...BUSN311-1302A-02 Quantitative Methods and Analysis Unit 3 DB Leah Murray May 13, 2013 While determining a sample size, the researcher would first need to know how many people, otherwise how many animals would be required because if you do not have enough sample size then it will have an cause on the general study conclusion (2006). The arithmetical power, P level as well as the treatment including the error variability is the factors otherwise; it would be the parameters in order to aids the researchers with choosing the correct sample size for the study (2006). The arithmetical power informs us how powerful the contributing people otherwise the animals in the study is going to be affected with the treatment that will be given to each of them. The P level will assist the researchers in determining the probability for any variation within the topic throughout the study (2006). At last, the investigators have determined the accurate sample size established as to whether or not the remedy predictability for the study is larger than the error predictability. In which it means that some more participants might be needed for the study if the researchers determine to facilitate the affect of the treatment that would happen to be smaller than the error variability (2006). Creative researcher systems as well as the raosoft are survey system companies that provides calculators in order to decide the model size intended for the research investigation. However, for a sample size...
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...QUANTITATIVE ANALYSIS: DESCRIPTIVE STATISTICS Introduction Suppose that we have carried out a survey on the effect of carrying out a management audit with three groups of nine participant institutions each i.e. small medium and large. Each group was given the same survey questions in questionnaire format and the answers from the scores were tagged between 0 and 20. What is to be done with the raw scores? There are two key types of measures that can be taken whenever we have a set of scores from participants in a given condition. First, there are measures of central tendency, which provide some indication of the size of average or typical scores. Second, there are measures of dispersion, which indicate the extent to which the scores cluster around the average or are spread out. Various measures of central tendency and of dispersion are considered next. For this assignment, a survey is the type of data collection method in consideration and how the results of that survey would be analysed. SURVEYS Surveys are a very popular form of data collection, especially when gathering information from large groups, where standardization is important. Surveys can be constructed in many ways, but they always consist of two components: questions and responses. While sometimes evaluators choose to keep responses “open ended,” i.e., allow respondents to answer in a free flowing narrative form, most often the “close-ended” approach in which respondents are asked to select from a range of...
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...31A00410 Quantitative Business Analysis Statistics Part, Autumn 2012 Problem set 5 The due date for this problem set is Thursday 06.12. at 14:00. Problem 1. A supplier of a raw material has agreed to deliver the material in packages of 20 kg each. As a part of quality control, a random sample of 10 packages were measured. The average weight of packages was 19.2 kg in the sample, and the standard deviation was 0.4. Test the null hypothesis that the expected weight of a package is 20 kg. The following exercises build on the previous Problem Set 4 and the data used therein. See Problem Set 4 for more detailed instructions regarding the data. Problem 2. In the previous problem set we estimated the following regression model using the data of hockey players in the Finnish league for the season 2009-2010: Goals = α + β × Shots-on-goal + ε The data for this exercise were provided in the Excel file “QBA Stats 4.xls” available on the course website. (a) Estimate the 95% confidence interval for the slope coefficient β. (This can be obtained directly by using the Excel Analysis ToolPak.) (b) Estimate the 99% confidence interval for the slope coefficient β. (The necessary statistics (critical t-value and standard error) can be computed with Excel, but you need to apply the formula for the confidence interval.) Problem 3. In another data set of hockey players in the NHL, you should have estimated a slope coefficient b = 0.1213. Test the hypothesis that β = 0.1213 in the model estimated...
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...Methodology In my thesis I utilized the quantitative content analysis method. There are many definitions of this research method from different authors. Most of the definitions include similar principles. The research should be systematical, objective, quantitative and replicable. Daniel Riff introduced in his book the major definition and compiled their aspects into his own. He says: “Quantitative content analysis is the systematic and replicable examination of symbols of communication, which have been assigned numeric values according to valid measurement rules, and the analysis of relationships involving those values using statistical methods, to describe the communication, draw inferences about its meaning, or infer from the communication...
