Recommended
Recommended
More Related Content
Similar to Phase 2Phase 2Lucia RuizRasmussen College.docx
Similar to Phase 2Phase 2Lucia RuizRasmussen College.docx (20)
More from mattjtoni51554
More from mattjtoni51554 (20)
Recently uploaded
Recently uploaded (20)
Phase 2Phase 2Lucia RuizRasmussen College.docx
- 1. Phase 2 Phase 2 Lucia Ruiz Rasmussen College Author Notes This paper is being submitted on February 26, 2017 for Juton Hemphill’s Inferential Statistics and Analytics course. In the statistical inference, the main aim is to estimate the populations’ parameters by the use of samples that have been drawn from that population. There is thus the need to create a confidence interval. It gives the range values that have a chance of including unknown parameters from the population from the sample drawn (2017). Simply this means that when drawing an inference from a sample taken we have the estimate of the population parameter and the margin of error which means there is a chance of inclusion of unknown population parameter, the estimated range being calculated from a given set of sample data (DG, 2017). The point estimate of a population parameter is a single value used to estimate the population parameter. For example, the
- 2. sample mean x is a point estimate of the population mean μ. Point estimation is defined as the process by which the estimation of parameters from a normal probability distribution that is based on the data that is observed from a particular population. The mean is the best point estimator. It is because the mean in a normal distribution is the average of the data set and normally the center of the data. It is where most of the items in the population lie and usually represent the data set. For all populations, the sample mean x is an unbiased estimator of the population mean µ, meaning that the distribution of sample means tends to center about the value of the population mean µ. For most populations, the distribution of sample means x is always more consistent (with less variation) than the distributions of other sample statistics. The confidence interval that is created from a range based on a confidence level and useful in the estimating the actual population values based on the normal distribution for a statistic of that population. It is useful to accommodate a range of estimates and reduce chances of avoiding of misinterpretation of non-significant results of small studies ("Point Estimates and Confidence Intervals", 2017). Since the best point estimator of the population means is the sample mean x is a point estimate of the population mean μ then for our data, then the Mean = 3705 / 60 = 61.81667 is the best. At 95% confidence level ) ) 59.26 < x < 64.38 At 99% confidence level ) ) 58.45 < x < 65.18
- 3. Interpretation: at 95% confidence level we estimate the populations mean to be 61.82. We are 95% confident that the true value of the mean lies between 59.26 < x < 64.38. From our study of the patients infected with the infectious disease in NCLEX Memorial Hospital is that at 95% confidence interval the mean lies between 59.26 < x < 64.38. On the other hand, at 99% confidence level, we estimate the populations mean to be 61.82. We are 95% confident that the true value of the mean lies between 58.45 < x < 65.18. From our study of the patients infected with the infectious disease in NCLEX Memorial Hospital is that at 99% confidence interval the mean lies between 58.45 < x < 65.18. Conclusion When the confidence level increases, the confidence interval increases. The increase of the confidence level from 95% to 995 leads to the range increase. A higher percent confidence level gives a wider band. The accommodation of a wide range in the interval leads to the chance of an error occurring form the interval of the means. Though there is more uncertainty, there is less chance of making an error but there is more uncertainty. Reference (2017). Retrieved 26 February 2017, from http://stattrek.com/statistics/dictionary.aspx?definition=confide nce_interval DG, A. (2017). Why we need confidence intervals. - PubMed - NCBI. Ncbi.nlm.nih.gov. Retrieved 26 February 2017, from https://www.ncbi.nlm.nih.gov/pubmed/15827844 Point Estimates and Confidence Intervals. (2017). Cliffsnotes.com. Retrieved 26 February 2017, from https://www.cliffsnotes.com/study-guides/statistics/principles- of-testing/point-estimates-and-confidence-intervals
- 5. PHASE 1 SCENARIO 2- NCLEX MEMORIAL HOSPITAL Lucia Ruiz Rasmussen College Author Notes This paper is being submitted on February 17, 2017 JuTon Hemphill’s Inferential Statistics and Analytics course. Introduction The scenario I shall be working with is whereby I am working at NCLEX Memorial Hospital in the infectious disease unit. As a healthcare professional, I need to work to improve the health of individuals, families and communities in various settings. The current situation that has posed as a problem at the hospital and raised eyebrows is that in the past few days, there has been an increase in patients admitted with an infectious disease. The basic statistical analysis shows that the disease does not affect minors hence the ages of the infected patients does play a critical role in the method that shall be required to treat the patients to impact positively on the health and wellbeing of the clients being served whether infected with the disease or associated with those infected. After speaking to the manager, we decided that we shall work together in utilising the available statistical analysis to look closer into the ages of the infected
