1. Using Stat Crunch, calculate the chi-square statistic and degrees of freedom for the following set of data for 300 people: Group A Group B Group C Total Had flu shot 20 30 32 82 Didn’t have flu shot 80 70 68 218 Total 100 100 100 300 Cell format Count (Percent with flu shot) (Percent of Group) (Percent of total) (Expected count) Got the Flu Shot? Group A Group B Group C Total Yes Percent with flu shot Percent with in group Percent of total Expected Count 20 (24.39%) (20%) (6.67%) (27.33) 30 (36.59%) (30%) (10%) (27.33) 32 (39.02%) (32%) (10.67%) (27.33) 82 (100%) (27.33%) (27.33%) No Percent without flu shot Percent with in group Percent of total …show more content…
Expected count 80 (36.7%) (80%) (26.67%) (72.67) 70 (32.11%) (70%) (23.33%) (72.67) 68 (31.19%) (68%) (22.67%) (72.67) 218 (100%) (72.67%) (72.67%) Total 100 (33.33%) (100%) (33.33%) 100 (33.33%) (100%) (33.33%) 100 (33.33%) (100%) (33.33%) 300 (100%) (100%) (100%) Statistic DF Value P-value Chi-square 2 4.1620049 0.1248 Is the value of the chi-square statistically significant at the 0.05 level?
No, the value of 4.1620049 was nonsignificant based on the level of significance for df2 which is 5.99. 2. Write a paragraph summarizing the results of the analysis in Exercise 1. In this analysis the null hypothesis is that the variable are independent, in other words whether or not a person has gotten their flu shot is unrelated to which group they are in. The Alternate hypothesis is that whether or not a person has gotten their flu shot is related to which group they were placed in, the variables are not independent. The results of this analysis are that the chi-square value is 4.1620049, which is nonsignificant according to the table on page 416 of the text which shows that the level of significance for 2 degrees of freedom is 5.99. The p value of 0.1248 is also indicative of a nonsignificant result. Based on the results of this analysis and the resulting significance the keep the null hypothesis. 3. Using StatCrunch, calculate the chi-square statistic and degrees of freedom for the following set of data for 180 people undergoing a knee replacement treatment with a drug
supplement: Treatment with drug X Treatment without Drug X Total Had > 8 wk rehab 18 32 50 Had < 8 wk rehab 70 60 130 Total 88 92 180 Cell format Count (Row percent) (Column percent) (Percent of total) (Expected count) Contingency table results: Rows: Length of rehab Columns: None Length of Rehab Treatment with drug X Treatment without drug X Total Had > 8 week rehab Percent within rehab length Percent within treatment group Percent of total Expected count 18 (36%) (20.45%) (10%) (24.44) 32 (64%) (34.78%) (17.78%) (25.56) 50 (100%) (27.78%) (27.78%) Had < 8 week rehab Percent within rehab length Percent within treatment group Percent of total Expected count 70 (53.85%) (79.55%) (38.89%) (63.56) 60 (46.15%) (65.22%) (33.33%) (66.44) 130 (100%) (72.22%) (72.22%) Total 88 (48.89%) (100%) (48.89%) 92 (51.11%) (100%) (51.11%) 180 (100%) (100%) (100%) Chi-Square test: Statistic DF Value P-value Chi-square 1 4.6026148 0.0319 Is the value of the chi-square statistically significant at the 0.05 level? Yes, the chi-square value of 4.6026148 is significant according to the level of significance for 1 degree if freedom, which is 3.84. Because the chi-square value in this analysis “beat the book” it is considered significant
Collected data were subjected to analysis of variance using the SAS (9.1, SAS institute, 2004) statistical software package. Statistical assessments of differences between mean values were performed by the LSD test at P = 0.05.
For this experiment the null hypothesis is that the intensity of the step rate test (High and Low) has no effect on the persons’ heart rate and recovery time. While the alternate hypothesis is that the intensity of the step rate test (High and Low) has an effect on the persons’ heart rate and recovery time.
The flu shot vaccine campaign for the Center of Disease Control consisted of several different posters promoting people to get the influenza vaccination shot. There was around 5 to 6 posters however 3 posters really seemed to stand out to me. " Spread popcorn, not the flu" "Flu shots aren't just for kids." And " My child won't get the flu" posters catered to three different target audiences.
