...paper: |11:59 PM MST | | | | | | | | |Define the scientific method. How does it relate to human services research? | | | | | | | | | |What are the steps in the process of scientific inquiry? Why must each of these steps be | | | | |included to support the scientific method? Provide a human services research example of the | | | | |scientific method and identify each step within your example. | | | | | | | | | |Define quantitative research and qualitative research. Explain how they differ and relate | | | | |each to the human services field and the scientific method. | |...
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...BSHS 382 Week 5 Learning Team Statistics and Hypothesis Testing Presentation To Buy This material Click below link http://www.uoptutors.com/BSHS-382/BSHS-382-Week-5-Learning-Team-Statistics-and-Hypothesis-Testing-Presentation Prepare a 10- to 15-slide Microsoft® PowerPoint® presentation on statistics and hypothesis testing. This is a Microsoft® PowerPoint® presentation with speaker notes. Anorexia, as described on p. 383 of Statistics Enchiladas, as described on pp. 113 and 114 of Statistics FL Student Survey, as described on pp. 22 and 23 of Statistics Georgia Student Survey, as described on pp. 22, 23, and 151 of Statistics Olympic High Jump, as described on pp. 124, 125, and 128 of Statistics Introduction o Introduce the Learning Team members and the data set. o Briefly explain how the data was gathered and identify the study population. Descriptive Statistics o Define descriptive statistics and list the various descriptive measures. o Explain how descriptive statistical analysis increases understanding of the data. o Include an original graph created with StatCrunch that uses at least one descriptive statistical measure to illustrate the data set. Inferential Statistics o Define statistical inference and include and explain at least one original inferential statistical calculation. o Use StatCrunch to check the calculation and show the steps in your presentation. o Explain how inferential statistical analysis increases...
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...STATISTICS TEST Length: 1090 words (3.1 double-spaced pages) Rating: Red (FREE) - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - Statistics are necessary for scientific research because they allow the researchers to analyze empirical data needed to interpret the findings and draw conclusions based on the results of the research. According to Portney and Watkins (2009), all studies require a description of subjects and responses that are obtained through measuring central tendency, so all studies use descriptive statistics to present an appropriate use of statistical tests and the validity of data interpretation. Although descriptive statistics do not allow general conclusions and allow only limited interpretations, they are useful for understanding the study sample and establishing an appropriate framework for the further analysis in the study. Further analysis using appropriate statistical methods allows the researchers to establish correlations between independent and dependent variables, define possible outcomes, and identify areas of potential study in the future accurately. Statistics is important for researchers because it allows them to investigate and interpret the data more accurately, and researchers will notice patterns in the data that would be overlooked otherwise and result in inaccurate and possibly subjective conclusions (Portney &ump; Watkins, 2009). Frequency distribution is a method used in descriptive statistics to arrange...
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...Statistics in Business QNT/351 Aug 21, 2013 Edward Balian Statistics Investopedia defines statistics as a type of mathematical analysis involving the use of quantified representations, models and summaries for a given set of empirical data or real world observations. Statistical analysis involves the process of collecting and analyzing data and then summarizing the data into a numerical form. ("Investopedia", 2013) Types and Levels Descriptive statistics, inferential statistics, ratio-level data, interval- level data, ordinal-level data, and nominal-level date are some types and levels of statistics. Descriptive statistics utilizes numerical and graphical methods to look for patterns in a data set, to summarize the information revealed in a data set, and to present the information in a convenient form. Inferential statistics utilizes sample data to make estimates, decisions, predictions, or other generalizations about a larger set of data. Business decision making When it comes to the role of statistics in business decision-making it is applied in many ways in terms of consumer preferences or even financial trends. For example, managers across any type of business formulate problems, they decide on a question relating to the problem and then form a statistical formulation of the question is used to determine answers to all of the above. An example of a business question may be how many calls are answered...
