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Sagot :
Let's walk through the steps to solve the problem.
### Step 1: State the Hypotheses
Firstly, we need to establish our null and alternative hypotheses:
- Null Hypothesis ([tex]$H_0$[/tex]): Health and happiness are independent.
- Alternative Hypothesis ([tex]$H_a$[/tex]): Health and happiness are dependent.
These hypotheses can be formally written as:
- [tex]\( H_0 \)[/tex]: Health and happiness are independent.
- [tex]\( H_a \)[/tex]: Health and happiness are dependent.
### Step 2: Determine the Test Statistic
We need to calculate the chi-square test statistic for the given data. The chi-square test statistic measures how the observed counts differ from the expected counts. Given that this test statistic has already been found, we can directly use it:
- Test Statistic ([tex]$\chi^2$[/tex]): [tex]\( \chi^2 = 182.17 \)[/tex]
### Step 3: Determine the p-value
The p-value tells us the probability of observing a chi-square statistic at least as extreme as the one computed, under the null hypothesis. This value has also been previously calculated:
- p-value: [tex]\( p = 0.0000 \)[/tex]
### Step 4: Make a Decision
We compare the p-value to our significance level, [tex]$\alpha = 0.05$[/tex].
- If [tex]\( p < \alpha \)[/tex]: reject the null hypothesis.
- If [tex]\( p \ge \alpha \)[/tex]: fail to reject the null hypothesis.
Since our p-value [tex]\(p = 0.0000\)[/tex] is less than our significance level of 0.05, we reject the null hypothesis.
### Step 5: Conclusion
Based on our decision to reject the null hypothesis, we conclude that:
- Conclusion: There is sufficient evidence to suggest that health and happiness are related.
In summary, the steps are:
1. Null and alternative hypotheses:
- [tex]\( H_0 \)[/tex]: Health and happiness are independent.
- [tex]\( H_a \)[/tex]: Health and happiness are dependent.
2. Test Statistic:
- [tex]\( \chi^2 = 182.17 \)[/tex]
3. p-value:
- [tex]\( p = 0.0000 \)[/tex]
4. Decision:
- Reject the null hypothesis.
5. Conclusion:
- There is sufficient evidence to suggest that health and happiness are related.
### Step 1: State the Hypotheses
Firstly, we need to establish our null and alternative hypotheses:
- Null Hypothesis ([tex]$H_0$[/tex]): Health and happiness are independent.
- Alternative Hypothesis ([tex]$H_a$[/tex]): Health and happiness are dependent.
These hypotheses can be formally written as:
- [tex]\( H_0 \)[/tex]: Health and happiness are independent.
- [tex]\( H_a \)[/tex]: Health and happiness are dependent.
### Step 2: Determine the Test Statistic
We need to calculate the chi-square test statistic for the given data. The chi-square test statistic measures how the observed counts differ from the expected counts. Given that this test statistic has already been found, we can directly use it:
- Test Statistic ([tex]$\chi^2$[/tex]): [tex]\( \chi^2 = 182.17 \)[/tex]
### Step 3: Determine the p-value
The p-value tells us the probability of observing a chi-square statistic at least as extreme as the one computed, under the null hypothesis. This value has also been previously calculated:
- p-value: [tex]\( p = 0.0000 \)[/tex]
### Step 4: Make a Decision
We compare the p-value to our significance level, [tex]$\alpha = 0.05$[/tex].
- If [tex]\( p < \alpha \)[/tex]: reject the null hypothesis.
- If [tex]\( p \ge \alpha \)[/tex]: fail to reject the null hypothesis.
Since our p-value [tex]\(p = 0.0000\)[/tex] is less than our significance level of 0.05, we reject the null hypothesis.
### Step 5: Conclusion
Based on our decision to reject the null hypothesis, we conclude that:
- Conclusion: There is sufficient evidence to suggest that health and happiness are related.
In summary, the steps are:
1. Null and alternative hypotheses:
- [tex]\( H_0 \)[/tex]: Health and happiness are independent.
- [tex]\( H_a \)[/tex]: Health and happiness are dependent.
2. Test Statistic:
- [tex]\( \chi^2 = 182.17 \)[/tex]
3. p-value:
- [tex]\( p = 0.0000 \)[/tex]
4. Decision:
- Reject the null hypothesis.
5. Conclusion:
- There is sufficient evidence to suggest that health and happiness are related.
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