What is the z score of 0.05?

Answer 1

The z-score of 0.05 is 1.64.

To find the #z# score for 0.05, we have to refer the Area Under Normal Distribution Table.

#z#scores are given along the 1st column and 1st row.

The table is populated with probability values or area under normal curve.

The given value is significance level. We have to find its corresponding confidence level. To do that -

#0.5 - 0.05 = 0.45#

The #z# score for 0.45 is the same as the #z# score for 0.05.

Look for this value in the table. At times you may not get the exact value. Then take the nearest value. The nearest value is 0.4495. Move horizontally to the z- score column. The value is 1.6. Then move vertically up to the z-score column. The value is 0.04.

Add these two values #1.6 + 0.04=1.64#

The z-score of 0.05 is 1.64.

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Answer 2

The z-score of 0.05 can be calculated using standard normal distribution tables or a statistical calculator. The z-score represents the number of standard deviations a value is from the mean in a normal distribution. For a value of 0.05, the z-score indicates how many standard deviations below or above the mean it lies.

Using standard normal distribution tables, we can find that the z-score corresponding to a cumulative probability of 0.05 (or 5th percentile) is approximately -1.645. Therefore, the z-score of 0.05 is -1.645.

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Answer from HIX Tutor

When evaluating a one-sided limit, you need to be careful when a quantity is approaching zero since its sign is different depending on which way it is approaching zero from. Let us look at some examples.

When evaluating a one-sided limit, you need to be careful when a quantity is approaching zero since its sign is different depending on which way it is approaching zero from. Let us look at some examples.

When evaluating a one-sided limit, you need to be careful when a quantity is approaching zero since its sign is different depending on which way it is approaching zero from. Let us look at some examples.

When evaluating a one-sided limit, you need to be careful when a quantity is approaching zero since its sign is different depending on which way it is approaching zero from. Let us look at some examples.

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