...Statistical Information Shella M. Gettings University of Phoenix HCS/438 Statistics are a part of the everyday world. They are all around us and help us understand information in a clear and distinct manner. In our professional and personal lives, we would have difficulty comprehending some data without statistics. That is particularly true in the medical field and in hospitals. Without statistics, I as a nurse, would not know where I needed to improve my provided care or in which direction to instruct my team to focus their time on. This paper will discuss statistical information that use at a local community medical facility, where I am employed, Alliance Health Deaconess, where I am currently employed as a medical-surgical and oncology registered nurse. Statistical Information How Statistics are used at my Workplace: There are many memos and emails that are passed around to the nurses that management and administrators hope that we read and absorb. In all honesty, they are glanced at and tossed aside the majority of the time. On rare occasions, we are handed graphs and charts that explain what they expect and shows us specifically what needs to be improved upon. Since our jobs are to care for the sick and hurt, this information is not tossed aside and is typically taken more seriously. One main focus for the nursing staff at my facility is safety scores. We have two medical-surgical floors, one including oncology and the other orthopedics, one intensive...
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...Falls: Risks, Facts, Prevention Falls: Risks, Facts, Prevention The following information is a compilation of the teaching plan utilized for the community teaching assignment. The information presented includes statistical analysis and detailed information on potential risks of injury and death related to falls among the elderly. It also highlights prevention methods that can be utilized in an attempt to decrease Emergency Room visits secondary to falls. My target audience consisted of 18 senior citizens who reside at Heartfield Assisted Living Facility in Cary, NC. The median age of this group was 78 years old. A wheelchair with faulty brakes, oxygen tubing and a quad cane were used for props and demonstrations of safe vs. unsafe use. My teaching plan followed the pamphlet that was created for the teaching assignment. I chose this format as I felt it would be helpful to provide a resource for seniors to reference after completion of the session. The title of the pamphlet is Falls: Risks, Facts, Prevention; Understanding potential hazards and how to promote safety. The pamphlet/teaching was broken down into three categories: facts and statistics related to falls in the elderly, fall risks and prevention methods. An “Are you at Risk”? question and answer segment was also included to create awareness for individuals who believe they are practicing safety. This encouraged participants to analyze their...
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...individual uses. There is data used for budgeting, staffing, and supplies. Data is collected on demographics of the patient population. Data is measured constantly in patients; vital signs, lab values, and measurements of all sorts. We also conduct clinical trials in vast numbers collecting data on efficacy, treatment variation, modalities, and outcomes. 2. One example of descriptive statistics we use in our work place is yearly charts depicting prevalence of falls hospital wide. These charts are shown in a number of ways and are divided by unit so that each unit can be compared one with the other. Additionally, they have charts showing the yearly trends by unit and hospital-wide. They get fairly detailed on some of them showing specific categories of fall occurrence, for example; assisted vs. unassisted; Falls due to toileting; falls by time of day; falls by assessed risk category. This practice is highly effective and allows everyone to see where, when, and how falls are occurring. It also provides insight into how to go about reducing the instances. 3. Where I work we do use inferential statistics, in fact we apply it a lot. We administer chemotherapy and study its effects. We collect data from sample populations (trials) and based on the analysis develop protocols to treat patients for side effects that are commonly associated with the treatment, regardless of actual occurrence or rather before assumed occurrence. An example of this is the chemotherapy “nadir” effect; data...
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...such significant words. Romeo is in love, and teenagers of this time period can also plunge into the emotions of love. Teenagers are capable of being in love because of examples from iconic duos, historic examples, and statistics. The main reason that teenagers can be in love are the examples of iconic duos. Romeo and Juliet is the ageless example of true love between adolescents. In this quote, Juliet explains that they can not be too rash. “Well, do not swear. Although I joy in thee, I have no joy of this contract tonight. It is too rash, too unadvised, too sudden,Too like the lightning, which doth cease to be...
