QUESTION ONE:
In your own words, give the meaning of the p-value and how it relates to the probability of Type-I error.
QUESTION TWO:
In your own words explain why random sampling is important. Give an example of how to do a random sample of a population and also give an example of a bad sampling scheme.
QUESTION THREE:
You are given the distribution of ages of students enrolled at College of the Redwoods from a simple random sample of size 53. You plot the data and it appears to be strongly skewed to the right.
(a) What would the relationship be between the mean and the median? Explain.
(b) What would be the best way to summarize the data numerically? Give your reasoning on how you came to this conclusion.
QUESTION FOUR:
You are given the distribution of ages of students enrolled at College of the Redwoods from a simple random sample of size 53. You plot the data and it appears to be strongly skewed to the right. You consult the college admissions office and they inform you that the population means age of students is 21.3 years with a population standard deviation of 10.7 years. What would be the sampling distribution of the mean be in this case?
QUESTION FIVE:
Your Aunt reads in the newspaper that the percent of Americans in favor of the death penalty for a person convicted of murder is 63% with a margin of error of 2.5%. They also state that the level of confidence was 95%. She knows you are taking a statistics course and wants you to explain what the margin of error and level of confidence mean. What would you tell her? Explain in your own words what the margin of error represents and how to interpret the level of confidence.
QUESTION SIX:
Since residuals measure how far the observations are from the regression line, they are often used to assess the fit of the regression line to the data. We might display these vertical deviations graphically using a residual plot. By plotting the residuals against the explanatory variable x, we effectively magnify the deviations (that is, change the y-axis from response to vertical deviations), which allows for a better and closer examination of the deviations. Describe what a residual plot would look like when a linear model is appropriate and an example of what a residual plot would look like where a linear model would not be appropriate.