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...SPSS Instruction Manual University of Waterloo Department of Statistics and Actuarial Science September 1, 1998 Table Of Contents Page Before Using This Manual……………………………………………………………………………….3 Introduction to SPSS……………………………………………………………………………………..4 SPSS Basics……………………………………………………………………………………………... 5 Tutorial 1: SPSS Windows.…………………………………………………………………………5 Tutorial 2: Starting A SPSS Session.……………………………………………………………...6 Tutorial 3: Getting Help on SPSS.………………………………………………………………… 6 Tutorial 4: Ending A SPSS Session.……………………………………………………………… 6 Creating and Manipulating Data in SPSS.……………………………………………………………. 7 Tutorial 1: Creating a New Data Set.……………………………………………………………... 7 Tutorial 2: Creating a New Data Set From Other File Formats.……………………………….10 Tutorial 3: Opening an Existing SPSS Data Set.………………………………………………. 16 Tutorial 4: Printing a Data Set.…………………………………………………………………… 16 Generating Descriptive Statistics in SPSS…………………………………………………………...17 Tutorial 1: Mean, Sum, Standard Deviation, Variance, Minimum Value, Maximum Value, and Range.……………………………………………………….. 17 Tutorial 2: Correlation.…………………………………………………………………………….. 18 Generating Graphical Statistics in SPSS……………………………………………………………..20 Tutorial 1: How to Generate Scatter Plots.………………………………………………………20 Tutorial 2: How to Generate A Histogram.………………….…………………………………... 22 Tutorial 3: How to Generate A Stem and Leaf Plot……………………………………………..23 Tutorial 4: How to Generate A Box Plot………………………………………………………….26 Statistical Models in SPSS……………………………………………………………………………...
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...Statistics in Business QNT/351 Statistics in business The purpose of this essay is to examine the purpose of statistics in business. Our text, Lind (2011) defines statistics as “The science of collecting, organizing, presenting, analyzing, and interpreting data to assist in making more effective decisions” (p.5). Types and levels of statistics There are two major types of statistics, descriptive and inferential. Descriptive statistics is defined by Lind (2011) as “methods of organizing, summarizing, and presenting data in an informative way” (p.6). An example of descriptive statistics would be a high school report showing that it had 300 graduates in 1990 and 450 graduates on 1991. The information that they provided described the amount of graduates that they had for each year. Inferential statistics is defined by Lind (2011) as “the methods used to estimate a property of a population on the basis of a sample” (p.7). If the same high school sent out a report showing the graduate numbers for 1999- the present to estimate the number of graduates that they would have for this school year, those statistics would be inferential because they are used to estimate future outcomes. There are four levels of statistical data: nominal, ordinal, interval and ratio. The nominal level deals with qualitative variables such as colors and blood types that can only be counted and classified. Ordinal data measurement is a variable rating system that ranks data according...
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... Introduction to Statistics LEARNING OBJECTIVES The primary objective of chapter 1 is to introduce you to the world of statistics, enabling you to: 1. Define statistics. 2. Be aware of a wide range of applications of statistics in business. 3. Differentiate between descriptive and inferential statistics. 4. Classify numbers by level of data and understand why doing so is important. CHAPTER OUTLINE 1.1 Statistics in Business Best Way to Market Stress on the Job Financial Decisions How is the Economy Doing? The Impact of Technology at Work 1.2 Basic Statistical Concepts 1.3 Data Measurement Nominal Level Ordinal Level Interval Level Ratio Level Comparison of the Four Levels of Data Statistical Analysis Using the Computer: Excel and MINITAB KEY TERMS census ordinal level data descriptive statistics parameter inferential statistics parametric statistics interval level data population metric data ratio level data nominal level data sample nonmetric data statistic nonparametric statistics statistics STUDY QUESTIONS 1. A science dealing with the collection, analysis, interpretation, and presentation of numerical data is called _______________. 2. One way to subdivide the field of statistics is into the two branches...
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...µ ≤ 50%. Team C will utilize the five-step hypothesis test on the CIA Global Demographics Data Set to accept or reject the null hypothesis as explained below. Five-Step Hypothesis Test Step 1 Step one of the five step hypothesis is to state the research problem in question form. In this case, I will define what will be tested and identify what variable will be used in the data collection. My hypothesis is: Do more than 50% of the population of both European Union countries and G-20 countries live past the age of 60? Step 2 During step two of the process, both the null and alternate hypotheses are recognized in both verbal and numerical form. The null hypothesis is that greater than 50% of the European Union and G-20 countries live past the age of 60.. This creates an alternative hypothesis that less than 50% of the European Union and G-20 countries die before the age of 60.. H0: µ = > .50 H1: µ NE > .50 alpha = .05 Step 3 The third step of hypothesis testing is to calculate a statistic analogous to the parameter indicated by the null hypothesis. Due to the sample size of 62 countries, the Z-test will be utilized to calculate the test statistic. The x bar is equal to the sample mean of 6.55 and the standard error of x bar is 1.83. Z= x bar - µ...
