...Budget Variance Every hospital faces many challenges, but one of the most difficult challenges is knowing how to manage expenses. When a hospital is planning their budget not only do they have to plan a budget that is beneficial for their staff but also high level care for their patients. With so much in society changing such as technology, increase health care cost, and government restrictions it makes planning the budget very challenging. In order to analyze how expenses are being spent compared to the budget that is allotted for them, budget variances have to be made (accountingtools.com, 2013). These variances have to be extremely well detailed in order to make an accurate budget for the following year. Things that should be taken into consideration when doing a budget variance are variance reports, interpreting the results of variance reports and analyzing these reports based on actual performance, and developing a way to better the budget based on these results. The purpose of this paper is to fully analyze all factors that should be considered for a budget variance for a hospital. The very first step for a budget variance is to develop a variance report (accountingtool.com, 2013). There are various factors that should be analyzed to produce a proper variance report such as how many beds are available, how many patients are seen daily, how much staff is available or working in a specific day, how many supplies will be used, and how much utilities such as electricity...
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...other model. The chi-square is the difference in the –2 log likelihood values for the two models. A priori test: A test of the difference between two groups of scores when this comparison has been planned ignorant of the actual data. This contrasts with a post hoc test which is carried out after the data have been collected and which has no particularly strong expectations about the outcome. Adjusted mean: A mean score when the influence of one or more covariates has been removed especially in analysis of covariance. Alpha level: The level of risk that the researcher is prepared to mistakenly accept the hypothesis on the basis of the available data. Typically this is set at a maximum of 5% or .05 and is, of course, otherwise referred to as the level of significance. Analysis of covariance (ANCOVA): A variant of the analysis of variance (ANOVA) in which scores on the dependent variable are adjusted to take into account (control) a covariate(s). For example, differences between conditions of an experiment at pre-test can be controlled for. Analysis of variance (ANOVA): An extensive group of tests of significance which compare means on a dependent variable. There may be one or more independent (grouping) variables or factors. ANOVA is essential in the analysis of most laboratory experiments. Association: A relationship between two variables. Bar chart: A picture in which frequencies are represented by the height of a set of bars. It should be the areas of a set of bars, but SPSS...
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...CHAPTER ONE Really statisticians do not perform experiments but majorly depends on the data collected. To be specific, experiments can be defined as an act of operation undertaken in order to discover the hidden principle effect and test, establish and illustrate the unknown truth. Generally, researchers carry out the experiments in order to accept or reject certain hypothesis. Experiments are conducted to establish tht effect of one or more in dependent variables in a response, which is dependent variable. These independent variables are often called treatments or factors. Examples are different fertilizers, different makes of machines and different advertisement channels. The values of a response are supposed to reflect the effect of different treatments. If an experiment is to be carried out on a particular project, the objective of the experiment must be clearly stated, unless the objective of tge experiments must be clearly stated, unless the objective of the project is carefully identified, the most appropriate design may not be adopted. The main aim of this project is to use the collected data to investigate the effect of different levels of nitrogen fertilizers on the fertilizers on the yield of maize and sugar beet separately using analysis of variance (ANOVA) method at the institute of agricultural research Akure It should be noted that the data used here is secondary data. Fertilization has to do with the aim of bringing about an increase in crop yield and consequently...
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...................... 4 2.1 EVENTS & PROBABILITIES 2.2 JOINT ............................................................................................................................................ 4 MARGINAL& CONDITIONAL PROBABILITIES 2.3 PROBABILITIES TREES ....................................................................................... 4 .......................................................................................................................................................... 4 2.4 BAYER’S THEOREM ......................................................................................................................................................................... 4 2.5 POPULATION MEAN & VARIANCE ......................................................................................................................... 4 2.6 BIVARIATE DISTRIBUTION .................................................................................................................................... 5 2.7 PORTFOLIO...
