# mthuringer module02courseprojectphase2 04072020 1

Option 2

Introduction:

As an existing healthcare professional for the NCLEX Memorial Hospital, it is

important to analyze data for the Infectious Disease Unit. There has been a steady increase in the

number of cases for patients that are being admitted for an infectious disease. Research has been

conducted to review the statistics on these cases. The data collected from 60 patients between 35

and 76 years of age includes the Client number, Infectious Disease Status and the age of the

patient. After evaluating and understanding the discovery of the current problem, I have

concluded that the age of the patients plays a major role in the use of methods used to help treat

the patients for the infectious disease.

The ages shown in the data are quantitative. The column on the ages provides information in

which the count, or quantity of the characteristics is the most important. For example, we are

interested in the total number of years in each one of these. This type of variable is known as

numerical or quantitative. The ages can be viewed also as discrete. A discrete numerical variable

can only have values at specific values. For example, the number of ages must be a whole

number. It is possible for a variable to be a fractional value and still be discrete. These ages can

also be viewed as continuous data because they vary for every person being measured. The

patient numbers are discrete meaning they go up uniformly. The mode is defined as the most

frequently occuring number in a set of data. The median is the middle number in the set of data.

The average is a measure of center that statisticians call the mean. Because the numerical

balancing point for the data is in an extremely important measure of center that is the basis for

many other calculations and processes which are necessary for making useful conclusions about

a set of data. The midrange is found by taking the mean of the max and min values of the data

set. A weighted mean involves multiplying individual data values by their frequencies or

percentages before adding them and then dividing by the total of the weights. The range is

simply the difference between the min and max in the data; this is able to give us a snapshot of

how the data is spread. The interquartile range is the difference between the quartiles and tells us

how widely spread the entire data set is. The standard deviation is an extremely important

measure of spread that is based on the mean.

The measures of variation include the range, interquartile range, standard deviation, variance,

square of the standard deviation, sum. These are important because one needs to be able to

understand how the degree to which data values are spread out in a distribution can be assessed

using simple measures to best represent the variability in the data. Also, the measures of

variation are statistics that indicate the degree to which numerical data tend to spread about an

average value. It is also called dispersion, scatter, spread. The measures of center include the

mode, median, mean, midrange, and the weighted mean. These are important because they are

some of the most important descriptive statistics you can get. In our society, we always want to

know the “average” of everything: the average age, average number, average speed, etc. etc. It

helps give us an idea of what the “most” common, normal, or representative answers might be.

Data:

– Mean=62.54

–

Median=64

–

Mode=69 and 71

–

Max=81

–

Min=35

–

Range=Max-Min=46

–

Midrange=(Max+Min)/2=58

–

Var=85.69

–

Standard deviation=9.257

Analysis

Any person who is suffering from the illness will have an average of 62.54 years. People

suffering this illness must have their years lie between 35-76 years. This tells us that in that

population only that age is affected. Standard deviation and variance tells us how these ages are

varying from the mean of 62.54.

Conclusion

Overall, when examining a set of data one must use descriptive statistics in order to provide

information about where the data is centered. The mode is a measure of the most frequently

occuring number in a data set and is the most useful for categorical data and the data being

measured at the nominal level. The mean and the median are two of the most commonly used

measures of center. The mean or the average is the sum of the data points which is divided by the

total number of data points in the set. All in all, looking at the information we found we were

able to find the median and the standard deviation as well as many other things all in which are

very important in a data set such as this. In conclusion, age really does have an effect on the

disease’s overall treatment and recovery.

Confidence intervals are important to approximate the mean of the population, addressing the

problem of how well the sample statistics estimates the underlying population value. All in all,

the population intervals provide the range of values which are likely to contain the population

parameter of interest. Mean is calculated as the total sum divided by the count, in this case would

be 3709 divided by 60 = 61.82. The standard deviation of the data is 8.92. This sample mean is

equal to 8.92/square of 60= 0.0025. The critical value for a 95 percent confidence interval is

1.96, where (1-0.95)/2=0.025. A 95% confidence interval for hte mean is [(61.82-(1.96*0.0025)],

[(61.82+ (1.96 * 0.0025))]= (61.8151,61.8249). As the level of confidence decreases, the size of

the corresponding interval also decreases. As for 99% confidence interval the critical value for

this is 2.576, where (1-0.99)/2= 0.005. A 99% confidence interval for the mean would be

[(61.82-(2.576*0.0025)],[(61.82+ (2.576* 0.0025))] = (61.8356 , 61.83134). In this case an

increase in sample size would therefore decrease the length of the confidence interval with no

reduction in the level of confidence.

In comparison between the 95% and 99%, the 95% confidence interval is almost correct while

the other is less correct. The 99% percent confidence interval is wider than the 95 percent

confidence interval. This is due to the fact that it allows one to be more precise that the unknown

population parameter is within the interval brackets. When defining confidence intervals, the

higher your confidence level is, the wider your confidence interval range will become. Due to an

increase in confidence level the chance of the population mean to fall within the range is high.

Although, the confidence level doesn’t stand for accuracy in estimate, it does mean that the

higher the confidence level less is the accuracy.

References

Biau, D. J. (2011, September). In brief: Standard deviation and standard error. Retrieved from

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3148365/

Jaykaran. (2010, October). “Mean ± SEM” or “Mean (SD)”? Retrieved from

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2959222/

Lee, D. K., In, J., & Lee, S. (2015, June). Standard deviation and standard error of the mean.

Retrieved from https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4452664/

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