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...Running Head: ANALYSIS AND IMPLICATIONS Analysis and Implications of Practice: Quantitative Research Analysis and Implications of Practice: Quantitative Research Why are Nurses Leaving? Findings From an Initial Qualitative Study on Nursing Attrition Carol Isaac MacKusic and Ptlene Minick Introduction/Purpose As the population ages and chronic disease runs rampant, the need for bedside nurses grows. MacKusick and Minic (2010) further tackle the nursing shortage in Why are Nurses Leaving? Findings from an Initial Qualitative Study on Nurse Attrition. The purpose of this study is clearly stated, “to understand the factors influencing the decision of registered nurses (RN’s) to leave clinical nursing” (p. 335). This purpose statement clearly states and highlights its importance to the nursing practice. Interviews were conducted to find out what factors influenced the decisions of the RN’s to leave clinical nursing. The introduction of this study gave a great summation of the entire article which pulled the reader in. Review of Literature In reviewing the article, it was discovered that the authors did a thorough search of bedside RN’s leaving the nursing practice and found that very limited data was available regarding nursing attrition. Their search began with “GoogleScholar and was narrowed to include CINAHL, Medline, PsychINFO, and LexisNexis MacKusic and Minic” (2010 p.335). MacKusic and Minic (2010) found the data search for this topic ended in 2007 when the...
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...Unit 5 – Regression Analysis Lakeia White American InterContinental University Abstract According to NLREG, “the goal of regression analysis is to determine the values of parameters for a function that cause the function to best fit a set of data observations that you provide.” (NLREG) As one continues to read one will find several different regression test that has been processed from AIU data set to assist them with their study on job satisfaction around the world. Introduction The following report contains the required data needed to find the regression analysis, there is three different test that has been processed regression analysis with benefits & intrinsic, regression analysis with benefits & extrinsic, and regression analysis with benefits & overall job satisfaction. As one continues to read one will find the ending results in each equation and how the results will benefit AIU. Benefits and Intrinsic Job Satisfaction Test #1: Regression Analysis-Benefits & Intrinsic: The line equation for the least square regression line is: y = 0.1697x + 4.4278 X = The independent variable which is Benefits’ Y = The corresponding dependent variable which is Intrinsic’ The slope (m) = 0.1697, and the intercept (b) = 4.4278 Therefore the Correlation Coefficient, r = 0.4061 and the Coefficient Determination, [pic] = 0.1649 [pic]. Benefits and Extrinsic Job Satisfaction Test #2: Regression Analysis-Benefits & Extrinsic: The equation for the Least...
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...Linear Regression Model 4 1.4 Multicollinearity 4 1.5 Conclusion for Multiple Linear Regression Modelling 5 2. Literature Review 5 2.1 Introduction of NBA 5 2.2 Reasons for High Average Salary in NBA 5 2.3 Salaries based on long term contract 6 2.4 Reasons for Choosing On-court Performance Data 7 3. Preparations for Running the Multiple Regression Model 8 3.1 Objectives of this paper 8 3.2 Introduction of the variables 8 3.2.1 Dependent Variable 8 3.2.2 Independent Variables 8 3.3 Data Source 9 3.4 Scattered Plots 9 3.4.1 Scattered Plots of Salary and Independent Variables 9 3.4.2 Residual Scattered Plots 10 4. Multiple linear regression modelling 11 4.1 The Adjusted R² 11 4.2 The Histogram 12 4.3 Model Generated by Analysis: 12 5. Ethical Problems 13 5.1 Sample Size 13 5.2 Data for Kobe Bryant 14 5.3 Excluded Related Independent Variables 14 5.4 Multi-collinearity 14 6. Conclusion 15 Abstract This paper examines the correlation between NBA players’ salaries and their on-court performance indicators. Before getting into the relationship, I would introduce the essence of what is regression model and how to interpret it, then we would move on to the introduction of NBA and how this model have been wildly used in testing the relationship between NBA players salaries and indicators. In the third part of this paper, regression models tell us that: firstly, age do not affect their salaries; furthermore, as they get better score in PER (play ‘efficiently’...
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...REVISED M04_REND6289_10_IM_C04.QXD 5/7/08 2:49 PM Page 46 C H A P T E R Regression Models 4 15 9 40 20 25 25 15 35 6 4 16 6 13 9 10 16 TEACHING SUGGESTIONS Teaching Suggestion 4.1: Which Is the Independent Variable? We find that students are often confused about which variable is independent and which is dependent in a regression model. For example, in Triple A’s problem, clarify which variable is X and which is Y. Emphasize that the dependent variable (Y ) is what we are trying to predict based on the value of the independent (X) variable. Use examples such as the time required to drive to a store and the distance traveled, the totals number of units sold and the selling price of a product, and the cost of a computer and the processor speed. Teaching Suggestion 4.2: Statistical Correlation Does Not Always Mean Causality. Students should understand that a high R2 doesn’t always mean one variable will be a good predictor of the other. Explain that skirt lengths and stock market prices may be correlated, but raising one doesn’t necessarily mean the other will go up or down. An interesting study indicated that, over a 10-year period, the salaries of college professors were highly correlated to the dollar sales volume of alcoholic beverages (both were actually correlated with inflation). Teaching Suggestion 4.3: Give students a set of data and have them plot the data and manually draw a line through the data. A discussion of which line is “best” can help them appreciate...
