a=0.01, z=2.79. h a: u1>u2 What is the critical Z Value? What is the decision making for this hypothesis test?   A is the positive critical value for this question, what is/are the possible critical region(s) for this hypothesis test?

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a=0.01, z=2.79. h a: u1>u2

What is the critical Z Value?

What is the decision making for this hypothesis test?

 

A is the positive critical value for this question, what is/are the possible critical region(s) for this hypothesis test?

 
This image depicts a bell-shaped curve, representing a normal distribution, which is commonly used in statistics to describe how data points are distributed around the mean. 

Key features of the graph:

1. **Symmetry**: The graph is symmetrical around the center line, marked as "0," indicating the mean of the distribution.

2. **Horizontal Axis**: The points labeled "-A" and "A" represent values equidistant from the mean, indicating standard deviations or other significant values in the context of the distribution.

3. **Vertical Lines**: The lines dropping from the peak and at points "-A" and "A" denote critical points for understanding data dispersion.

The normal distribution is fundamental in probability theory and statistics due to its natural occurrence in various datasets and its useful properties in inferential statistics.
Transcribed Image Text:This image depicts a bell-shaped curve, representing a normal distribution, which is commonly used in statistics to describe how data points are distributed around the mean. Key features of the graph: 1. **Symmetry**: The graph is symmetrical around the center line, marked as "0," indicating the mean of the distribution. 2. **Horizontal Axis**: The points labeled "-A" and "A" represent values equidistant from the mean, indicating standard deviations or other significant values in the context of the distribution. 3. **Vertical Lines**: The lines dropping from the peak and at points "-A" and "A" denote critical points for understanding data dispersion. The normal distribution is fundamental in probability theory and statistics due to its natural occurrence in various datasets and its useful properties in inferential statistics.
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