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Submitted By jasister

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Words 642

Pages 3

Kevin Lee

BUS-352

November 14, 2014

Statistics helps decision makers by transforming collected information into numbers. It allows decision makers to understand the risks associated with decisions that has or been made. Statistic collects, summarizes, presents, and analyzes data. Then it uses its data to help reach conclusions. It is crucial for statisticians to collect data carefully and fairly for importance of finding great value. To have a correct statistic data, it is necessary to have high-top work ethics. Statisticians must take a full responsibility of fairly collecting a data with interest of collecting a valuable data to make the best decisions. Statisticians should not favor any particular data. Statistics must apply Christian worldview principles of trust and honesty. It is an important standard to have for collecting statistic data. The numbers in collected data must be truthful. Utilitarianism ethical theory is most applicable for me. It’s about sacrificing few to save many. The Trolley Problem could be a supportive theory for the utilitarianism. The problem tells about a speeding train with full of passengers sees that there are few workers standing in on the rail road up ahead. To save those few, should the train hit the hard break which will kill all the passengers; or should they run over the few to save many in the train? Utilitarianism theory would support an idea of sacrificing those few to save many. I support that theory as well. If it’s a situation where one side must be sacrificed, it is more ethical choice to save many than few. Utilitarianism theory allows statisticians to stay on task, and preventing to make emotional or personal relationship to a particular data. The theory allows the statistics to stay honest and truthful, creating most corrective conclusions. Christian...

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... Cases Used All non-missing data are used. Syntax DESCRIPTIVES VARIABLES=Income /STATISTICS=MEAN STDDEV VARIANCE RANGE MIN MAX SKEWNESS. Resources Processor Time 00:00:00.00 Elapsed Time 00:00:00.02 [DataSet0] Descriptive Statistics N Range Minimum Maximum Mean Std. Deviation Statistic Statistic Statistic Statistic Statistic Statistic Three-Year-Average Median Income(2008-2010) 51 $29,453 $36,850 $66,303 $50,734.18 $7,555.310 Valid N (listwise) 51 Descriptive Statistics Variance Skewness Statistic Statistic Std. Error Three-Year-Average Median Income(2008-2010) 57082705.308 .389 .333 Valid N (listwise) EXAMINE VARIABLES=Income /PLOT BOXPLOT STEMLEAF /COMPARE GROUPS /PERCENTILES(5,10,25,50,75,90,95) HAVERAGE /STATISTICS DESCRIPTIVES EXTREME /CINTERVAL 95 /MISSING LISTWISE /NOTOTAL. Explore Notes Output Created 05-SEP-2012 16:32:55 Comments Input Active Dataset DataSet0 Filter Weight Split File N of Rows in Working Data File 51 Missing Value Handling Definition of Missing User-defined missing values for dependent variables are treated as missing. Cases Used Statistics are based on cases with no missing values for any dependent variable or factor used. Syntax EXAMINE VARIABLES=Income /PLOT BOXPLOT STEMLEAF /COMPARE GROUPS /PERCENTILES(5,10,25,50,75,90,95) HAVERAGE /STATISTICS DESCRIPTIVES EXTREME /CINTERVAL 95 /MISSING LISTWISE ......

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...To investigate if the mean JSL differs between the branches of the company. The data set used for the analysis: Variable | How the variable is measured | Branch | Branches of the company:1= TESS-Nizhnevartovsk, TESS-Kogalym2= TESS Head Office, TESS-Surgut3=TESS-Tyumen, TESS-Khanty-Mansiysk | Number | Number of the respondent | Work_Exp | Work Experience in JSC “TESS”:1= 2 year or less 2= more than 2 years | JSL | Job Satisfaction Level:Ratings from 1 to 5 where 1= very unsatisfied, 5= very satisfied and 0= no answer/blank | 1.2. Revised Data. Test for Normal Distribution To proceed with the analysis it is necessary to determine if the data are distributed normally. The Histogram below as well as the Descriptive Statistics (Appendix 1, Table 1b) show that the data distribution is leptokurtic (kurtosis is 2,021) and negatively skewed (skewness -,240). We can determine several outliers (Appendix 1, Table 1c, Table 1d) with extreme ratios. In cases #46 and #178 JSL is more than the highest option provided in the questionnaire. That could be a mistake in data entering or the respondent wanted to emphasise his/her satisfaction level. These cases were delisted. Cases with “0” responses are to...