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...Ass Analytical chemistry What is Qualitative Analysis? Qualitative analysis is the aspect of analytical chemistry dealing with the identification of elements or compounds in an unknown substance. Very simply put, it answers the question "What is in this sample?" and usually does so with yes/no question. What is Quantitative Analysis? Quantitative chemical analysis is the aspect of analytical chemistry dealing with determining the quantity of a particular chemical is in a substance. In short, it attempts to answer questions involving "How much?" Accuracy: Accuracy refers to the agreement between experimental data and a known value. You can think of it in terms of a bull’s eye in which the target is hit close to the center, yet the marks in the target aren't necessarily close to each other. Accuracy is defined as, "The ability of a measurement to match the actual value of the quantity being measured". If in reality it is 34.0 F outside and a temperature sensor reads 34.0 F, then than sensor is accurate. Precision Precision refers to how well experimental values agree with each other. If you hit a bull’s-eye precisely, then you are able to hit the same spot on the target each time, even though that spot may be distant from the center. Precision is defined as, "(1) The ability of a measurement to be consistently reproduced" and "(2) The number of significant digits to which a value has been reliably measured". If on several tests the temperature sensor...
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...BUSN311: Quantitative Methods and Analysis The qualitative data used in this analysis broke down the numbers between men and women employees of the American Intellectual Union (AIU). The demographics of men far outweighed the women in this job with 40 men verses the smaller number of 12 women. This particular data was collected to express the number of each gender both male and female employees. The results clearly demonstrate that this profession is heavily dominated by males. This data could potentially identify links in job satisfaction in any given profession. The purpose of this study was to identify contributing factors associated with job satisfaction or dissatisfaction of both male and female employees in the AIU by demographic characteristic (Cano, Miller). Gender Breakdown 40 Males 12 Females Mean 1.230769231 Median 1 Mode 1 Standard Deviation 0.425436 Sample Variance 0.180995 The quantitative methodology used in the study of employees of the American Intellectual Union was intrinsic. Depending on the type of information required for this study, a parameter must use a constant that is most appropriate for the study. In this case, the intrinsic data focused on the level of job satisfaction. The range was from 1-7 with 7 being the highest level of satisfaction and 1 being the least satisfied. The range in this study fell mostly towards...
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...AIIAS BUAD635 Quantitative Analysis for Decision-Making Study Guide To accompany the prescribed text: Quantitative Analysis for Management by Render, Stair and Hanna, 11th edition, Prentice Hall, 2012 Unit # 1: Overview and Introduction to Quantitative Analysis Prescribed Text: Quantitative Analysis for Management by Render, Stair and Hanna, 11th edition, Prentice Hall, 2012 – Chapter 1 Objectives of unit 1: After completing this unit, students should be able to: 1. Describe the quantitative analysis approach for management 2. Demonstrate an understanding by applications of quantitative analysis in real world situations 3. Demonstrate the use of modeling in quantitative analysis 4. Use computers and spreadsheet models to perform quantitative analysis 5. Understand the limitations of quantitative analysis 6. Demonstrate/perform break-even analysis. Scope of coverage: Concepts Development 1. Overview of quantitative analysis 2. Defining quantitative analysis 3. The approach to quantitative analysis 4. A quantitative analysis model 5. Using spreadsheet for quantitative analysis 6. Limitation of quantitative analysis Introduction Quantitative analysis for decision-making is the application of a scientific approach to solve management problems. The purpose is to help managers make better decisions. Quantitative analysis encompasses a...
