As Kristoffer says, an MWE would be helpful here. The following works for me:
julia> using GLM, DataFrames
julia> data = DataFrame(A = rand(1:122, 50), B = rand(22.1:0.1:22.5, 50))
50×2 DataFrame
│ Row │ A │ B │
│ │ Int64 │ Float64 │
├─────┼───────┼─────────┤
│ 1 │ 4 │ 22.1 │
│ 2 │ 117 │ 22.5 │
...
julia> lm(@formula(A ~ B), data)
A ~ 1 + B
Coefficients:
──────────────────────────────────────────────────────────────────────────────
Estimate Std. Error t value Pr(>|t|) Lower 95% Upper 95%
──────────────────────────────────────────────────────────────────────────────
(Intercept) -470.991 882.242 -0.533857 0.5959 -2244.86 1302.87
B 23.7686 39.5652 0.600744 0.5508 -55.7826 103.32
──────────────────────────────────────────────────────────────────────────────
(modulo some DataFrames deprecation warnings currently)