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...Statistics and Business Analytics, FALL 2014 ES-1, Grp 7: Koel BANERJEE, Sahil BIHARI, Rong TIAN, Jean-Baptiste VARNIER, Dmitry YATSENKO House Prices Analysis in the DFW Area Report prepared for M. Sam Horton HEC PARIS MBA PROGRAM FALL 2014 FINAL TEAM PROJECT 0 Statistics and Business Analytics, FALL 2014 ES-1, Grp 7: Koel BANERJEE, Sahil BIHARI, Rong TIAN, Jean-Baptiste VARNIER, Dmitry YATSENKO This report summarizes the statistical analysis performed in relation to the sale of houses in the Dallas Fort Worth (DFW) area, which includes Dallas, Fort Worth, Arlington and the MidCities. This report was prepared exclusively for M. Horton. The purpose of the statistical analysis is to investigate whether the claims from two former Horton Realty customers that their houses were underpriced are justified. 1) Descriptive analysis In preparation of this report, we analyzed residential sales data received from Pat McCloskey that occurred in 2010 in the DFW area. The sample data received included: Categorical variables: the sale quarter (ordinal), the location of the house within the DFW metroplex (nominal), the real estate agency that sold the home (nominal), the observation ID (nominal) and, Quantitative variables: the sale price (ratio), the size of the home (ratio), the number of bedrooms (ratio) and the age of the house (ratio). A descriptive analysis of our sample is performed in Sections 1.1 and 1.2. The observations made in these two sections concern the sample...
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...Misuse of Statistics Studying about misuses of statistics with example: Statistics: Statistics is an aggregate of facts. Individual facts do not constitute statistics.The height of an individual does not constitute statistics. But the heights of 50 students in a class constitute statistics, since they are affected by multiplicity of causes, like age, heritage etc.The facts must be related to some department of enquiry. Collection of facts will not form statistics unless they are subjected to enquiry.Statistical data should be collected in a systematic manner keeping the purpose in view. Statistics means the methods used for collection, classification, analysis and interpretation of numerical data. Statistics is also defined as the science which deals with collection, analysis and interpretation of numerical data. Limitations and Misuses of Statistics with Examples: Statistics can be used only to study numerically valued data. Statistics deals only with aggregate and not with individuals. Statistical data are true only on an average. Statistical data collected for given purpose cannot be applied to any situation. It is not always possible to compare statistical data, unless they are homogeneous in character. Misuses of Statistics with Examples: Statistical methods should be intelligently and carefully used as their misuses may lead to unsatisfactory results and dangerous conclusions. False conclusions will follow if the data collected is incomplete...
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...this is z.05 = 1.645. The decision rule is Reject H0 if z > 1.645 Otherwise do not reject H0 Step 3: Calculate the value of the test Statistic. Sample proportion, The value of the test statistic is Step 4: Make a decision. The value of the test statistic (2.45) is greater than the critical value of Z (1.645) and it falls in the rejection region. Hence, we reject the null hypothesis. Step 5: Conclusion. We conclude that the proportion of people using facebook is greater than 50%. Claim: Does the average children living along with them is less than 2? Step1: State the null and alternative hypothesis. Null hypothesis: H0: That is, the average children living along with them is greater than or equal to 2 Alternate hypothesis: H1: That is, the average children living along with them is less than 2 Step 2: Determine the rejection regions. The significance level is, α = 0.05 The degree of freedom is given by n-1 = 24-1=23 The critical value at degrees of freedom 23 and level of significance 0.05 is 1.71 Rejection region: Reject null hypothesis, if |t| < -1.71 Step 3: Calculate the value of the test Statistic. The value of the test statistic is Step 4: Make a decision. The value of the test statistic |t| (-6.12) is not greater than the critical value of t (-1.72) and it falls in the rejection region....
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...Math 533: Applied Managerial Statistics Course Project Part C: Regression and Correlation Analy Summary After statistical analysis of data collected on a random sample of AJ Davis’ customers, several inferences can be made. The goal of this analysis was to determine the best model for predicting customer income. This knowledge is paramount in most aspects of the company including but not limited to advertising, sales and merchandising. Based on the analysis, it is determined that a model using customer credit balance and household size is the most efficient. These variables gave the most reliable predictions of customer income. At the close of the analysis, the data yielded the following equation for determining customer income, income== - 1.90 + 0.0173 CREDIT BALANCE($) - 5.30 SIZE - 0.390 YEARS. Though, the number of years a customer has live in their home did not show high effectiveness in predicting income, it increased the overall fit of the model and has been included in the equation. We can be 95% confident in the data obtained through the use of this formula. Appendix 1. 2. Regression Analysis: INCOME($1000) versus CREDIT BALANCE($) The regression equation is INCOME($1000) = 4.45 + 0.00987 CREDIT BALANCE($) Predictor Coef SE Coef T P Constant 4.448 7.037 0.63 0.530 CREDIT BALANCE($) 0.009866 0.001728 5.71 0.000 S = 11.3247 R-Sq = 40.5% R-Sq(adj) = 39.2% Analysis of Variance Source...