The Chi-Square test of Independence examines the concept referred to as Cross Tabulation (Mirabella, 2011). The difference in the Chi-Square Goodness of Fit test determines if it is a fit to proportion, and the Cross Tabulation in the Independent test is going to determine if the two variables are related (Mirabella, 2011). When dealing with proportion, the sampling error and confidence level is the significant factor (Mirabella, 2011). However, this test is looking for the error, or a difference in the relationship between the two variables and will make a decision based on the significance level, and the P-value (Mirabella, 2011). Is there a relationship between gender and the major chosen? The question for this case is calculated in the Cross Tabulation to determine if one's choice of major is dependent on one's gender. Does the answer to the question depend on one's gender? The null hypothesis here is that one's choice of major is independent of gender, and the alternate choice is one’s choice of major is dependent on one's gender (Mirabella, 2011). There could be a dependent relationship between their gender in which major was chosen. If so, the Chi-Square Independent
Should the Flu Shot Be Mandatory? Vaccines have been proclaimed by many people as one of the miracles of modern medicine. Vaccines are credited with saving thousands of lives and wiping out many contagious diseases. Recently, there has been a tremendous debate whether annual influenza vaccines should be mandatory. Influenza vaccines should be voluntary because people have the right to examine data on vaccinations and make their own informed decisions.
In 1976, due to an outbreak of influenza at Fort Dix, New Jersey, the United States set a precedent in immunology by attempting to vaccinate the entire population of the country against the possibility of a swine-type Influenza A epidemic. While a great many people were successfully immunized in a very short period of time, the National Influenza Immunization Program (NIIP) quickly became recognized as a failure, one reason being that the feared epidemic never surfaced at all. But this massive undertaking deserves more analysis than just a simple repudiation. For example, all evidence linked to the pathology, microbiology, and historical cycle of influenza and the outbreak at Fort Dix suggests that the reactions of the scientists and other personnel involved in the NIIP were correct. However, one must also acknowledge the many complications and misjudgments that plagued the program after its initiation, from biological difficulties, logistical problems, to tensions with the media. The swine flu is a historical event that needs to be evaluated, regarding both its successes and its failures, so that lessons can be learned for future immunization programs.
..., M., Oort, F., & Sprangers, M. (2013). Significance, truth and proof of p values:
For this study ten participants were chosen to complete the study. For this particular study, the participants had to be the eldest and youngest child from the same family. They both also had to be raised in the same household. The pairs were picked at random and then asked to complete the test. There were three males tested and seven females tested.
Table 3.1. The question asked by the two surveys was slightly different (i.e., “top three” in
Influenza is a major public health problem which outbreaks all over the world. Resulting in considerable sickness and death rates. Furthermore, it is a highly infectious airborne disease and is caused by the influenza virus. Influenza is transmitted easily from one person to another person which has a great impact on society. When a member of society becomes sick, it is more prone to spread to other people. In the United States, every year between 5 to 20 percent of the population is affected by influenza. As a result of this, between 3,000 and 49,000 deaths have occurred per year (Biggerstaff et al., 2014). Therefore, the influenza vaccine is the most effective strategy to prevent influenza. This essay will examine two significant reasons for influenza vaccination which are the loss of workforce and economic burden as well as one effect regarding herd immunity.
Illnesses have long haunted the human race. As long as these illnesses have existed, humans have developed ways to cure themselves, beginning with simple herbs and proceeding as far as vaccines and complex medicines. One cure that long eluded scientists was that of the influenza virus. Now, the influenza vaccine, or flu shot, saves thousands of lives a year and helps prevent serious complications resulting from influenza infection.
The first table was titled Other Measures. It provided information on the sample size, minimum, maximum, first quartile, third quartile, given percentage, and value of percentile. These values are used to compute range and interquartile range in the measures of dispersion. The last table shows the mean plus or minus 1, 2, or 3 times the standard deviation and offers details on how many values fall within the ranges created by those calculations.
The study consisted of a significant number of females compared to males, which makes it invalid to conclude that the findings support the general population. A strength was that participants were selected at random. By doing so, the study remained unbiased, thus making the results more credible.
In the health care industry, gathering information in order to find the best diagnosis route or even determine patient satisfaction is necessary. This is complete by conducting a survey and collecting data. When the information is complete, we then have statistical information used to make administrative decision within the healthcare field. The collection of meaningful statistics is an important function of any hospital or clinic.
Table #1: Number of cases for particular side effect within observable group. Sample number is 40 and total number of participants is 120.