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... CLASS 2015 QUANTITATIVE METHODS PERSONAL ASSIGNMENT DATA ANALYSIS BY LORENZO CORONATI Prof. Maurizio Poli Via Bocconi 8 Office room: 517 (5th floor) E-‐Mail: maurizio.poli@sdabocconi.it 1 1. PRELIMINARY ANALYSIS The main scope of the work and the data analysis consist in developing a multiple linear regression model capable of demonstrate the function between ITC cost and the selected independent variables. All data in this work have been extrapolated from Dataset Eurostat Datawherehouse. The statistical units that have been studied are the 15 countries of the European Community as described in table 1. It has been utilized for the analysis a software called ...
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...BIMS Data Collection Jesse Gillen, Melroy Hyman, Joseph Boots, Michael Richards, Megan Hudspeth QNT/351 February 10th, 2015 Mohammad Sharifzadeh, Ph.D BIMS Research Analysis Introduction This report and the two studies conducted within it are to determine the reason for the increased turnover rate at Ballard Integrated Management Systems, Inc.. This report contains how the studies were conducted, the information that was gathered, interpretations of the data and recommendations for management. Study I Overview Ballard Integrated Management Systems, Inc. (BIMS) is experiencing an increased turnover rate with no clear answer why this is happening. The purpose of this study is to investigate why the turnover rate at BIMS has increased. The question that the research is attempting to answer is: What are the factors that may be contributing to the increased turnover rate in each region? Hypothesis Our hypothesis is that each region is going to show a spike of negative feedback to a specific problem. For example, employees working in the hospitality division may show more displeasure for their supervisor than the employees working in the food service division. This spike may indicate the reason for the increased turnover rate. BIMS One of the problems BIMS faces at its Douglas Medical Center site is a 4 percent increase in employee turnover. The root cause of the increased turnover rate has not yet been determined, and the exit interviews have been unsuccessful...
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...Unit 5 – Regression Analysis American InterContinental University Abstract In this scenario, Microsoft Excel has been utilized in order to perform a regression analysis therefore; each one has a chart in order to show the correlations in the data. However, satisfaction: overall, intrinsic, and extrinsic had been used. Introduction An analysis has been given to employees for the benefits satisfaction and compared to three different job types such intrinsic, extrinsic, as well as the over all. However, the regression analysis that was performed had been done in excel as well as there were charts made up. Benefits and Intrinsic Job Satisfaction Regression output from Excel Regression Statistics Multiple R 0.022301 R Square 0.000497 Adjusted R Square -0.0093 Standard Error 0.656922 Observations 104 ANOVA df SS MS F Significance F Regression 1 0.021902 0.021902 0.050753 0.822209 Residual 102 44.01771 0.431546 Total 103 44.03962 Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Lower 95.0% Upper 95.0% Intercept 5.270871 0.348709 15.11541 8.66E-28 4.579209 5.962532 4.579209 5.962532 X Variable 1 0.017947 0.079664 0.225284 0.822209 -0.14007 0.175959 -0.14007 0.175959 It did not want to add my 2 to the answer of 5.962532 or did it add the 9 to the answer of 0.175959 Graph Benefits and Extrinsic...
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...Problem 1 Decision rule: Reject if tSTAT> 2.0096 d.f. = 49 Test statistic: Decision: Since tSTAT> 2.0096, reject . There is enough evidence to conclude that the mean number of days is different from 20. (b) The population distribution needs to be normal. (c) The boxplot plot indicates that the distribution is skewed to the right. (d) Even though the population distribution is probably not normally distributed, the result obtained in (a) should still be valid due to the Central Limit Theorem as a result of the relatively large sample size of 50. Problem 2 H0: = 1. The mean amount of paint is 1 gallon. H1: 1. The mean amount of paint differs from 1 gallon. Decision rule: Reject if |ZSTAT| > 2.5758 (a) Test statistic: cont. Decision: Since |ZSTAT| < 2.5758, do not reject . There is not enough evidence to conclude that the mean amount of paint contained in 1-gallon cans purchased from a nationally known manufacturer is different from 1 gallon. (b) p-value = 0.0771. If the population mean amount of paint contained in 1-gallon cans purchased from a nationally known manufacturer is actually 1 gallon, the probability of obtaining a test statistic that is more than 1.7678 standard error units away from 0 is 0.0771. Problem 3 (a) Decision rule: Reject if |tSTAT| > 2.0555 d.f. = 26 Test statistic: Decision: Since |tSTAT| < 2.0555, do not reject . There is not enough evidence to conclude...
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