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...| Syllabus School of Business QNT/TM561 Version 2 Research and Statistics for Process Control | Copyright Copyright © 2009, 2005 by University of Phoenix. All rights reserved. University of Phoenix® is a registered trademark of Apollo Group, Inc. in the United States and/or other countries. Microsoft®, Windows®, and Windows NT® are registered trademarks of Microsoft Corporation in the United States and/or other countries. All other company and product names are trademarks or registered trademarks of their respective companies. Use of these marks is not intended to imply endorsement, sponsorship, or affiliation. Edited in accordance with University of Phoenix® editorial standards and practices. Course Description This course prepares graduate students to apply statistics and probability concepts to business decisions in organizations that focus on process improvement. Students learn criteria for developing effective research questions, including the creation of appropriate sampling populations and instruments. Other topics include descriptive statistics, probability concepts, confidence intervals, sampling designs, data collection, and data analysis—including parametric and nonparametric tests of hypothesis and regression analysis. Policies Students/learners will be held responsible for understanding and adhering...
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...------------------------------------------------- Ch 1 Introduction 1.1 Why Learn Statistics? * Statistics is the branch of mathematics that transforms numbers into useful information for decision makers. Statistics lets you know about the risks associated with making a business decision and allows you to understand and reduce the variation in the decision-making process. * Statistics provides you with methods for making better sense of the numbers used every day to describe or analyze the world we live in. * Statistical methods help you understand the information contained in “the numbers” and determine whether differences in “the numbers are meaningful or just due to chance. * Why learn statistics? First and foremost, statistics helps you make better sense of the world. Second, statistics helps you make better business decisions. 1.2 Statistics in Business * In the business world, statistics has these important specific uses: 1. To summarize business data 2. To draw conclusions from those data 3. To make reliable forecasts about business activities 4. To improve business processes * The statistical methods you use for these tasks come from one of the two branches of statistics: descriptive statistics and inferential statistics. * Descriptive statistics are the methods that help collect, summarize, present, and analyze a set of data. * Inferential statistics are the methods that use the data collected from a small...
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...Simulation with Arena Assignment G2: a multi-echelon inventory policy Christopoulou Evdoxia Kuodzevicius Bernardas 101283 534893 10/12/2012 Preliminaries The given Arena model is a steady-state model, because there is no clear event that could indicate the end of model run and actually we are interested in the long run behavior of the system represented by the given model. Before we start to do the main parts of the assignment, that is design of experiments and optimization, we conduct some preliminary experiments to check if the model could be modified in order to get better results. First we set the number of replications to 30 and we choose the length of each replication be 730 days (two years), which we think should be enough to reach the steady-state. When we run the model with these settings, we get that the mean of the main response variable - average cost - is 568.4 and the half width is equal to 2.37. Although the confidence interval is not extremely wide taking into account the relatively high value of mean, we still perform a check whether it is possible to get more precise results by using common random numbers. To figure out if the model would benefit from the use of CRN we perform a pilot study. In this study we need to have two different scenarios and then we can decide whether it is useful to use CRN by checking the following inequality: { } { } { } and if this inequality holds, then it is worth using CRN in the model. In our case the scenarios differ in three...
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...One-Sample Tests of Hypothesis LEARNING OBJECTIVES 1 Define a hypothesis. 2 Explain the five-step hypothesis-testing procedure. 3 Define Type I and Type II errors. 4 Define the term test statistic and explain how it is used. 5 Distinguish between a one-tailed and a two-tailed hypothesis. 6 Conduct a test of hypothesis about a population mean. 7 Compute and interpret a p-value. 8 Conduct a test of hypothesis about a population proportion. 10-2 Define a hypothesis. Explain the five-step hypothesis-testing procedure. Hypothesis and Hypothesis Testing HYPOTHESIS A statement about the value of a population parameter developed for the purpose of testing. HYPOTHESIS TESTING A procedure based on sample evidence and probability theory to determine whether the hypothesis is a reasonable statement. 10-3 The Null and Alternate Hypotheses NULL HYPOTHESIS A statement about the value of a population parameter developed for the purpose of testing numerical evidence. ALTERNATE HYPOTHESIS A statement that is accepted if the sample data provide sufficient evidence that the null hypothesis is false. 10-4 Important Things to Remember about H0 and H1 H0: null hypothesis and H1: alternate hypothesis. H0 and H1 are mutually exclusive and collectively exhaustive. H0 is always presumed to be true. H1 has the burden of proof. A random sample (n) is used to “reject H0”. If we conclude “do not reject H0”, this does not necessarily ...
