You have sample data that leads you to believe that the average high temperature in Oklahoma City is $$ 92^{\circ} \mathrm{F} $$. What is the alternative hypothesis, $$ H_{a} $$, for this situation? $$ \mu90 $$ x $$ \mu=90 $$ $$ \mu eq 90 $$ REFRY

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You have sample data that leads you to believe that the average high temperature in Oklahoma City is $$ 92^{\circ} \mathrm{F} $$. What is the alternative hypothesis, $$ H_{a} $$, for this situation? $$ \mu<90 $$ $$ \mu>90 $$ x $$ \mu=90 $$ $$ \mu 
eq 90 $$ REFRY

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In hypothesis testing, we typically set up two competing hypotheses: the null hypothesis ($$ H_0 $$) and the alternative hypothesis ($$ H_a $$).

1. **Null Hypothesis ($$ H_0 $$)**: This is a statement of no effect or no difference, and it usually includes the status quo. In this case, if we believe the average high temperature in Oklahoma City is $$ 92^{\circ} \mathrm{F} $$, the null hypothesis would be $$ H_0: \mu = 92 $$.

2. **Alternative Hypothesis ($$ H_a $$)**: This is what we want to test for. It represents a statement that contradicts the null hypothesis.

Given the context of your question, if we are testing whether the average high temperature is different from $$ 90^{\circ} \mathrm{F} $$, the alternative hypothesis would be:

- $$ H_a: \mu 
eq 90 $$

This indicates that we are looking for evidence that the average high temperature is not equal to $$ 90^{\circ} \mathrm{F} $$.

Thus, the correct alternative hypothesis for this situation is:

$$ H_a: \mu 
eq 90 $$
The alternative hypothesis is $$ H_a: \mu 
eq 90 $$.

You have sample data that leads you to believe that the average high temperature in Oklahoma City is \( 92^{\circ} \mathrm{F} \). What is the alternative hypothesis, \( H_{a} \), for this situation? \( \mu<90 \) \( \mu>90 \) x \( \mu=90 \) \( \mu \neq 90 \) REFRY

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