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...Statistical Information Paper I will describe the use of statistic at Veterans hospital in Loma Linda that has 142 Hospital beds and 108 beds of Community Living Center. Employs 2,436 staff. The VA hospital Provided 546,017 outpatients visits in 2008.In 2010 Outpatients visits 584,028 it is increase 38011 or increase 1.07%. Statistics is data use to compare and analysis. Hospital statistics Includes current and historical data on utilization revenue, expenses, person and mush morel Will describe numerical data, numerical count, statically analysis, and four levels of Measurement. Numerical data. Bennett, Briggs, and Troika (2009). Numerical Numerical data is identified, measured, and numerical scale. Numerical data can be Displayed using charts, tables, and graphs. Example I work at medical floor is a busy floor. The Physician is always order many test for the new admit patient. Such as Order the patient, take X-Ray, EKG, CAT scan, GI lab so on. For example, if the patients come back for GI lab.Nurse has To take vital sign every 15 minutes times four, every 30 minutes times two, and one-hour time One. This Vital sign was taken to compare how the vital sign are difference between them. If the vital Sign Drop too low or too high that will nurse alert nurse to check the patient and report to the Physician right away. This entire vital sign nurse has to record in the computer that will show in Line graph. The line graph is easy to...

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...the following variables (all measured in billions USD) and estimate the corresponding model (Model 1):(Use α=0.05 for references) Yt: Defense budget outlay for year t X2t: GNP for year t X3t: US military sales in year t X4t: Aerospace industry sales in year t D1t: Dummy variable presenting the military conflict involving more than 100,000 troops; D1t=1 if more than 100,000 troops are involved and equal to 0 if fewer than 100,000 troops are involved. |Dependent Variable: Y Sample: 1962 1981 | |Method: Least Squares Included observations: 20 | |Variable |Coefficient |Std. Error |t-Statistic |Prob. | |C |21.40251 |1.496947 |14.29744 |0.0000 | |D1 |-48.21987 |6.871544 |-7.017328 |0.0000 | |X2 |0.013879 |0.003207 |4.328062 |0.0008 | |X3 |0.073146 |0.203805 |0.358902 |0.7254 | |X4 |1.389753 |0.130197 |10.67423 |0.0000 | |X4*D1 |1.540792 |0.325005 |4.740818 |0.0004 | |X2*D1 |0.022406 |0.005781 |3.876038 ......

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...1. Introduction Poverty, which is measured by the household income lower than poverty line has been identified as the dependent variable in this project. It is important to know which elements are associated with poverty. The purpose of this paper is to evaluate the key determinants of American household poverty in 1980. The four possible determinants will be analyzed in this project, the average numbers of every family (FAMSIZE), URB is the percent of people live in urban, UR is the level of people have no job over 16 years and the median family income in US dollars (INCOME). Descriptive statistics, correlation and regression will be used in this project. 2. Descriptive statistics Variable | Mean | Median | Mode | VAR | STDEV | URB | 58.76034483 | 66.15 | 0 | 1012.828049 | 31.82495953 | FAMSIZE | 3.140172414 | 3.135 | 2.93 | 0.033377163 | 0.182694178 | UR | 9.293103448 | 8.95 | 5.8 | 10.92696915 | 3.30559664 | INCOME | 19240.43103 | 18512 | N/A | 10889936.04 | 329.990309 | POV | 9.120689655 | 9.05 | 8.8 | 6.230792498 | 2.496155544 | 3. Correlation Correlation and regression are techniques for investigating the statistical relationship between two, or more, variables (Barrow, 2013, pp. 238). * Correlation defines the degree to which there is a linear relationship between pairs of variables. Firstly, it is useful to graph the variables to see if anything useful is revealed. In this case, XY graphs are the most suitable and they are shown in......

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...Download Share Add to Flag Embed Views: 292 Category: Education License: All Rights Reserved Presentation Description No description available. Comments Presentation Transcript Quality Associates : Case 1 Quality Associates Introduction : Introduction It is a case of a consulting firm which consults its clients regarding statistical procedures that is used to control the production process. In this case, Quality Associates has taken example with random sample size 30 of 4 samples i.e. 120 out of 800 given observations to explain the quality control process. Hypothesis : Hypothesis H0 : µ = 12 Ha : µ ≠ 12 Level of Significance = 0.01 Z test : Z test z = Z values : Z values Test statistic (z value) for all the samples P value : P value P values (2*(1-z score))for all the samples Rejection of null hypothesis : Rejection of null hypothesis Rejection rule for two tailed test using p-value approach Reject H0 if p-value ≤ α Standard Deviation : Standard Deviation Computed standard deviation for each of the samples Quality Associates utttsav Download Share Add to Flag Embed Views: 292 Category: Education License: All Rights Reserved Presentation Description No description available. Comments Presentation Transcript Quality Associates : Case 1 Quality Associates Introduction : Introduction It is a case of a consulting firm which consults its clients......

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