10.7 Small-Sample Inferences Concerning A Population Mean

In summary, the conversation discussed the dissolved oxygen content in a river, which is affected by industrial waste and sewage. The state agency requires a minimum of 5 ppm for the oxygen content to support aquatic life. Six water specimens were taken and the results showed a sample mean of 4.93 ppm. The calculations were done to determine if there is sufficient evidence to indicate that the dissolved oxygen content is less than 5 ppm. However, the answer obtained was slightly different from the one in the book due to rounding errors.
  • #1
Little Bear
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Homework Statement


Dissolved O2 Content. Industrial wastes and sewage dumped into our rivers and streams absorb oxygen and thereby reduce the amount of dissolved oxygen available for fish and other forms of aquatic life. One state agency requires a minimum of 5 parts per million (ppm) of dissolved oxygen in order for the oxygen content to be sufficient to support aquatic life. Six water specimens taken from a river at a specific location during the low-water season (July) gave readings of 4.9, 5.1, 4.9, 5.0, 5.0 and 4.7 ppm of dissolved oxygen. Do the data provide sufficient evidence to indicate that the dissolved oxygen content is less than 5 ppm? Test using alpha = .05.

Homework Equations


n = 6
sum of x samples = x1+x2...+xn
ample mean = (x1+ x2...+xn)/n
Sum of squares = x1^2+x2^2+...+xn^2
s^2 = [sum of squares – (sum of x samples)^2/n]/[n-1]
s = sqrt(s^2)
popn mean = 5 (parts ppm)
t = [sample mean – popn mean]/[s/sqrt(n)]

The Attempt at a Solution


sum of x samples = [4.9 + 5.1 + 4.9 + 5.0 + 5.0 + 4.7] = 29.6
sample mean = sum of x samples/6 = 29.6/6 = 4.93
sum of squares(x^2) = [4.9^2+ 5.1^2+ 4.9^2+ 5.0^2+ 5.0^2+ 4.7^2]
= 24.01 + 26.01 + 24.01 + 25 + 25 + 22.09
= 146.12
s^2= [Sum of x^2– (Sum of x samples)^2/n]/[n-1]
= [146.12 – (29.6^2/6)]/[6-1]
= [146.12 – 146.03]/5
= 0.09/5
= 0.018
s = sqrt(0.018)
s = 0.134
t = [sample mean – popn mean]/[s/sqrt(n)]
= [4.93 – 5]/[0.134/sqrt(6)]
= -0.07/0.0547
= -1.28
The answer at the back of the book is:
No; t = -1.195.
So, what did I do wrong in my calculations?
 
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  • #2
You rounded too early. If you hold all your calculations to more decimal places you will get the book's answer.
 
  • #3


Your calculations seem to be correct. The only difference between your answer and the one in the book is in the rounding of the values. The book most likely rounded the values to a certain number of decimal places, resulting in a slightly different answer. In scientific calculations, it is important to use the full precision of the values to avoid rounding errors. In this case, both answers are within the margin of error and can be considered correct.
 

Related to 10.7 Small-Sample Inferences Concerning A Population Mean

1. What is a small sample inference?

A small sample inference is a statistical method used to make conclusions about a population when the sample size is relatively small. This is necessary because it is often not feasible to collect data from an entire population, so a smaller sample is used to make inferences about the larger population.

2. How is the sample size determined for small sample inferences?

The sample size for small sample inferences is typically determined by calculating the minimum sample size needed to achieve a desired level of accuracy and confidence in the results. This can be done using statistical power analysis or by consulting a table of recommended sample sizes based on the population size and desired confidence level.

3. What is the central limit theorem and how does it relate to small sample inferences?

The central limit theorem states that, regardless of the shape of the population distribution, the sampling distribution of the sample means will be approximately normal as the sample size increases. This is important for small sample inferences because it allows us to use the normal distribution to make inferences about the population mean, even when the sample size is small.

4. What is a confidence interval and how is it used in small sample inferences?

A confidence interval is a range of values that is likely to contain the true population parameter with a certain level of confidence. In small sample inferences, a confidence interval is calculated using the sample mean and standard error, and it provides a range of values within which the true population mean is likely to fall. This can be used to make inferences about the population mean, such as whether it is significantly different from a hypothesized value.

5. How do you test a hypothesis using small sample inferences?

To test a hypothesis using small sample inferences, a t-test is typically used. This involves calculating a t-statistic using the sample mean, standard deviation, and sample size, and comparing it to a critical value from a t-distribution table. If the t-statistic falls within the critical region, the null hypothesis is rejected and it can be concluded that the population mean is significantly different from the hypothesized value.

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