A consumer group has determined that the distribution of life spans for gas ranges (stoves) has a mean of 15.0 years and a stand
1 answer:
Answer:
b. Mean = 1.6 years, standard deviation - 0.92 years, shape: approximately Normal.
Step-by-step explanation:
Central Limit Theorem
The Central Limit Theorem estabilishes that, for a normally distributed random variable X, with mean and standard deviation
, the sampling distribution of the sample means with size n can be approximated to a normal distribution with mean
and standard deviation
.
For a skewed variable, the Central Limit Theorem can also be applied, as long as n is at least 30.
Subtraction of normal Variables:
When we subtract normal variables, the mean is the subtraction of the means, while the standard deviation is the square root of the sum of the variances.
A consumer group has determined that the distribution of life spans for gas ranges (stoves) has a mean of 15.0 years and a standard deviation of 4.2 years. Sample of 35:
This means that:
The distribution of life spans for electric ranges has a mean of 13.4 years and a standard deviation of 3.7 years. Sample of 40:
This means that:
Which of the following best describes the sampling distribution of the difference in mean life span of gas ranges and electric ranges?
Shape is approximately normal.
Mean:
Standard deviation:
So the correct answer is given by option b.
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There appears to be a positive correlation between the number of hour spent studydng and the score on the test.
When identifying the independent and dependent quantities, we think about what would cause the other to change. The score on the test would not cause the number of hours spent studying to change; rather, the number of hours spent studying would cause the score to change. This means that the number of hours studying would be the independent quantity and the score would be the dependent quantity.
Plotting the graph with the time studying on the x-axis (independent) and the score on the y-axis (dependent) gives you the graph shown. You can see in the image that there seems to be a positive correlation; the data seem to generally be heading upward.
When identifying the independent and dependent quantities, we think about what would cause the other to change. The score on the test would not cause the number of hours spent studying to change; rather, the number of hours spent studying would cause the score to change. This means that the number of hours studying would be the independent quantity and the score would be the dependent quantity.
Plotting the graph with the time studying on the x-axis (independent) and the score on the y-axis (dependent) gives you the graph shown. You can see in the image that there seems to be a positive correlation; the data seem to generally be heading upward.
Answer:
The appropriate hypothesis set for Andraya to test is:
Step-by-step explanation:
The makers of the game claim that the wheel has an equally likely probability of landing on each section.
That is, the makers claim that the probability of earning the grand prize is:
Andraya has played the game for several months and believes that the grand prize occurs less often than the game makers claim.
At the null hypothesis, we test if it happens as often as the makers claim, that is, the proportion is 0.125. So
At the alternate hypothesis, we test if it happens less ofter than the makers claim, that is, the proportion is less than 0.125. So
The appropriate hypothesis set for Andraya to test is: