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STATISTICS 14

FinalExam

FinalExam

Essay#1

Researchersconducted an investigation to establish the appropriate vaccine whichis deemed successful in the prevention and treatment of influenza. Inthe experiment, two types of vaccines were examined, a nasal sprayand shot. In the experiment, 1000 participants were randomly selectedto participate in the test to determine the effectiveness of the fluvaccine. Furthermore, the 1000 participants were grouped into twogroups each with 500 participants per group. One group consisting of500 participants were given the vaccine through shots while thesecond group was received the vaccine through nasal spray.

Fromthe 500 participants who got the vaccine through the shot, 420participants did not catch flu while eighty participants got the fluinfection. Whereas from the 500 participants who received the vaccinethrough the nasal spray, one hundred and twenty participants gotinfected with influenza while three hundred and eighty did not getinfected with influenza. The set the significance level was set at0.5. The ratio of the clinical trial volunteers whom the shotvaccines worked and those who caught flu equaled to 0.16. Similarly,the ratio of participants whom the nasal spray vaccine worked was0.24 (Aron,2013).The computed p-Value was 0.0008. The hypothesis sought to find outthe successful and effective kind of vaccine (nasal spray or shot).The focus was on the participants who did not catch the flu duringthe study.

Thenull hypothesis assumes that the two vaccine treatments that aresimilar. From the study results, the vaccine administered throughinjection was the most effective. The null hypothesis, in this case,would be that the treatments being equated are similar. The nullhypothesis was rejected by the researchers meaning there was astatistically significant difference. By the p-Value which is below.05 as well as the upper-confidence bound of 95% which fails toinclude a zero, the null hypothesis is rejected. A low p-Value meansthe possibility of getting the value obtained is particularly minimalleading to rejection of the null hypothesis (Aron,2013).Furthermore, the data provided does not support the alternativehypothesis since there is no supporting evidence to prove the claimthat vaccine prevents the flu. Both types of vaccines fail to preventthe flu.

Thestudy findings prove to be statistically significant since they hadthe lower p-Value. The research sample especially the number ofparticipants can be considered as suitable. The objectivity of thestudy is enhanced when the research includes more participants henceit leads to conclusive answers. People are usually hesitant toparticipate in new experiments. The nasal spray is highly preferredas people fear injections despite it being less effective (Aron,2013). A follow-up study should involve similar participants but shouldswitch how the vaccines are administered to determine how people areresponsive to both forms of vaccines. Participants can be surveyed todetermine the preference and the side effects they experienced.

Thelikely study limitations include: the nasal vaccine is a newtreatment, and the study findings prove that the data fail to provethat the shot vaccine is more effective as compared to the nasalspray. Additionally, there lacks information in the research likeage-ranges, current and past health conditions among others (Aron,2013).Such information should have included in supporting inferences sincechildren and the elderly are susceptible to germs and sicknessrespectively. Despite the number of participants, additionalvariables must be included to ensure accurate results. Additionally,there is need to conduct the study during a flu season or epidemic toensure reliable results are obtained.

Essay#2

Thetask seeks to establish the existing relationship between grade pointaverages (GPA) and IQ. There are several applications used in thestudy relationships. A correlation defines the level of therelationship that exists between two dissimilar variables. There canbe a positive and negative correlation between the two variables(Aron,2013). There is a positive correlation between the GPA and IQ when anindividual has a high IQ, it is likely that his/her GPA willrelatively increase. Nonetheless, it must be assumed that certainfactors attributed to an increase in a person’s GPA such extratuition, additional mentoring, or accessing more resources are notinvolved.

Asdiscussed, the researchers established that the correlation betweenGPA and IQ is .75. This means there is a 75% chance that the twovariables correlate. When the study determines that the correlationlies between 0.7 and 1, one can confidently conclude there is astrong correlation. However, the relationship between the GPA and IQis very strong. Additionally, it can be concluded that as the IQlevel increases the GPA also increases. The researchers mustunderstand that correlation is not comparable to causation(Gravetter,2016).Causation relationship shows that a single event is the outcome ofthe occurrence of another event. The phenomenon can be called thecause and effect. Despite GPA and IQ being positively correlated, itdoes not mean that when a person has a higher IQ does not translateto a higher IQ.