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...Analyzing the impact of FDI on Inflation of the NAFTA Countries using Fixed Effects Model and Random Effect Model Objective of this study is to examine the impact of foreign direct investment on the inflation of the NAFTA countries (United States, Canada and Mexico). To conduct the analysis we have developed a panel data set consisting of FDI and inflation data of all three countries from 1988 to 2014. One important aspect of this analysis is test the notion that unobservable factors that might simultaneously affect the left hand side and right hand side of the regression are time-invariant. To test this, we need to use Fixed Effects (F-E) model and Random Effects (R-E) model. We used STATA for necessary analysis. Results are presented below: Simple OLS regression (see Table-1) tells us that for NAFTA countries, FDI and inflation is negatively correlated and statistically significant. The estimated coefficient of -.0000251 indicates that for 1 dollar increase in FDI, inflation is expected to decrease by .0000251%, ceteris paribus. The data set has been estimated by both 1) least squares dummy variable (LSDV) estimator. 2) Fixed effects estimator. The LSDV (see Table-2) fits the data better than does the pooled OLS. The F statistic increased from 5.05 to 10.21 (p<0000); SSE (sum of squares due to error or residual) decreased from .633356331 to .482044733; and R2 increased from 0.0601 to 0.2846. Due to the dummies included, this model loses two degrees of freedom (from 79...
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...Dichotomous II- Likert-type scale III- List of items IV- Matrix question Correlation analysis is used to describe the strength and direction of the linear relationship between two variables. Types I- Pearson correlation is used when quantitative data is normally distributed. II- Spearman correlation is used when data is not normally distributed Reliability: Reliability of measure indicates extent to which it is without bias and hence ensures consistent measurement across time (stability) and across the various items in the instrument (internal consistency). Reliability is a test of how consistently a measuring instrument measures whatever concept it is measuring. Internal Consistency the homogeneity of the items in the measure I- items should hang together as a set and be capable of independently measuring the same concept II- Whether the items and the subsets of items in the measuring instrument are correlated highly. a. Inter-item Consistency Reliability: This is a test of the consistency of respondents’ answers to all the items in a measure. The most popular test of inter-item consistency reliability is the Cronbach’s coefficient alpha. b.Split-Half Reliability: Split-half reliability reflects the correlations between two halves of an instrument Screening * Missing Data: Statistical Problems: SEM requires a certain minimum number of data points in order to...
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...Analysis of variance The main objective of variance analysis (analysis of variance - ANOVA) and comparison more than two groups with regard to localization '. To illustrate, consider the situation in which to evaluate the effectiveness of a new drug in the treatment of deter- undermined administrac~ao disease through the four different treatments: the new drug, two other already existing on the market to treat the disease concerned and a placebo. Different are treatments used in individuals who suffer from that disease distributed randomly four groups. Sera that it is probable that the four treatments have different results? Sera that the new medicamentoe more effective than existing already in the market? The analysis of variance seeks to address quest~oes of this type through the comparison of the localizac~oes different groups. This comparac~aoe made from the analysis of the dispersion present in the set data; Hence the designac~ao of variance analysis. In the example described above, the observations come from classified through a so groups factor (the disease in question); in this case, there is talk in analysis of variance with one factor - one-way ANOVA. Of course that sounds legitimate to consider such a factor as the cause of differences between the media if it can ensure the homogeneity of populac~oes in relation to all other factors that might be relevant to the explanation of the phenomenon. In many situations practices ha more than a factor influencing the results...
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...Abstract Business strategy is the strategy which the company uses to compete with their rivals in the same industry. Business strategy is also considered as the spine of the business because it dominates other operations in the company and helps create competitive advantages over their competitors. Due to these very important roles of the business strategy, we decided conduct this research paper with the aim to test whether choosing different types of business strategies will lead to difference in the performance of the companies or not. This research paper is based on the data from quarterly financial reports of the three companies in the computer industry in US from 1999 to 2011. With the findings of the research, the paper draws a conclusion that the impacts of different strategies are not the same, so that the managers should choose the most appropriate and profit-maximizing strategy for their company. Moreover, this result of the test will give incentives to the firms to put forward further test to find out the most effective strategy, which then can be recommended to Vietnam’s businesses. I. Scenario Computer industry is a very young industry in the world and it was not until the late 1990s that it really developed rapidly. In spite of this fact, the computer industry has gained a lot of significant achievements, opening a new era for the human-beings. It is also considered as an industry that has the fastest growth rate with a lot of new innovative products being produced...