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...Appendix: A Crash Course in Fundamental Statistical Concepts INTRODUCTION Throughout the book we’ve attempted to provide as much statistical background as possible without letting it get too overwhelming. In this appendix we review some fundamental statistical concepts and provide pointers to chapters where the concepts are covered in greater detail. If you’ve never had an introductory statistics class or don’t remember basic concepts such as measuring central tendency and variability, then you can use this appendix for a quick review. TYPES OF DATA The first step in using statistics to make better decisions is to obtain measurements. There are two major types of measurements: quantitative and categorical. Task time, number of usability problems, and rating-scale data are quantitative. Things like gender, operating system, and usability problem type are categorical variables. Quantitative data fall on a spectrum from continuous to discrete-binary, as shown in Figure A.1. Note that the extreme discrete end of this spectrum includes binary categorical measurements such as pass/fail and yes/no. The more discrete the data, the larger the required sample size for the same level of precision as a continuous measure. Also, you’ll usually use different statistical tests for continuous versus discrete data (see Chapters 3–6). Discrete data have finite values, or buckets. You can count them. Continuous data technically have an infinite number of steps, which form a continuum...
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...Statistics http://science.kennesaw.edu/~jdemaio/1107/hypothesis_testing.htm | | | Hypothesis TestingA statistical hypothesis is a statement about the value of a population parameter.The Alternative Hypothesis, Ha, is usually the hypothesis for which the researchers wish to gather evidence to support.The Null Hypothesis, Ho, is usually the hypothesis for which the researchers wish to gather evidence to reject. The Null Hypothesis, Ho, is always expressed in the form of an equality. i.e. Ho: = 5.8 lbs. or Ho: p= 3.2%example: The Georgia Department of Transportation claims the average number of accidents on I-285 each day is 3.2. We believe the claimed average is too small. State Ho and Ha. Ho: = 3.2Ha: > 3.2example: Kennesaw State University claims the average GPA is 2.73. We believe the claimed average is incorrect. State Ho and Ha. Ho: = 2.73Ha: 2.73A one-tailed test is one where Ha is directional and includes < or >.orA two-tailed test is one where Ha no direction is indicated and utilizes . What can go right? Decide Ho is true when it is true. Decide Ha is true when it is true. What can go wrong? Decide Ho is true when it is false. Type II errorDecide Ha is true when it is false. Type I errorThe level of significance , is the probability of making a Type I error.The rejection region is the set of possible values for which the null hypothesis will be rejected. This region will depend on .In specifying the rejection region for a...
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...Paper HCS 438 July 17, 2011 Monica Vargas Statistical Information Paper Statistics are used in many different ways in my workplace. The use of statistics is for the improvement of quality care and safety. Statistics are also used to measure employee compliance in regards to hand washing and proper use of policies and procedures. We also use charts and graphs to show infection rates, skin integrity, falls within the facility, budget concerns, and many more. These graphs help hospital personal improve care and safety to provide quality care to all patients. Graphs can also be used to measure patient and employee satisfaction. Descriptive statistics are used to describe the basic features of the data in a study and do not involve generalizing the data that has been collected. They provide simple summaries about the sample and the measures. Together with simple graphics analysis, they form the basis of virtually every quantitative analysis of data (Trochim, 2006). An example of descriptive statistics used at my workplace can be the number of patients that are admitted into the hospital on a Monday versus a patient admitted on any other day of the week. This information can also be broken down into more descriptive categories such as how many patient were men, women , children, what is their diagnosis, why were they admitted, and so on and so forth. We use inferential statistics to make judgments of the probability that an observed difference between groups...