Aperson with a lower IQ can work harder in their studies to increasetheir GPA. The causative effect ensures that when a person IQdecreases or increases the GPA increases or decreasesproportionately. In most cases when an individual’s IQ rises ordrops, similarly the GPA increases or plummets. One of the studylimitations includes the point that the data did not consider suchvariables like past academic achievements, gender groups, and agegroups (Aron,2013).Some tests like ANOVA would be more appropriate if such variables areconsidered. An analyst can use a scatterplot to illustrate thecorrelation.

Essay#3

Aresearcher recorded the outcomes of a memory assessment test whichcovered twenty subjects. There is need to organize the informationgiven into meaningful sets, interpreting the results, calculatingdescriptive statistics, changes in variables, as well as theassessment of the outliers (Aron,2013).The data was analyzed using the calculator soup which provided themean, standard deviation, mode, total, kurtosis, median, skew, andrange. The first step will involve organizing the data intomeaningful sets. The data will also be grouped into two diverse setsnamely even number as well as odd numbered groups.

Evennumber group: 2.2, 2.5, 2.7, 2.9, 3.1, 3.5, 4.1, 4.3, 4.7, and 4.8Total = 34.8

Oddnumbered set: 7.3, 7.6, 8.1, 8.2, 8.5, 9.2, 9.3, 9.5, 9.5, and 15.2. Total = 92.4

Thetwo sets show several differences for instance, in the second setonly one number is a mode (9.5) while in the first set, all thenumbers act as the mode.

Secondly,there is need to calculate the sum, standard deviation, mean, skew,sum, kurtosis, range, and median for each set using the CalculatorSoup, a statistic calculator.

Calculationof the set of numbers (Table 1)

Even Numbered Group (10)

Odd Numbered Group (10)

Minimum:

2.2

7.3

Maximum:

4.8

15.2

Range:

2.6

7.9

Count:

10

10

Sum:

34.8

92.4

Mean:

3.48

9.24

Median:

3.3

8.85

Mode:

2.2, 2.5, 2.7, 2.9, 3.1, 3.5, 4.1, 4.3, 4.7, 4.8

9.5

Standard Deviation:

0.9414

2.237

Variance:

0.8862

5.005

Mid-Range:

3.5

11.25

Quartiles:

Quartiles:

Quartiles:

&nbsp

Q1&nbsp–&gt 2.7

Q1&nbsp–&gt 8.1

&nbsp

Q2&nbsp–&gt 3.3

Q2&nbsp–&gt 8.85

&nbsp

Q3&nbsp–&gt 4.3

Q3&nbsp–&gt 9.5

Interquartile Range (IQR):

1.6

1.4

Sum of Squares:

7.976

45.04

Mean Absolute Deviation:

0.8

1.308

Root Mean Square (RMS):

3.593

9.481

Std Error of Mean:

0.2977

0.7075

Skewness:

0.1522

1.955

Kurtosis:

1.409

5.706

Coefficient of Variation:

0.2705

0.2421

Relative Standard Deviation:

27.05%

24.21%

Thereafter,two sets of numbers are doubled and calculated afresh using theCalculator Soup. (Table 2)

Even Numbered Group (20)

Odd Numbered Group (20)

Minimum:

2.2

7.3

Maximum:

4.8

15.2

Range:

2.6

7.9

Count:

20

20

Sum:

69.6

184.8

Mean:

3.48

9.24

Median:

3.3

8.85

Mode:

2.2, 2.5, 2.7, 2.9, 3.1, 3.5,4.1, 4.3, 4.7, 4.8

9.5

Standard Deviation:

0.9163

2.177

Variance:

