The missing data value according to the given z-score is <u>39</u>.
We can determine how distant a data point is from the mean using its z-score. It is a crucial subject in statistics. Z-scores are a way to compare data to a population that is considered "normal." When attempting to compare someone's weight to that of the "average" person, for instance, it might be intimidating to look at a large table of data even though we know they weigh 70 kg. A z-score offers us an indication of how that person's weight compares to the mean weight of the general population. We shall discover what the z score is in this post.
The z score is a measurement of how many standard deviations a raw score is below or above the population mean. If the value is higher than the mean, it will be positive; if it is lower, it will be negative. The standard score is another name for it. It shows how far away from the mean an object is, in terms of standard deviations. The mean and population standard deviation must be known to apply a z-score. The likelihood of a score happening inside a typical normal distribution may be calculated with the use of a z score. We may also compare two scores from other samples thanks to it. A z score table is a table that contains the values of, which represent the cumulative distribution function of the normal distribution.
The equation is given by z = (x – μ)/ σ.
μ = mean
σ = standard deviation
x = test value.
In the question, z = -2.1, μ = 43, and σ = 2.
Substituting the values, we get:
-2.1 = (x - 43)/2,
or, x - 43 = -2.1*2,
or, x = -4.2 + 43,
or, x = 38.8 ≈ 39.
Thus, the missing data value according to the given z-score is <u>39</u>.
Learn more about z-scores at
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