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...&r' 1 || i I Test Anxiety Applied Research, Assessment, and Treatment Interventions i I : fet 2nd Edition I « MARTY SAPP m: I H 1 ttTttTtttttrrtiTTITTtrrtttttttttTtrttiTTtrrttTtttTtTTTtttttiTttt TEST ANXIETY Applied Research, Assessment, and Treatment Interventions 2nd Edition Marty Sapp University Press of America, Inc. Lanham • New York • Oxford Copyright © 1999 by University Press of America,® Inc. 4720 Boston Way Lanham, Maryland 20706 12 Hid's Copse Rd. Cumnor Hill, Oxford 0X2 9JJ All rights reserved Printed in the United States of America British Library Cataloging in Publication Information Available Library of Congress Cataloging-in-Publication Data Sapp, Marty. Test Anxiety : applied research, assessment, and treatment interventions / Marty Sapp. —2nd ed. p. cm. Includes bibliographical references and indexes. 1. Test anxiety—Research—Statistical methods. 2. Social sciences—Statistical methods. I. Title. LB3060.6.S27 1999 371.26'01'9—dc21 99—22530 CIP ISBN 0-7618-1386-1 (cloth: alk. ppr.) fc/ The paper used in this publication meets the minimum requirements of American National Standard for Information Sciences—Permanence of Paper for Printed Library Materials, ANSI Z39.48—1984 To my students Preface to First Edition Preface to Second Edition This text is divided into three parts. Part I deals with applied research design and statistical methodology frequently occurring...
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...Research Methodology Unit IV Editing:- The raw data is likely to contain a no. of errors during the process of recording the information in surveys. By means of editing one tries to eliminate the errors or remove the points of confusion, if any. For proper analysis & interpretation, it is necessary that the collected data are present, readable & accurate in the desired form i.e., data collected has no missing values, all entries are readable or all information is accurate. This introduces errors in collected information, which are to rectified before further analysis. Editing comprises inspecting, correcting & mo0difying the raw data to ensure that information available is correctly, adequately & relevantly. Stages of Editing:- The editing may be done in two stages: Field Editing:- The field editing is done immediately after collection of data because the interviewer then have a fresh memory about the lapses & wrong statements of answers. The preliminary editing should be done by a field supervisor. Office Editing:- The office editing is done after the field editing. This implies a complete & thorough scrutiny of questionnaire. There should be expert editors in the office to evaluate & examine the completed returns of the respondents. The incorrect answers may also be verified by observation. When the answers are given in an ambiguous manner, serious efforts are made to rectify & complete them by consulting another questionnaire. Factors to be considered while Editing:- ...
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...questions Mr. Dean had. He then instructed her to run the analysis and they would go over the results the following day. Ms. Brown ended up with six questions that Mr. Dean was especially interested in. (Burns, A., Bush, R. (2006) p518) The problem with this case study is even though Ms. Brown may be a competent employee she isn’t Mr. Rogers. Mr. Rogers called the meeting and has his own agenda in regards to the survey findings. Unless Mr. Brown and Mr. Rogers are on the exact same page she may not be able to determine the right questions that Mr. Dean may ask: furthermore, her analysis may be in complete depending on what information she has taken form Mr. Dean. Mr. Rogers should have rescheduled the meeting so that he could ensure all pertinent areas regarding the results of the survey and questions were adequately addressed. The strengths of this case are apparent in that it is assumed that Ms. Brown has a good understanding of what Mr. Rogers wants. If she is able to accurately gather the information from Mr. Dean in regards to his concerns about the survey, then her analysis should be accurate: however this will not be known until the following day when Mr. Rogers and Ms. Brown go over the results. There is an apparent weakness in this case study in that Mr. Rogers is not present for the fact finding portion in regards to what questions Mr. Dean may have. Not to mention Mr. Rogers has left the analysis part up to Ms. Brown. It’s good that he is going over...
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... 1; pg. 158, 12 pgs| | » | |Jump to full text [pic] | | | | | | » | |Translate document into: Spanish , Portuguese | | | [pic][pic][pic][pic] |Subjects: |[pic][pic][pic][pic][pic][pic][pic][pic]Studies, Total quality, Competitive advantage, Hypotheses, Correlation | | |analysis, Organization theory, Regression analysis | |Classification Codes |9190 United States, 9130 Experimental/theoretical, 2500 Organizational behavior | |Locations: |United States, US | |Author(s): |Thomas J Douglas [pic], William Q Judge Jr [pic] | |Document types: |Feature | |Publication...
