Testing a Statistic without standard deviation?

In summary, when conducting car crash tests, small samples are often used due to the expense involved. In this specific case, 5 BMW cars were crashed under standard conditions and the repair costs were recorded as 797, 571, 904, 1147, and 418 dollars. Using a 0.05 significance level, we can test the claim that the mean repair cost for all BMW cars is less than $1000. To do this, we can use the Student t test, which requires the sample standard deviation. The formula for t is provided, with the sample standard deviation (s) calculated using the given data.
  • #1
KatieLynn
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0

Homework Statement



Due to the expense involved, car crash tests often use small samples. When 5 BMW cars are crashed under standard conditions, the repair costs (in dollars) are as shown in accompanying list. Use 0.05 significance level to test the claim that the mean for all BMW cars is less than $1000.

797, 571, 904, 1147, 418


Homework Equations





The Attempt at a Solution



I don't understand how I can do this problem without being given the standard deviation. Any suggestions?
 
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  • #2
KatieLynn said:

Homework Statement



Due to the expense involved, car crash tests often use small samples. When 5 BMW cars are crashed under standard conditions, the repair costs (in dollars) are as shown in accompanying list. Use 0.05 significance level to test the claim that the mean for all BMW cars is less than $1000.

797, 571, 904, 1147, 418


Homework Equations





The Attempt at a Solution



I don't understand how I can do this problem without being given the standard deviation. Any suggestions?

I wouldl suggest using the Student t test. The statistic t is given by
[tex]t = \frac{\bar{y} - \mu_0}{\frac{s}{\sqrt{n}}}[/tex]

In the above, s is the sample st. dev.
[tex]s = \sqrt{\frac{n \sum_{i = 1}^n x_i^2 -(\sum_{i = 1}^n x_i)^2}{n(n - 1)}}[/tex]
 

What does it mean to test a statistic without standard deviation?

Testing a statistic without standard deviation means determining the significance of a sample statistic without knowing the population's standard deviation. This is commonly used when the standard deviation is unknown or when the sample size is small.

Why is it important to test a statistic without standard deviation?

Testing a statistic without standard deviation allows for determining the significance of a sample statistic without relying on assumptions about the population's standard deviation. This can be useful in situations where the standard deviation is not known or when the sample size is small and assumptions about the population's distribution cannot be made.

What are the different methods for testing a statistic without standard deviation?

There are several methods for testing a statistic without standard deviation, including the t-test, z-test, and chi-square test. Each of these methods has its own assumptions and is appropriate for different types of data and research questions.

What are the assumptions for testing a statistic without standard deviation?

The assumptions for testing a statistic without standard deviation depend on the specific method being used. In general, these methods assume that the sample is representative of the population, the data is normally distributed, and the sample size is large enough for the central limit theorem to apply.

What are some common challenges when testing a statistic without standard deviation?

Some common challenges when testing a statistic without standard deviation include not having a large enough sample size, not having a representative sample, and not having normally distributed data. These challenges can lead to inaccurate results and should be carefully considered when interpreting the results of these tests.

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