If f(x_r)*f(x_u) < 0, then x_u = (x_r + x_l)/2 and x_l = (x_r + x_u)/2.

If f(x_l)*f(x_r) < 0, then x_l = x_r and x_u stays the same.

If f(x_r)*f(x_u) < 0, then x_u = x_r and x_l stays the same.

If f(x_r)*f(x_u) > 0, then x_l = x_r and x_u stays the same.

If f(x_l)*f(x_r) > 0, then x_u = x_r and x_l stays the same.

If f(x_r)*f(x_u) > 0, then x_u = (x_r + x_l)/2 and x_l = (x_r + x_u)/2.

If f(x_r)*f(x_u) > 0, then x_u = x_r and x_l stays the same. 

If f(x_r)*f(x_u) > 0, then x_u = (x_r + x_u)/2 and x_l = (x_r + x_l)/2.

4

3

2

5

None of the above

n represents the number of points

p provides a function handle for a polynomial

yp=polyval(p,x) is equivalent to yp=y

w = @(c)  t(1)*c.^2 + t(2)*c + t(3)

None of the above

w = @(c)  t(3)*c.^2 + t(2)*c + t(1)

w = @(c)  c(3)*t.^2 + c(2)*t + c(1)

[1  3]

[1  0  3  4]

[1  4  6  8  2]

[1  1  -5]

It is the vertical distance between the datapoint and the model

It is the distance between the datapoint and the model that is perpendicular to the fitted line’s gradient

It is the averaged distance between the datapoint and the model

It is the horizontal distance between the datapoint and the model

log(H) = xlog(4πω) + γ

log10(H) = log10(γ) + 4πω log10(x)

log10(H) = xlog10(γ) + 4πω

log(H) = γlog(4πω) + x

1/θ = σx/k^2 + 1/k^2 

θ = k^2/(σx) + k^2

1/θ = σ/(k^2*x) + 1/k^2 

1/θ = k^2/(σx) + 1/k^2 

log⁡(H)/2 = log(δL) + μ

log⁡(H) = μlog(2δ) + 2L

log⁡(H) = 2log(μ) + δL

log⁡(H) = log(μ) + 2δL