Decoding Z-Score Chart Accuracy

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In summary, the conversation discusses finding the value of alpha using a chart in a textbook. The solutions manual provides a method of finding alpha by subtracting the confidence coefficient from 1 and dividing by 2, then looking up the resulting value in the chart. However, there is confusion on how the value of 1.96 was obtained, as the chart only provides decimal values. It is explained that the value was rounded to two decimal places.
  • #1
60051
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I made sure this chart is identical to the one in my textbook, so there are no discrepancies.

So in the solutions manual, they find alpha by subtracting the confidence coefficient from 1, then dividing that value by 2, and looking up that value in the table.

So for one example, they used that chart and found that z0.025 = 1.96. I just don't see this though. If you look up 0.025 in the chart, you get 0.1985. In fact, all the values in the chart are decimals, so how did they get 1.96?
 
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  • #2
The table is one of probabilities P(0 <= z <= n.nn), where n.n is one of the numbers down the left column and 0.0n is a number across the top row.

z0.025 is the z score for which the probability that z > some number is 0.025. Another way to say this is that the probability (or area) in the "tail" is 0.025. This means that the remaining probability is 0.975. Since the table gives probabilities for z values that are greater than or equal to 0, the probability you want is 0.475.

Look in the body of the table for 0.475. You will find this in the row that is marked 1.9, and in the column marked 0.06. What this is telling you is that P(0 <= z <= 1.96) = 0.475, or equivalently, P(z > 1.96) = 0.025.
 
  • #3
How did you get 0.475? 0.95/2?
 
  • #4
That will work, but you can also subtract .025 from .500.
 
  • #5
60051 said:
I made sure this chart is identical to the one in my textbook, so there are no discrepancies.

So in the solutions manual, they find alpha by subtracting the confidence coefficient from 1, then dividing that value by 2, and looking up that value in the table.

So for one example, they used that chart and found that z0.025 = 1.96. I just don't see this though. If you look up 0.025 in the chart, you get 0.1985. In fact, all the values in the chart are decimals, so how did they get 1.96?
??They rounded off to two decimal places!
 
  • #6
HoI, I think you missed that decimal point. If you round 0.1985 to two decimal places, you 0.20, not 1.96. My explanation is in post 2.
 

Related to Decoding Z-Score Chart Accuracy

What is a z-score chart?

A z-score chart, also known as a standard normal distribution table, is a table that shows the probability of a score falling within a certain number of standard deviations from the mean in a normal distribution.

How do you read a z-score chart?

To read a z-score chart, you first need to find the z-score of the value you are interested in. Then, locate the corresponding row and column on the chart. The number at the intersection of the row and column is the probability of the score falling within that z-score.

What does a z-score represent?

A z-score represents the number of standard deviations a score is above or below the mean in a normal distribution. A positive z-score indicates a score above the mean, while a negative z-score indicates a score below the mean.

How is a z-score calculated?

A z-score is calculated by subtracting the mean from the score and then dividing by the standard deviation. The formula is z = (x - μ) / σ, where x is the score, μ is the mean, and σ is the standard deviation.

Why is a z-score chart useful?

A z-score chart is useful because it allows us to quickly and easily determine the probability of a score falling within a certain range in a normal distribution. This can be helpful in analyzing data and making statistical inferences.

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