Researchers examined the influence of a television program on children's aggressiveness. The number of aggressive responses was measured during an observation period after viewing the television program. Imagine that the known national average for number of aggressive responses typically performed by children who do not watch television is 6.847. Use the data below to conduct an analysis to determine if the number of aggressive responses on average is different from the national average. Number of aggressive responses: 10, 5, 10, 11, 12, 6, 11 A) Is the assumption(s) met? Please explain your answer and proceed with the analysis even if the assumption(s) is not met.  B)What do you conclude from your analysis?

The Centers for Disease Control (CDC) want to study the effects of lead exposure on children.  They randomly select 2500 children from Olympia, Washington.    What is the sample and the population?

The U.S. Department of Transportation, National Highway Traffic Safety Administration, reported that 77% of all fatally injured automobile drivers were intoxicated. A random sample of 52 records of automobile driver fatalities in a certain county showed that 35 involved an intoxicated driver. Do these data indicate that the population proportion of driver fatalities related to alcohol is less than 77% in Kit Carson County? Use ? = 0.10. What is the value of the sample test statistic? (Round your answer to two decimal places.)=__   (c)Find the P-value of the test statistic. (Round your answer to four decimal places.)=__

A contributor for the local newspaper is writing an article for the weekly fitness section. To prepare for the story, she conducts a study to compare the exercise habits of people who exercise in the morning to the exercise habits of people who work out in the afternoon or evening. She selects three different health centers from which to draw her samples. The 57 people she sampled who work out in the morning have a mean of 4.8 hours of exercise each week. The 56 people surveyed who exercise in the afternoon or evening have a mean of 4.2 hours of exercise each week. Assume that the weekly exercise times have a population standard deviation of 0.8 hours for people who exercise in the morning and 0.3 hours for people who exercise in the afternoon or evening. Let Population 1 be people who exercise in the morning and Population 2 be people who exercise in the afternoon or evening.   Step 1 of 2 :   Construct a 95% confidence interval for the true difference between the mean amounts…

Please also explain why you determined whether or not the results are statistically significant.  The mean score on a psychological characteristic for women is 25 (SD = 5) and the mean score for men is 24 (SD = 5). There were 12 women and 10 men in this study.

Find a real-life example of the use of ANOVA (Make sure to cite the source). Explain how and why they used ANOVA.

Biologists caught and measured 215 fish from the northern Atlantic Ocean. The lengths of the fish (in cm) are summarized in the histogram. 40 35 30 25 20 15 10 5 15 30 45 60 75 90 105 120 length of fish (cm) About what percentage of fish caught in this study had lengths between 45 cm and 75 cm? % Frequency

Warren wants to know if the amount of coffee that you drink is related to the number of hours that you stay awake. He recruits 50 participants using random sampling to report how much coffee they drank and the time of the day that they went to sleep. He then performs a correlation to determine the association between the amount of coffee that was consumed and the number of hours that they stayed awake. There was a statistically significant positive correlation between amount of coffee that participants drank and the number of hours that they stayed awake, r(49) = .82, p < .05, two-tailed, r2 = .08. This was a small effect – 8% of the variance in the amount of coffee drank is explained by hours that you stay awake (and vice versa).

A university would like to describe the relationship between the GPA and the starting monthly salary of a graduate who earned a business degree from the university. The accompanying table shows the monthly starting salaries for eight graduates of the business school along with their corresponding GPAS. Determine the sample correlation coefficient between the starting salary and the GPA of a business graduate of the university. Click the icon to view the salary and GPA data. Starting salary and GPA data (Round to three decimal places as needed.) Starting Salary GPA Starting Salary GPA 2,500 3.1 3,100 3.7 2,800 3.4 2,900 3.9 2,300 2.6 2,000 2.5 2,600 3.5 2,400 3.2 4o5

How Panel Data is useful to control some types of omitted variables without actually oberving them?