Hi,

I’m pretty new to Data Science.

I have some Data with measurement errors and I want to fit a linear model.

```
using DataFrames
using GLM
using StatsPlots
df = DataFrame(x = [0.0, 0.0669873, 0.25, 0.5, 0.75, 0.933013, 1.0],
y = [0.223, 0.291, 0.393, 0.549, 0.73, 0.85, 0.896],
u_y = [0.023, 0.024, 0.027, 0.031, 0.037, 0.041, 0.043])
# Weighted Least Squares
wlr = glm(@formula(y~x), df, Normal(), wts = 1 ./ df.u_y.^2)
x_interval = DataFrame(x = 0:0.01:1);
pred = predict(wlr, x_interval, interval = :confidence)
@df df scatter(:x, :y, yerror = :u_y)
plot!(x_interval.x, pred.prediction,
ribbon = (pred.prediction .- pred.lower, pred.upper .- pred.prediction))
```

I get this result:

The confidence interval should get plotted but it is so small that you can’t even see it.

Shouldn’t it be as wide as the measurement errors?

How do I fit it accordingly?

Thank your for all answers and have a nice day