Find the value of zg. Z0.45 Click the icon to view a table of areas under the normal curve. Z0.45 =O (Round to two decimal places as needed.) %3D

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**Finding the Value of \( Z_{\alpha} \)**

To find the value of \( Z_{0.45} \), you can use the provided table of areas under the normal curve. 

### Steps:

1. **Understanding the Table**: 
   - The table displays cumulative probabilities from the left up to a given Z-score in a standard normal distribution.
   - The rows represent the whole number and tenths place of the Z-score.
   - The columns represent the hundredths place.

2. **Locating \( Z_{0.45} \)**:
   - Identify the row that begins with the digit representing the whole number and tenths place of your Z-score. 
   - Move to the column representing the hundredths place of the Z-score.

3. **Using the Table**: 
   - Find the intersection of the chosen row and column to get the cumulative probability or Z-score value. 

Ensure the Z-score is rounded to two decimal places as needed.

### Table Explanation:

- The table includes color-coding for different Z-score values.
- Probabilities range from 0 to 1, with different shades representing various levels of certainty.

By utilizing this table effectively, you can accurately determine the Z-score value (rounded to two decimal places) corresponding to your desired cumulative probability.
Transcribed Image Text:**Finding the Value of \( Z_{\alpha} \)** To find the value of \( Z_{0.45} \), you can use the provided table of areas under the normal curve. ### Steps: 1. **Understanding the Table**: - The table displays cumulative probabilities from the left up to a given Z-score in a standard normal distribution. - The rows represent the whole number and tenths place of the Z-score. - The columns represent the hundredths place. 2. **Locating \( Z_{0.45} \)**: - Identify the row that begins with the digit representing the whole number and tenths place of your Z-score. - Move to the column representing the hundredths place of the Z-score. 3. **Using the Table**: - Find the intersection of the chosen row and column to get the cumulative probability or Z-score value. Ensure the Z-score is rounded to two decimal places as needed. ### Table Explanation: - The table includes color-coding for different Z-score values. - Probabilities range from 0 to 1, with different shades representing various levels of certainty. By utilizing this table effectively, you can accurately determine the Z-score value (rounded to two decimal places) corresponding to your desired cumulative probability.
Title: Understanding the Standard Normal Distribution Table

**Find the value of \( Z_{\alpha} \).**

\( Z_{0.45} \)  
(Click the icon to view a table of areas under the normal curve)

\( Z_{0.45} = \) [      ]  (Round to two decimal places as needed.)

---

**Tables of Areas Under the Normal Curve**

The table shown is a standard normal distribution table, often referred to as a Z-table. It provides the area (probability) under the standard normal curve to the left of a given Z-score. The table is structured with Z-scores in tenths on the vertical side and hundredths across the top.

- **Diagram**: On the left, there is a graph depicting the standard normal distribution curve. The shaded area under the curve represents the probability or area corresponding to a specific Z-score.

- **Table V**: This is a detailed section of the table:
  - The left column lists Z-scores from -3.4 to 3.4 in increments of 0.1.
  - The top row adds further precision, from .00 to .09, allowing for Z-scores with two decimal places.
  - The body of the table contains probabilities associated with each Z-score.

To find the area (probability) for a specific Z-score (e.g., \( Z = 0.45 \)), locate the value 0.4 in the left column, then move across to the 0.05 column. The intersection gives the area under the curve to the left of the Z-score.

**Options:**

- **Print**: Click this to print the table for further analysis.
- **Done**: Click this when you have completed working with the table.

**Additional Help Options:**

- *Help me solve this*: A guided solution tool.
- *View an example*: Provides an example problem and solution.
- *Get more help*: Offers further resources for assistance.

This table is an essential tool for statistical analyses, particularly in hypothesis testing and confidence interval estimation.
Transcribed Image Text:Title: Understanding the Standard Normal Distribution Table **Find the value of \( Z_{\alpha} \).** \( Z_{0.45} \) (Click the icon to view a table of areas under the normal curve) \( Z_{0.45} = \) [ ] (Round to two decimal places as needed.) --- **Tables of Areas Under the Normal Curve** The table shown is a standard normal distribution table, often referred to as a Z-table. It provides the area (probability) under the standard normal curve to the left of a given Z-score. The table is structured with Z-scores in tenths on the vertical side and hundredths across the top. - **Diagram**: On the left, there is a graph depicting the standard normal distribution curve. The shaded area under the curve represents the probability or area corresponding to a specific Z-score. - **Table V**: This is a detailed section of the table: - The left column lists Z-scores from -3.4 to 3.4 in increments of 0.1. - The top row adds further precision, from .00 to .09, allowing for Z-scores with two decimal places. - The body of the table contains probabilities associated with each Z-score. To find the area (probability) for a specific Z-score (e.g., \( Z = 0.45 \)), locate the value 0.4 in the left column, then move across to the 0.05 column. The intersection gives the area under the curve to the left of the Z-score. **Options:** - **Print**: Click this to print the table for further analysis. - **Done**: Click this when you have completed working with the table. **Additional Help Options:** - *Help me solve this*: A guided solution tool. - *View an example*: Provides an example problem and solution. - *Get more help*: Offers further resources for assistance. This table is an essential tool for statistical analyses, particularly in hypothesis testing and confidence interval estimation.
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