashed', col = 'blue')
lines(newx, preds[ ,2], lty = 'dashed', col = 'blue')```

Hereâ€™s the complete code from start to finish:

```#make this example reproducible
set.seed(0)

#create dataset
x
y
df

#fit linear regression model
model

#get predicted y values using regression equation
newx
preds

#create plot of x vs. y
plot(y ~ x, data = df, type = 'n')

#fill in area between regression line and confidence interval
polygon(c(rev(newx), newx), c(rev(preds[ ,3]), preds[ ,2]), col = 'grey', border = NA)