0.8396

4.741

Mid-Range:

3.5

11.25

Quartiles:

Quartiles:

Quartiles:

&nbsp

Q1&nbsp–&gt 2.7

Q1&nbsp–&gt 8.1

&nbsp

Q2&nbsp–&gt 3.3

Q2&nbsp–&gt 8.85

&nbsp

Q3&nbsp–&gt 4.3

Q3&nbsp–&gt 9.5

Interquartile Range (IQR):

1.6

1.4

Sum of Squares:

15.95

90.09

Mean Absolute Deviation:

0.8

1.308

Root Mean Square (RMS):

3.593

9.481

Standard Error of Mean:

0.2049

0.4869

Skewness:

0.1563

2.009

Kurtosis:

1.488

6.023

Coefficient of Variation:

0.2633

0.2357

Relative Standard Deviation:

26.33%

23.57%

Table1 shows calculations of the sets with ten numbers while Table 2 showscomputations with 20 numbers.

Anotable difference that can be seen shows there is a repetition ofone set of numbers in the odd-numbered set. Additionally, number 15.2is considered as an outlier number since other numbers are below 9.5.The outlier has a great influence since it makes the otherdescriptive statistics to be larger (Howell,2016).While assessing the results of the calculation, there is a hugedifference between results when using the twenty and ten data sets.The sample size of data impacts some final values. For instance, inthe odd numbered set, the mean, median, range, and mode wasunchanged. Nonetheless, the sum, skew, kurtosis, and standarddeviation increased after the numbers were doubled (Gravetter,2016).Similarly, in the even numbered set some differences are clearlyevident after the numbers were doubled.

PartII: Research Study Critique

MindiManes, Mark Dworkin, and Li Liu (2013) from University of Illinois’School of Public Health conducted a baseline knowledge survey toanalyze the knowledge level of the food handlers in many restaurantslocated in Chicago. The study sought to determine their level ofawareness of keeping the food uncontaminated and safe (Manes,2013).The article states that in the United States, millions of peoplesuffer from foodborne disease leading to many deaths. There is needto reduce the levels of food poisoning hence there is need to buildthe capacity of the restaurant food handlers. The handlers must haveperfect knowledge of the principles of food safety. Such ideal foodsafety behavior will help protect people against diseases and savelives. The objective of the research was to determine the levels ofknowledge gap related to food safety amongst food handlers inChicago’s many restaurants.

Thestudy employed cross-sectional survey to collect data from sevenhundred and twenty nine food handlers from two hundred and elevenrestaurants in Chicago. The research was done from mid-2009 to early2010. Additionally, a questionnaire containing 50-questions wasadministered both in Spanish and English. The researcher used amixed-effects regression analysis to establish the factors which arerelated to food safety. The knowledge score had a mean of 72% thefindings revealed there are considerable knowledge gaps on foodstorage, cooking, food handling, and cross-contamination (Manes,2013).The food handlers who speak the Spanish language scored considerablylower as compared to the food handlers who speak English language (p&lt .05).

Thecertified food managers scored considerably higher (79%) as comparedto the non-certified food handlers. Such data can be used foreducational initiatives to reduce such knowledge gaps especiallyamong the cooks with an aim of reducing or stopping food poisoning.Food contamination can be caused by poor handling of food, poorhygiene, cross contamination, and setting improper temperatures whilecooking food.

Methods

Theresearchers compiled a list of food establishments, a total of 2,087facilities in numerous counties in Chicago. The list was reduced toeliminate caterers and banquet halls. After random sampling, nearly32% or 668 restaurants were selected for the study. The restaurantmanagers were requested to allow the researchers interview employeesat their premises. Later, after approval was granted, the50-questionnaire survey was administered to collect baselineinformation related to the restaurants, personal hygiene, foodhandlers’ behaviors, and the food handlers’ knowledge. Afterpretest interview was done, the last survey instrument wasintroduced. The survey questionnaire had 40 questions related to foodsafety knowledge which included fill-in-the-blank questions,true/false questions, and multiple choices.