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...Multi-regression Analysis Summer 2013 EC315: Quantitative Research Methods Professor Scott Sowder Introduction One day I was sitting in class with my classmates. Our GPA, the number of classes were are taking, ages, IQ and the amount of time we spend studying were all different. I became curious and wanted to know what effect the different variables had on the student’s GPA, if any. So I decided to a survey of 30 students with varies GPAs, IQs, ages, number of classes being taken and the time they spend studying. My hypothesis statement is that all the independent variables will have the same or no effect on the dependent variable. The alternate statement is that at least one of the independent variables will have an effect on the dependent variable. I have applied a 95% confidence level, which means I am 95% sure that I will be able to show that at least one of the independent variables will have an effect on the GPA. Variable Identification My dependent variable is the students’ GPA. I chose GPA as my dependent variable because it relies on the other variables. The remaining variables are my independent variables. I chose them because they could all have an effect on a student’s GPA. Student | GPA (4.0) | # Classes | Age | IQ | Study time | 1 | 3.2 | 4 | 29 | 119 | 12 | 2 | 3.1 | 2 | 31 | 118 | 8 | 3 | 3.7 | 1 | 28 | 135 | 6 | 4 | 3.5 | 3 | 22 | 129 | 13 | 5 | 2.8 | 4 | 22 | 110 | 15 | 6 | 3.0 | 3 | 24 | 115 | 15 | 7 | 3.8 | 2 | 24 |...
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...past data about their clients or customers that what are their attributes as well as their insured vehicles attributes while insuring the same. They have heard a lot about the data analytics and are very sure that their competitors are also using some of these techniques to beat the competition. They have to be very focused to their target segment. Please help the company to answer some of their basic questions like: 1. What kind of segments generates better leads? 2. What would be a good target segment? And which segment is a “bad” segment? Solution For a car insurance company, it is important to determine different segments of customers based on the profitability to generate better leads. Hence , we have tried in our analysis to segment the customers that should be targeted for profitability and that which can be considered “bad”, i.e., not profitable to the customers. Reasons Analytics has been used to address the problem: - * Lots of data but little conclusive information * Analytical capability is a Key Competitive Advantage * Information should be appropriate to take action in corporate Our Approach: Steps Followed 1. Identification of Appropriate Data 2. Perform Required Statistical Analysis 3. Segmentation of Customers based on desired criterion 4. Characteristics identification of Customers based on segment 5. Planning business strategies for the targeted segments Data Used * Primary data is provided...
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...| ABSTRACT | | | iii | ACKNOWLEDGMENT | | | vi | TABLE OF CONTENTS | | | viii | LIST OF TABLES | | | xxiii | LIST OF FIGURES | | | xxvi | CHAPTERS | | | | 1 | THE PROBLEM AND ITS BACKGROUND | | Introduction | 1 | | Related Literature | 2 | | Conceptual Framework | 3 | | Statement of the Problem | 3 | | Null Hypothesis | 5 | | Significance of the Study | 6 | | Scope and Limitation of the Study | 7 | | Definition of Terms | 8 | 2 | RESEARCH METHODOLOGY | 34 | | Research Design | 39 | | Research Locale | 40 | | The Respondents | 42 | | Sampling Technique | 42 | | Research Instrument | 42 | | Data Gathering Procedure | 43 | | Statistical Treatment of Data | 43 | | Interpretation of Data | 45 | 3 | PRESENTATION,INTERPRETATIONAND ANALYSIS OF DATA | 48 | | Demographic Profile of the Respondents | 48 | | Perceptions of the Respondents on Emotional Intelligence | 57 | | Perceptions of the Respondents on Sources of Stress | 67 | | Perceptions of the Respondents on Stress Management Strategies | 79 | | Perceptions towards Emotional Intelligence when grouped according to Profile | 83 | | Significant Differences on Competency Level of the Respondents when grouped according to Profile | 85 | | Perceptions towards Stress Management Strategies when grouped according to Profile | 86 | | Significant Differences on the competency Level of the Respondents when grouped according to Profile | 87...
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