Use the table to answer the question. Note: Round z-scores to the nearest hundredth and then find the required A values using the table. A manufacturer of light bulbs finds that one light bulb model has a mean life span of 1030 h with a standard deviation of 85 h. What percent of these light bulbs will last as follows? (Round your answers to one decimal place.) (a) at least 990 h  % (b) between 840 and 890 h

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Use the table to answer the question. Note: Round z-scores to the nearest hundredth and then find the required A values using the table.

A manufacturer of light bulbs finds that one light bulb model has a mean life span of 1030 h with a standard deviation of 85 h. What percent of these light bulbs will last as follows? (Round your answers to one decimal place.)

(a) at least 990 h
 %

(b) between 840 and 890 h
**Area Under the Standard Normal Curve**

This table provides the area under the standard normal curve for various values of \( z \). The table is organized in columns, each beginning with a specific \( z \) value and its corresponding area \( A \). Here is the structure of the table:

- Each row represents a different \( z \) value and the associated area \( A \) under the standard normal distribution from the mean to that \( z \) value.
- The columns are labeled with pairs of \( z \) values and areas \( A \).
- The table is divided into several sections horizontally, with each section showing incremental increases in the \( z \) value.

**Sample Transcription of Selected Data Points:**

- \( z = 0.00 \), \( A = 0.000 \)
- \( z = 0.01 \), \( A = 0.004 \)
- \( z = 1.12 \), \( A = 0.369 \)
- \( z = 1.68 \), \( A = 0.454 \)
- \( z = 2.24 \), \( A = 0.487 \)
- \( z = 2.80 \), \( A = 0.497 \)

**Description of Data Use:**

This table is used to find the cumulative probability associated with a standard normal distribution up to a given \( z \) value. For example, if you need to find the probability that a standard normal random variable is less than \( z = 1.34 \), you would locate \( z = 1.34 \) and find the corresponding area \( A = 0.409 \).

The table is useful in statistical analysis for converting standard scores (\( z \)-scores) to probabilities and vice versa, which is essential for various confidence interval estimations and hypothesis testing.
Transcribed Image Text:**Area Under the Standard Normal Curve** This table provides the area under the standard normal curve for various values of \( z \). The table is organized in columns, each beginning with a specific \( z \) value and its corresponding area \( A \). Here is the structure of the table: - Each row represents a different \( z \) value and the associated area \( A \) under the standard normal distribution from the mean to that \( z \) value. - The columns are labeled with pairs of \( z \) values and areas \( A \). - The table is divided into several sections horizontally, with each section showing incremental increases in the \( z \) value. **Sample Transcription of Selected Data Points:** - \( z = 0.00 \), \( A = 0.000 \) - \( z = 0.01 \), \( A = 0.004 \) - \( z = 1.12 \), \( A = 0.369 \) - \( z = 1.68 \), \( A = 0.454 \) - \( z = 2.24 \), \( A = 0.487 \) - \( z = 2.80 \), \( A = 0.497 \) **Description of Data Use:** This table is used to find the cumulative probability associated with a standard normal distribution up to a given \( z \) value. For example, if you need to find the probability that a standard normal random variable is less than \( z = 1.34 \), you would locate \( z = 1.34 \) and find the corresponding area \( A = 0.409 \). The table is useful in statistical analysis for converting standard scores (\( z \)-scores) to probabilities and vice versa, which is essential for various confidence interval estimations and hypothesis testing.
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