Cigarette Smoking A researcher found that a cigarette smoker smokes on average 30 cigarettes a day. She feels that this average is too low. She selected a random sample of 8 smokers and found that the mean number of cigarettes they smoked per day was 32 . The sample standard deviation was 2.8 . At \( \alpha=0.05 \), is there enough evidence to support her claim? Assume that the population is approximately normally distributed. Use the critical value method and tables. Part: \( \mathbf{0} / \mathbf{5} \) Part \( \mathbf{1} \) of 5 (a) State the hypotheses and identify the claim.

\( n = 5 \quad p ( \overline { x } > 500 ) \)

Many investors and financial analysts believe the Dow Jones Industrial Average (DJIA) gives a good barometer of the overall stock market. On January 31, 2006, 9 of the 30 stocks making up the DJIA increased in price (The Wall Street Journal, February 1, 2006). On the basis of this fact, a financial analyst claims we can assume that \( 30 \% \) of the stocks traded on the New York Stock Exchange (NYSE) went up the same day. A sample of 76 stocks traded on the NYSE that day showed that 10 went up. You are conducting a study to see if the proportion of stocks that went up is is significantly less than 0.3 . You use a significance level of \( \alpha=0.002 \). What is the test statistic for this sample? Use \( \hat{p} \) rounded to 4 decimal places. (Report answer accurate to 4 decimal places.) test statistic \( =-3.198^{\circ} \times \)

Find the mean of: \( 1.8,1.7,1.3,1.1,1.2,1.6,1.8 \) A. 1.6 B. 1.4 C. 1.8 D. 1.1 E. 1.5

Question 2 ( 0.25 points) Retake question Choose the correct interpretation of a p-value. If the test is right-tailed, the \( p \)-value is the probability of observing a sample proportion as small as we saw in our sample, if the null hypothesis were true. If the test is right-tailed, the \( p \)-value is the probability of observing a sample proportion as large as we saw in our sample, if the null hypothesis were true. If the test is right-tailed, the \( p \)-value is the probability of observing a sample proportion as large as we saw in our sample. If the test is two-tailed, the \( p \)-value is the probability of observing a sample proportion as far away from the null value as what was observed in the current data set.

Select all of the possible null hypotheses from the list below. \( p=0.37 \) \( p>0.84 \) \( p<0.42 \) \( p \neq 0.10 \)