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...itical values and p values Determination of critical values Critical values for a test of hypothesis depend upon a test statistic, which is specific to the type of test, and the significance level, , which defines the sensitivity of the test. A value of = 0.05 implies that the null hypothesis is rejected 5% of the time when it is in fact true. The choice of is somewhat arbitrary, although in practice values of 0.1, 0.05, and 0.01 are common. Critical values are essentially cut-off values that define regions where the test statistic is unlikely to lie; for example, a region where the critical value is exceeded with probability if the null hypothesis is true. The null hypothesis is rejected if the test statistic lies within this region which is often referred to as the rejection region(s). Critical values for specific tests of hypothesis are tabled in chapter 1. Information in this chapter This chapter gives formulas for the test statistics and points to the appropriate tables of critical values for tests of hypothesis regarding means, standard deviations, and proportion defectives. P values Another quantitative measure for reporting the result of a test of hypothesis is the p-value. The p-value is the probability of the test statistic being at least as extreme as the one observed given that the null hypothesis is true. A small p-value is an indication that the null hypothesis is false. Good practice It is good practice to decide in advance of the test how small...
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...Statistics in Business QNT/275 June 29, 2016 Statistics in Business According to "Define Statistics At Dictionary.com the science that deals with the collection, classification, analysis, and interpretation of numerical facts or data, and that, by use of mathematical theories of probability, imposes order and regularity on aggregates of more or less disparate elements. Quantitative vs. Quantities There are two general types of data. Quantitative data is information about quantities; that is, information that can be measured and written down with numbers. Some examples of quantitative data are your height, your shoe size, and the length of your fingernails. Speaking of which, it might be time to call Guinness. You've got to be close to breaking the record. Qualitative data is information about qualities; information that can't actually be measured. Some examples of qualitative data are the softness of your skin, the grace with which you run, and the color of your eyes. However, try telling Photoshop you can't measure color with numbers. Here's a quick look at the difference between qualitative and quantitative data. The age of your car. (Quantitative.) The number of hairs on your knuckle. (Quantitative.) The softness of a cat. (Qualitative.) The color of the sky. (Qualitative.) The number of pennies in your pocket. (Quantitative.) Remember, if we're measuring a quantity, we're making a statement about quantitative data. If we're describing qualities, we're making a...
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...Teradata Database Release Summary Release 12.0 B035-1098-067A March 2008 The product or products described in this book are licensed products of Teradata Corporation or its affiliates. Teradata, BYNET, DBC/1012, DecisionCast, DecisionFlow, DecisionPoint, Eye logo design, InfoWise, Meta Warehouse, MyCommerce, SeeChain, SeeCommerce, SeeRisk, Teradata Decision Experts, Teradata Source Experts, WebAnalyst, and You’ve Never Seen Your Business Like This Before are trademarks or registered trademarks of Teradata Corporation or its affiliates. Adaptec and SCSISelect are trademarks or registered trademarks of Adaptec, Inc. AMD Opteron and Opteron are trademarks of Advanced Micro Devices, Inc. BakBone and NetVault are trademarks or registered trademarks of BakBone Software, Inc. EMC, PowerPath, SRDF, and Symmetrix are registered trademarks of EMC Corporation. GoldenGate is a trademark of GoldenGate Software, Inc. Hewlett-Packard and HP are registered trademarks of Hewlett-Packard Company. Intel, Pentium, and XEON are registered trademarks of Intel Corporation. IBM, CICS, RACF, Tivoli, z/OS, and z/VM are registered trademarks of International Business Machines Corporation. Linux is a registered trademark of Linus Torvalds. LSI and Engenio are registered trademarks of LSI Corporation. Microsoft, Active Directory, Windows, Windows NT, and Windows Server are registered trademarks of Microsoft Corporation in the United States and other countries. Novell and SUSE are registered trademarks...
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