Question 13 70% of all Americans live in cities with population greater than 100,000 people. If 39 Americans are randomly selected, find the probability that a. Exactly 28 of them live in cities with population greater than 100,000 people. b. At most 27 of them live in cities with population greater than 100,000 people. C. At least 29 of them live in cities with population greater than 100,000 people. d. Between 25 and 33 (including 25 and 33) of them live in cities with population greater than 100,000 people. Check AnswerProgress saved Question 15 Find the area of the shaded region under the standard normal distribution to the right of the given z-score. Round your answer to four decimal places. - 2 z = -0.69 z P(z > - 0.69)= Check AnswerQuestion 25 It takes an average of 11.7 minutes for blood to begin clotting after an injury. An EMT wants to see if the average will change if the patient is immediately told the truth about the injury. The EMT randomly selected 69 injured patients to immediately tell the truth about the injury and noticed that they averaged 12.9 minutes for their blood to begin clotting after their injury. Their standard deviation was 4.07 minutes. What can be concluded at the the a = 0.05 level of significance? a. For this study, we should use | Select an answer b. The null and alternative hypotheses would be: tor Ho: 7 Select an answer v H1: 7 Select an answer v c. The test statistic |? v = (please show your answer to 3 decimal places.) d. The p-value - (Please show your answer to 4'decimal places.) e. The p-value is ? v a f. Based on this, we should | Select an answer v | the null hypothesis. g. Thus, the final conclusion is that ... The data suggest that the population mean is not significantly different from 11.7 at a = 0.05, so there is statistically insignificant evidence to conclude that the population mean time for blood to begin clotting after an injury if the patient is told the truth immediately is different from 11.7- The data suggest the populaton mean is significantly different from 11.7 at a = 0.05, so there is statistically significant evidence to conclude that the population mean time for blood to begin clotting after an injury if the patient is told the truth immediately is different from 11.7. The data suggest the population mean is not significantly different from 11.7 at a = 0.05, so there is statistically significant evidence to conclude that the population mean time for blood to begin clotting after an injury if the patient is told the truth immediately is equal to 11.7.A study was done to look at the relationship between number of vacation days employees take each year and the number of sick days they take each year. The results of the survey are shown below. Vacation Days 0 3 8 6 6 4 10 7 9 7 Sick Days 8 10 3 3 6 9 3 4 5 3 a. Find the correlation coefficient: T = Round to 2 decimal places. b. The null and alternative hypotheses for correlation are: Ho: 20 -0 H1: 2 0 The p-value is: (Round to four decimal places) . Use a level of significance of a = 0.05 to state the conclusion of the hypothesis test in the context of the study. There is statistically insignificant evidence to conclude that there is a correlation between the number of vacation days taken and the number of sick days taken. Thus, the use of the regression line is not appropriate. There is statistically significant evidence to conclude that an employee who takes more vacation days will take more sick days than an employee who takes fewer vacation days. There is statistically significant evidence to conclude that an employee who takes more vacation days will take fewer sick days than an employee who takes fewer vacation days There is statistically significant evidence to conclude that there is a correlation between the number of vacation days taken and the number of sick days taken. Thus, the regression line is useful. d. (Round to two decimal places) e. Interpret r- 59% of all employees will take the average number of sick days. There is a 59% chance that the regression line will be a good predictor for the number of sick days taken based on the number of vacation days taken. There is a large variation in the number of sick days employees take, but if you only look at employees who take a fixed number of vacation days, this variation on average is reduced by 59%. Given any group with a fixed number of vacation days taken, 59% of all of those employees will take the predicted number of sick days. The equation of the linear regression line is: (Please show your answers to two decimal places) g. Use the model to predict the number of sick days taken for an employee who took 2 vacation days this year. Sick Days - (Please round your answer to the nearest whole number.) h. Interpret the slope of the regression line in the context of the question: As x goes up, y goes down. The slope has no practical meaning since a negative number cannot occur with vacation days and sick days. For every additional vacation day taken, employees tend to take on average 0.70 fewer sick days. Interpret the y-intercept in the context of the question: The average number of sick days is predicted to be 10. If an employee takes no vacation days, then that employee will take 10 sick days. The y-intercept has no practical meaning for this study. The best prediction for an employee who doesn't take any vacation days is that the employee will take 10 sick days.Question 30 Listed below are paired data consisting of amounts spent on advertising (in millions of dollars) and the profits (in millions of dollars). Determine if there is a significant linear correlation between advertising cost and profit . Use a significance level of 0.05 and round all values to 4 decimal places. Advertising Cost Profit 3 27 24 5 18 6 24 7 23 8 25 9 21 10 30 11 25 12 25 Ho: p = 0 Ha: p = 0 Find the Linear Correlation Coefficient Find the p-value p-value = The p-value is Less than (or equal to) a Greater than a The p-value leads to a decision to Reject Ho Accept Ho O Do Not Reject Ho The conclusion is There is a significant positive linear correlation between advertising expense and profit. There is a significant linear correlation between advertising expense and profit. There is insufficient evidence to make a conclusion about the linear correlation between advertising expense and profit. There is a significant negative linear correlation between advertising expense and profit