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...Calorie and Alcohol Content Percentage in Large Sample of Premium Beers Due Date: December 8, 2003 Descriptive Statistics for Calories Per Beer: Focusing on the Descriptive Statistics of the number of Calories in the sample of beers it can be determined that, the average calories per beer, 142.3478, is the central point around which the data cluster. The median calories per beer, 148, are the middle value in the data and means that fifty percent of the beers have calories above 148 and fifty percent of the beers have calories below 148. The mode, 148, is the value, or number of calories that occur most frequently in the data set. The standard deviation is the typical spread around the mean number of calories per beer and is +/- 29.90221 calories. The range of calories per beer is 143 and measures the total spread of the data from the minimum value of 58 calories per beer to the maximum value of 201 calories per beer. The shape of the data concerning calories per beer is left or negatively skewed, meaning that there are some extremely low outliers, or beers that are very low in calories, which distort the shape of the data. Looking at the Frequency Distribution of Calories per beer it can be said that, 58% of the beers have 150 calories of less, 92.75% of the beers have 170 calories of less, and 34.78% of the beers fall between 150 and 170 calories per beer. Interval Estimates of Calories Per Beer: Concentrating on the Interval Estimate of Calories of beer it can be understood...
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...and 1 and is based on residents' net income, helps define the gap between the rich and the poor, with 1 representing perfect inequality where only one household has any income and 0 representing perfect equality, where all households have equal income (U.S. Census Bureau, 2012). It is named after its developer, Corrado Gini (1884-1965), an Italian statistician of the early 20th century. As with all statistics, when collecting the income data initially, there will always be systematic and random errors. If the data is less accurate, then the Gini coefficient has less meaning. Also, countries may measure the Gini coefficient differently, thus reducing the utility in comparison of the coefficient values between countries (The University of Texas at Austin, 2005). 1.2 Gini Coefficient for income of Bangladesh (as of 2010) Item | Gini Coefficient | National | 0.458 | Rural | 0.430 | Urban | 0.452 | Table 1: The national, along with the rural and urban Gini Coefficient for income of Bangladesh (Source: Statistical Yearbook of Bangladesh-2010; Bangladesh Bureau of Statistics) 1.3 Gini Coefficient for expenditure of Bangladesh (as of 2010) Item | Gini Coefficient | National | 0.320 |...
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...economic policy; therefore, I provided research on the general outlook for economists. The Bureau of Labor Statistics documented that “53 percent of economists” are employed by the United States federal government, state government, or an affiliated agency; and the employment outlook will rise at 6% from 2008 through 2018. “Candidates who hold master’s or Ph.D. degree in economics will have the best employment prospects and advancement opportunities” where as applicants holding a bachelor’s degree will have limited access to entry level positions. As the field of economics requires advanced education, I have conducted extensive research on applicable graduate and PhD programs. The degrees I am considering include a Master of Public Administration (MPA), Master in Public Policy (MPP), Master of Arts in International Economics and Finance (MAIEF), or PhD. Program in Economics. My target schools are the University of Pennsylvania’s Fels Institute of Government, Bandeis University’s Lemberg Program, Duke University, and American University’s School of Public Affairs. The BLS referenced that salary ranges for economists varied greatly; the highest 10 percent of professionals earned more than $149,110 and the lowest 10 percent earn less than $44,050. I also reviewed the GS salary grades for the federal government. Based on my co-op experience and bachelor degree, I would expect to fall between a GS-5-7, starting at $26,264 to a maximum of $42, 290. However, after completing a graduate program...
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...committed and seriousness of the crime. Criminal behavior changes from place to place and from time to time. Strong public notions and varying moral values have a substantial influence on the interpretation and understanding of crime. The first main problem of studying crime is coming up with the appropriate definition of the act or behavior. Discussion Crime covers complicated and several acts and behaviors that it has been difficult to have a standard definition of the subject. For instance, some countries and states have legalized abortion while others still consider abortion a crime. Matters of homosexuality are still frowned upon by some people because of their moral standings. In this case, it can be confusing on whether abortion falls under criminal activities or lawful actions. Nevertheless, the general definition of crime is any infringing action or behavior of criminal law. Therefore, the simplest way of defining crime is to present it as something that is entirely in opposition to the law and liable to be punished by the state (Bartol & Bartol, 2011). Measuring crime can also be a difficult aspect in...
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