2010 Healthcare Law. (6.48, p. 248) On June 28, 2012 the U.S. Supreme Court upheld the much debated 2010 healthcare law, declaring it constitutional. A Gallup poll released the day after this decision indicates that 46% of 1,012 Americans agree with this decision. At a 95% confidence level, this sample has a 3% margin of error. Based on this information, determine if the following statements are true or false, and explain your reasoning.

  1. We are 95% confident that between 43% and 49% of Americans in this sample support the decision of the U.S. Supreme Court on the 2010 healthcare law.
  2. We are 95% confident that between 43% and 49% of Americans support the decision of the U.S. Supreme Court on the 2010 healthcare law.
  3. If we considered many random samples of 1,012 Americans, and we calculated the sample proportions of those who support the decision of the U.S. Supreme Court, 95% of those sample proportions will be between 43% and 49%.
  4. The margin of error at a 90% confidence level would be higher than 3%.

Answer

TRUE. Since the margin of error is .03 that would be +-.03 to .46 between 43% to 49% TRUE. Yes we know that because of the Confidence interval that is between .43 to .49 TRUE. YEs if we satisfy the conditions of inedependant observations which they are and have the rule of n*p>10 and n(1-p)>10 then we can use inference and calculate the confidence interval of 95% and we will get an answer close to this. TRUE . Because our confidence is lower margin of error is higher.

Legalization of marijuana, Part I. (6.10, p. 216) The 2010 General Social Survey asked 1,259 US residents: “Do you think the use of marijuana should be made legal, or not” 48% of the respondents said it should be made legal.

  1. Is 48% a sample statistic or a population parameter? Explain.
  2. Construct a 95% confidence interval for the proportion of US residents who think marijuana should be made legal, and interpret it in the context of the data.
  3. A critic points out that this 95% confidence interval is only accurate if the statistic follows a normal distribution, or if the normal model is a good approximation. Is this true for these data? Explain.
  4. A news piece on this survey’s findings states, “Majority of Americans think marijuana should be legalized.” Based on your confidence interval, is this news piece’s statement justified?

Answer(a)

It is a Sample statistic because it is derived from a Sample data and it is used to estimate the population parameter.

Answer (b)

n<-1259

p<-.48

se<-sqrt((p*(1-p))/n)

me<-1.96*se


c(p-me,p+me)
## [1] 0.4524028 0.5075972

Answer(c)

Yes this is true and the conditions for inference satisfy then that will be the case for this sample.

Answer(d)

No it is not justified as that is not what our confidence interval gives us, In fact based on our confidence interval we see less than 50% support this.

Legalize Marijuana, Part II. (6.16, p. 216) As discussed in Exercise above, the 2010 General Social Survey reported a sample where about 48% of US residents thought marijuana should be made legal. If we wanted to limit the margin of error of a 95% confidence interval to 2%, about how many Americans would we need to survey?

Answer

p <- 0.48
Margin_Of_Error <- 0.02
Standard_Error <- Margin_Of_Error / 1.96
n <- (p * (1-p)) / (Standard_Error^2) 
The total number we would need to survey is 2397.1584

Sleep deprivation, CA vs. OR, Part I. (6.22, p. 226) According to a report on sleep deprivation by the Centers for Disease Control and Prevention, the proportion of California residents who reported insuffient rest or sleep during each of the preceding 30 days is 8.0%, while this proportion is 8.8% for Oregon residents. These data are based on simple random samples of 11,545 California and 4,691 Oregon residents. Calculate a 95% confidence interval for the difference between the proportions of Californians and Oregonians who are sleep deprived and interpret it in context of the data.

Answer

p_california <- 0.08
p_oregon <- 0.088

p <- p_california - p_oregon

n_california <- 11545
n_oregon <- 4691

se_california <- (p_california * (1-p_california)) / n_california
se_oregon <- (p_oregon * (1-p_oregon)) / n_oregon

se <- sqrt(se_california + se_oregon )
margin_of_error <- 1.96 * se
margin_of_error
## [1] 0.009498128
upper=p+margin_of_error

lower=p-margin_of_error

c(lower,upper)
## [1] -0.017498128  0.001498128

It looks like that the confidence interval contains the value of 0 and the hypothesis failed and doesnt seem like there is a significant difference in sleep deprivation between the two.

Barking deer. (6.34, p. 239) Microhabitat factors associated with forage and bed sites of barking deer in Hainan Island, China were examined from 2001 to 2002. In this region woods make up 4.8% of the land, cultivated grass plot makes up 14.7% and deciduous forests makes up 39.6%. Of the 426 sites where the deer forage, 4 were categorized as woods, 16 as cultivated grassplot, and 61 as deciduous forests. The table below summarizes these data.

  1. Write the hypotheses for testing if barking deer prefer to forage in certain habitats over others.
  2. What type of test can we use to answer this research question?
  3. Check if the assumptions and conditions required for this test are satisfied.
  4. Do these data provide convincing evidence that barking deer pre- fer to forage in certain habitats over others? Conduct an appro- priate hypothesis test to answer this research question.

Coffee and Depression. (6.50, p. 248) Researchers conducted a study investigating the relationship between caffeinated coffee consumption and risk of depression in women. They collected data on 50,739 women free of depression symptoms at the start of the study in the year 1996, and these women were followed through 2006. The researchers used questionnaires to collect data on caffeinated coffee consumption, asked each individual about physician-diagnosed depression, and also asked about the use of antidepressants. The table below shows the distribution of incidences of depression by amount of caffeinated coffee consumption.

  1. What type of test is appropriate for evaluating if there is an association between coffee intake and depression?
  2. Write the hypotheses for the test you identified in part (a).
  3. Calculate the overall proportion of women who do and do not suffer from depression.
  4. Identify the expected count for the highlighted cell, and calculate the contribution of this cell to the test statistic, i.e. (\(Observed - Expected)^2 / Expected\)).
  5. The test statistic is \(\chi^2=20.93\). What is the p-value?
  6. What is the conclusion of the hypothesis test?
  7. One of the authors of this study was quoted on the NYTimes as saying it was “too early to recommend that women load up on extra coffee” based on just this study. Do you agree with this statement? Explain your reasoning.