Theknowledge tested included determining if or not the food handlerswere aware of the optimal temperatures that inhibit bacterial growth,the schedule of disposing food, suitable cooling and heatingtemperatures, and their cross-contamination knowledge.

Additionally,the trained interviewers inquired the cooks’ personal hygiene andbeing sick at work. Demographic information was gathered includingtheir age, ethnicity/race, certification, food safety training,frequency of performing certain food handling tasks, and their yearsof experience (Manes,2013).

Therestaurant characteristics such as average entrée price, food type,and service style were collected. The restaurants were classified bytheir size: large (≥30 seating or table ≥120 seats) medium(11–29 seating or table 41–119 seats) and small (≤10 seatingor table ≤40 seats). The research used the Windows SAS 9.2 forstatistical analysis. Data from food handlers was analyzed using thesystem that is, 722 out of the 729 of them representing a 99% of thefood handlers was analyzed (Manes,2013).The researchers matched the mean knowledge scores using the t-Tests.Computation of Pearson correlation coefficients was done to definethe link between the continuous variables and the knowledge score.The researcher performed the multivariate analysis to establish therisk factors related to knowledge score of the food handlers (Manes,2013).The mixed-effects regression models were employed to perform themultivariate analysis and predict the knowledge score. Lastly, totake into consideration the likely correlations between food handlersfrom similar restaurant, the researcher used the random restauranteffect (Manes,2013).

Results

Theresults were relatively shocking. The food handlers spent nearly 9.9years as their average time spent. A positive correlation wasidentified between the average food handler’s age and theirexperience (r2=.73, p &lt.0001). Sadly, majority of food handlershave never attended training on food safety. Almost 285 restaurantsor 39% provided no training to their employees on food handling,storage, regulating temperature, or bacterial infection.Additionally, majority of the food handlers were not certifiedespecially those with less work experience those with &lt1 and 1 -3 years’ experience represented 40% 3-6 years’ experiencerepresented 60% 6-10 years were 73% lastly, &gt10 yearsrepresented 80%. In addition, the food handlers performed severaltasks related to food handling. 68% handled poultry or raw meat 42%handled sea food 90% handled fruits and vegetables while, 46%handled raw eggs. Besides, 56% cooked poultry or meat 40% cookedeggs while, 37% cooked seafood.

Discussion

Thestudy was successful done hence the results were accurate anddetailed enhancing the validity and credibility of the results. Themajor strengths include the high response rate and the surveyquestionnaire allowed collection of accurate data (Manes,2013).There is a probability that some employees gave false informationespecially their participation in food handling courses. Nonetheless,the research provided valuable insight on the state of food handling.

Conclusion

Theresearch creates awareness especially on such sensitive topic on foodpoisoning to reduce mortality and morbidity rates. The survey showsthere is a huge knowledge gap on food handling in most restaurants inChicago. The findings must be adopted and generalized across thecountry to create awareness and inspire action to ensure food safety.Restaurant owners must invest in training among other safety measuresto improve the levels of food safety and hire only certified foodhandler. The government must enforce the necessary laws to ensure therestaurant owners and food handlers comply with the public healthlaws.

References

Aron,A., Coups, E., &amp Aron, E. N. (2013).&nbspStatisticsfor The Behavioral and Social Sciences: Pearson New InternationalEdition: A Brief Course.Pearson Higher Ed.

Gravetter,F. J., &amp Wallnau, L. B. (2016).&nbspStatisticsfor the behavioral sciences.Cengage Learning.

Howell,D. C. (2016).&nbspFundamentalstatistics for the behavioral sciences.Nelson Education.

Manes,M. R., Liu, L. C., &amp Dworkin, M. S. (2013). Baseline knowledgesurvey of restaurant food handlers in suburban Chicago: dorestaurant food handlers know what they need to know to keepconsumers safe? Journalof environmental health,&nbsp76(1),18.