Normalizing Data: 200, 300, 400, 600, 1000

Method 1: Min-Max Normalization (new_min=0, new_max=1)

$$v' = \frac{v - min}{max - min} \times (\text{new\_max} - \text{new\_min}) + \text{new\_min}$$

Here $min = 200$, $max = 1000$, range $= 800$, new_min=0, new_max=1, so simply:
$$v' = \frac{v - 200}{800}$$

v	Calculation	v'
200	(200-200)/800	0.000
300	(300-200)/800	0.125
400	(400-200)/800	0.250
600	(600-200)/800	0.500
1000	(1000-200)/800	1.000

Method 2: Z-Score Normalization

$$v' = \frac{v - \mu}{\sigma}$$

Mean: $\mu = \dfrac{200+300+400+600+1000}{5} = \dfrac{2500}{5} = 500$

Variance (population):
$$\sigma^2 = \frac{(200-500)^2+(300-500)^2+(400-500)^2+(600-500)^2+(1000-500)^2}{5}$$
$$= \frac{90000+40000+10000+10000+250000}{5} = \frac{400000}{5} = 80000$$

Std dev: $\sigma = \sqrt{80000} \approx 282.84$

v	v − μ	z = (v−μ)/σ
200	−300	−1.06
300	−200	−0.71
400	−100	−0.35
600	+100	+0.35
1000	+500	+1.77

Summary

Original	Min-Max [0,1]	Z-Score
200	0.000	−1.06
300	0.125	−0.71
400	0.250	−0.35
600	0.500	+0.35
1000	1.000	+1.77

Min-max squeezes everything cleanly into [0,1] (bounded by the observed extremes), while z-score centers the data at 0 with unit spread — the value 1000, being furthest from the mean, gets the largest magnitude z-score (+1.77), reflecting how much of an outlier it is relative to the rest of the group.