Linear Regression

Data points are distributed normally about the line y = mx + c

σ = 1.45 (slider from 0 to 5, step 0.01)
μ = -0.8 (slider from -5 to 5, step 0.1)
c = 8.8 (slider from -5 to 15, step 0.1)
m = 0.6 (slider from -5 to 5, step 0.1)
A1 = -8
A2 = -7
A3: 2 * A2 - A1
A4: 2 * A3 - A2
A5: 2 * A4 - A3
A6: 2 * A5 - A4
A7: 2 * A6 - A5
A8: 2 * A7 - A6
A9: 2 * A8 - A7
A10: 2 * A9 - A8
A11: 2 * A10 - A9
A12: 2 * A11 - A10
A13: 2 * A12 - A11
A14: 2 * A13 - A12
A15: 2 * A14 - A13
A16: 2 * A15 - A14
A17: 2 * A16 - A15
A18: 2 * A17 - A16
A19: 2 * A18 - A17
B2: m * A2 + c + RandomNormal[μ, σ]
B3: m * A3 + c + RandomNormal[μ, σ]
B4: m * A4 + c + RandomNormal[μ, σ]
B5: m * A5 + c + RandomNormal[μ, σ]
B6: m * A6 + c + RandomNormal[μ, σ]
B7: m * A7 + c + RandomNormal[μ, σ]
B8: m * A8 + c + RandomNormal[μ, σ]
B9: m * A9 + c + RandomNormal[μ, σ]
B10: m * A10 + c + RandomNormal[μ, σ]
B11: m * A11 + c + RandomNormal[μ, σ]
B12: m * A12 + c + RandomNormal[μ, σ]
B13: m * A13 + c + RandomNormal[μ, σ]
B14: m * A14 + c + RandomNormal[μ, σ]
B15: m * A15 + c + RandomNormal[μ, σ]
B16: m * A16 + c + RandomNormal[μ, σ]
B17: m * A17 + c + RandomNormal[μ, σ]
B18: m * A18 + c + RandomNormal[μ, σ]
B19: m * A19 + c + RandomNormal[μ, σ]
B1: m * A1 + c + RandomNormal[μ, σ]
A: (A1, B1)
B: (A2, B2)
C: (A3, B3)
D: (A4, B4)
E: (A5, B5)
F: (A6, B6)
G: (A7, B7)
H: (A8, B8)
I: (A9, B9)
J: (A10, B10)
K: (A11, B11)
L: (A12, B12)
M: (A13, B13)
N: (A14, B14)
O: (A15, B15)
P: (A16, B16)
Q: (A17, B17)
R: (A18, B18)
S: (A19, B19)
list1: {A, B, C, D, E, F, G, H, I, J, K, L, M, N, O, P, Q, R, S}
LOBF = FitLineY(list1)
a: y = m * x + c
Mean: (MeanX[list1], MeanY[list1])
listx: {A1, A2, A3, A4, A5, A6, A7, A8, A9, A10, A11, A12, A13, A14, A15, A16, A17, A18, A19}
listy: {B1, B2, B3, B4, B5, B6, B7, B8, B9, B10, B11, B12, B13, B14, B15, B16, B17, B18, B19}
b = PMCC(listx, listy)
text1: "PMCC = " + b + ""