Course project week 3

Introduction

As a health care professional, this study works to improve and maintain families, and communities in various settings. To understand current problem and its solution this study uses statistical tools to analyze results. The objective of this project is the application of basic statistical tools to a fictional scenario in order to impact the health and wellbeing of the clients being served.

1. Scenario/Problem

You are currently working at NCLEX Memorial Hospital in the Infectious Diseases Unit. Over the past few days, you have noticed an increase in patients admitted with a particular infectious disease. It is believed that the ages of these patients play a critical role in the method used to treat the patients. We have decide to speak to manager and work together to use statistical analysis to look more closely at the ages of these patients. You do some research and put together a spreadsheet of the data that contains the following information:

• Client number

• Infection Disease Status

• Age of the patient

To treat patients, there is a need the preliminary findings immediately so that treatment of patient can be started.

1. Data and Variables

The data set consists of 60 patients that have the infectious disease with ages ranging from 35 years of age to 76 years of age for NCLEX Memorial Hospital.

Patient number is a quantitative and discrete variable. Infectious disease is qualitative variable because it contains answer yes or no, not inn digits. It shows that all patients in the data set have infectious disease. Age is quantitative and continuous variable because it is continuously increasing. This study uses dummy for infectious disease variable to convert it into quantitative.

1. Data Analysis Techniques

This study uses descriptive statistics as data analysis techniques including Mean, Median, Mode, Midrange, Range, Variance and Standard Deviation of 60 patients facing infectious disease from age 35 to 76 years. Data is taken in compatible form. The average value measures the central location. The difference between maximum and minimum value is known as range. Standard deviation measures the variations from the mean value. Median is the value in the middle while the value repeating most often is known as mode. Midrange is the sum of highest vale and lowest value divided by 2.

1. Results Discussion

Following table shows descriptive statistics of all variables. Table covers data of 60 patients between age of 35 and 76. For infectious disease, this study uses dummy so that data is converted into quantitative type and D1 is kept equal to 1 for yes as all patients are facing infectious disease. D2 is used as this disease is inherited or not. If inherited, D2=1, if not D2 equals to 0.

Descriptive states of patient number and infectious disease are meaningless because all patients have infectious disease and number of patient doesn’t give any result or conclusion. Mean age is 61.82 with a standard deviation of 8.92 and variance 79.64. Median age is 61.50 and mode is 69. Maximum age is 76 while minimum age is 35 with a range of 41 and mid-range of 55.5. Mean value of D2 is 0.30 with variance of 0.21 and standard deviation of 0.46. Range is 1 and midrange is 0.50. It means that 30% of patients have inherited disease which is not so much authentic to say that infectious disease is because of family problem. Rather it also supports that infectious disease can’t be inherited. It is because of some germs attacking out body.

1. Conclusion

The objective of this project is the application of basic statistical tools to a fictional scenario in order to impact the health and wellbeing of the clients being served. This study uses data set consists of 60 patients that have the infectious disease with ages ranging from 35 years of age to 76 years of age for NCLEX Memorial Hospital. Patient number is a quantitative and discrete variable. Infectious disease is qualitative variable Age is quantitative and continuous variable. This study uses descriptive statistics to analyze data. The results show that infectious disease is very high among people of age above 34 and below 76. Result also concludes that ages of these patients play a critical role in the method used to treat the patients.

1. Confidence interval

1. Confidence interval is a range which is used to provide an estimate of population mean. It is believed that the population mean would be from that range.

2. Point estimate is the value which we derive from a sample to give an estimate about the population.

3. The best point estimate is an estimate about the population mean. We derive this by finding the mean of the sample as a sample represents a population and there for its mean gives the best point estimator of population mean.

4. Normally samples represent a population. In theory it should be the same as a population but in reality the sample may have higher values and may not contain lower values and vice versa thus we don’t get the best point estimators of the population. To cater to this problem we use confidence interval by which we calculate a range in which the population mean is assumed to be present.

2. Best Point estimate of population Mean

BPE = sum of all ages / number or patients

BPE = 3709 / 60

BPE = 62 (61.82 round up)

3. Confidence interval

Confidence interval = 61.82 (+-) Z0.05/2 (S/ ((n)^(1/2))

= 61.82 (+-) 1.96 ( 8.92/ (601/2 )

= 61.82 - 2.257 , 61.82 + 2.257

= 59.56 > X > 64.077

4. Interpretation of confidence interval

In the research we were looking at the significance that old people tend to have particular infectious disease. We constructed a confidence interval to have an idea were the population mean lies. By constructing a confidence interval we have a point estimate of population mean. 95% of the samples would have a mean within the above range which is between 59.56 > age > 64.077.

5. Calculating confidence interval at 99%

61.82 (+-) 2.5758 (8.92/ (601/2)

= 58.85 > age > 64.79

1. Yes I did notice change in the interval as the range has increased as we can see in the calculation above.

2. Now the range for the point estimate has increase and as it at 99% confidence level we can say that its more accurate as 99 sample means would be within this sample. Whereas in 95% confidence we had 95 sample mean in that interval.