- 6. patients. To do that, I had to put together a spreadsheet with the data containing the information we shall need to carry out the analysis. Data Analysis From the data collected and input on an Excel sheet, there are sixty patients with the infectious disease. Of the patient’s whose data has already been collected an input on the excel sheet, the ages range from thirty-five years of age to seventy-six. There is only one patient in their thirties with the age of thirty-five. There are five patients in their forties, One forty-five, one forty-six, two at forty-eight and two at forty-nine. There are fifteen patients in their fifties, two at fifty, one fifty-two, one fifty-three, one fifty-four, four at fifty-five, one fifty-six, one at fifty-eight and four at fifty-nine. There are twenty-three patients in their sixties, five at sixty, one at sixty-two, one at sixty-three, two at sixty-four, one at sixty-five, three at sixty- eight and seven at sixty-nine. Finally, we have fifteen infected patients in their seventies, six at seventy, three at seventy-one, three at seventy-two, one at seventy-three, one at seventy-four and one at seventy-six. From the graph in Figure 1 below, the horizontal axis depicts the age group of patients infected with the disease and the vertical axis depicts the number of patients in the age group infected with the disease. Figure 1 Data Classification The qualitative variables in our data analysis would be the names of the patients infected with the disease while the quantitative data would be their ages, number of patients in each age category or age bracket that are infected with the disease and the number of patients in each specific age that are affected. The graph in Figure 1 above shows a quantitative analysis of the data. The discrete variables in this analysis are the number of patients infected with the disease because they could continue to increase to a finite number and we could still count them and add them to the analysis. Our continuous
- 7. variable in this analysis is the age. For our analysis, we shall use the age in years. In our data set, the qualitative data has been omitted. The quantitative data is being measured based on the number of patients counted to have the disease and their ages. We have classified them in clusters of five in the graph to visualise the analysis. The discrete variable is being measured by the number of patients already diagnosed with the diseases and the continuous variable which is the age is currently being measured annually. The Measures of Centre and Variation The measures of centre are the values in the middle of the data set which is the focal point. It can be determined using the mean medium and the mode. The mean defines the very centre and could also be defined as the average point. In our data analysis, it is important to figure out the centre of variation because it shall assist us to determine the most common age bracket that has been infected with the disease and shall therefore help us narrow down to the cause and effect faster by concentrating on the mean median and the mode of the data analysis. The measures of variation are those that are utilised to describe data distribution and the variation between random variable. They show the range between the greatest and the least data values which are commonly known as the difference. Quartiles can be used to measure variation as they divide the data set into four equal parts. They are important as they assist in measuring probability of occurrence. In our case, they could be used with the most common age group to have the infectious disease and random variables such as their residents, their places of work and their activities or eating habits could be used to further analyse the data to figure out the source, the cure and the best way to prevent the spread. Arithmetically, it is derived by the variance and standard deviations of a data set. Calculation of the Measures of Center and Measures of Variation The Mean The mean is the average of the data set and normally the centre
- 8. of the data. The Mean = Total of Ages / Sample Size The Mean = 3705 / 60 = 61.81667 The Mean = 61.82 The Median The Median = The Value in the Centre of the data which in our case is the value in the centre of the ages. There are 60 patients hence our median shall be the age of the 30th patient. The Median = 61 The Midrange The Midrange = The Midpoint between the lowest and the highest values. In our data set, the lowest age value is 35 and the highest is 76 The Midrange = (35+76) /2 The Midrange = 111/2 =55.5 Midrange = 55.5 The Mode The mode is the most frequent value in the data set. Our data set is composed of the ages of the infected patients with the disease. The most frequent age is 69 which has 7 patients Mode= 69 The Range The range of a data set is the difference between the highest and the lowest values in the set. Our data set is composed of the infected patient’s ages. The highest value is 76 and the lowest is 35. The Range = 76 -35 The Range = 41 The Variance Measures how far the data are from the mean. In this case the variance is 4698.9833/60 = 78.3164 The Standard deviation is calculated from the SQRT of the variance. In this case = 8.85 Conclusion
- 9. The conclusion from our study of the patients infected with the infectious disease in NCLEX Memorial Hospital is that they are currently sixty. The most infected patients range between the age of sixty and seventy-five but the highest number of infected patients are the age of sixty-nine as they are seven. The disease seems to be attained by the elderly from the age of thirty-five and seventy-six with the average age being sixty-one. Children, teenagers, the youth and the extremely elderly are not prone to the infectious disease. Infected Patients Graph "Patients 35-39 40-45 46-49 50-55 56-59 60-65 66-69 70-75 76-80 1 1 5 9 6 12 10 14 1