...| |UNIVERSITI TUNKU ABDUL RAHMAN (UTAR) | | | | | |FACULTY OF BUSINESS AND FINANCE (FBF) | Teaching Plan | |Unit Code & |UBEQ1123 QUANTITATIVE TECHNIQUES II | | |Unit Title: | | | |Course of Study: |Bachelor of Commerce (Hons) Accounting | | | |Bachelor of Business Administration (Hons) | | | |Bachelor of Business Administration (Hons) Banking and Finance | | | |Bachelor of Business Administration (Hons) Entrepreneurship ...
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...Fin 700 Tianqi Sun Dr. Al. Barzykowsi Dec. 19, 2015 Short Paper - Statistical Methods This paper talks about statistical methods. Statistical data indicates that the agency 's approach is characterized by its population by inference Presented from a representative sample of the population views. As scientists rarely observed throughout Crowd, sampling and statistical inference is essential. This paper discusses some of the general principles Visualization of planning experiments and data. Then, a strong focus on the appropriate choice Standard statistical models and statistical inference methods. First of all, the Standard Model described. These models, in order to apply interval estimation and hypothesis testing parameters Also described, including the next two sample cases, when the purpose of comparing two or more of the population For their means and variances. Secondly, non-parametric inference tests are also described in the case where the data Sample distribution is not compatible with standard parameter distribution. Thirdly, using multiple resampling methods Computer -generated random sample finally introduced the characteristics of the distribution and estimate Statistical inference. The method of multivariate data processing of the following sections involved. method Clinical trials also briefly review process. Finally, the last section of statistical computer software discussion And through the collection of citations to adapt to different levels of expertise...
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...COURSE SYLLABUS AP STATISTICS Course Description: The purpose of this course is to introduce students to the major concepts and tools for collecting, analyzing, and drawing conclusions from data. Students are exposed to four broad conceptual themes: • Exploring Data: Describing patterns and departures from patterns • Sampling and Experimentation: Planning and conducting a study • Anticipating Patterns: Exploring random phenomena using probability and simulation • Statistical Inference: Estimating population parameters and testing hypotheses Students who successfully complete the course and examination may receive credit and/or advanced placement for a one-semester introductory college statistics course. Textbook: The Practice of Statistics, 3rd ed. (2008) by Yates, Moore and Starnes (Freeman Publishers) Calculator needed: TI-83 Graphing Calculator (Rentals Available) TI-83+, TI-84, TI-84+ are acceptable calculators as well Note: Any other calculator may/may not have statistical capabilities, and the instructor shall assist whenever possible, but in these instances, the student shall have sole responsibility for the calculator’s use and application in this course. AP STATISTICS Textbook: The Practice of Statistics, 3rd edition by Yates, Moore and Starnes Preliminary Chapter – What Is Statistics? (2 Days) A. Where Do Data Come From? 1. Explain why we should not draw conclusions based on personal experiences. 2....
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...Identify and apply appropriate statistical analysis: The statistical test that I decided to analyze was the F-Test. This test is used for comparing variances. The reason I chose this test was due to the fact that I am going to be using this same data on my term paper. I will be comparing runway incursions that took place between 2011 and 2012. The data from this group will be independent of each other. The F-Test can also be conducted with data that is unpaired. Data Collection: Techniques in data collection can be from the entire population, or it can be from sample data, which can be random or selected from the population. In the runway incursion F-test the data will be collected from the entire population. Review: The data that was collected for this test was taken from the FAA website, which gives runway incursion totals for each quarter. The given data is from the population. I have chosen the F-test because this will give us variance. Critique: There were a few other tests that could have been conducted, but the F-test was the one that would give me the best results. The T-Test would have been an option for me if I were comparing variance for a single sample. A second option would have been the Chi-test for independence, the problem with this test was I wasn’t testing for independence. I was testing for variance. The F-test was the best fit because I had more than one sample set that was independent from each other. Interpretation: This is where we analyze...
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...Chapter 10: Comparing Two Groups Bivariate Analysis: Methods for comparing two groups are special cases of bivariate statistical methods – Two variables exist: Response variable – outcome variable on which comparisons are made Explanatory variable – binary variable that specifies the groups Statistical methods analyze how the outcome on the response variable depends on or is explained by the value of the explanatory variable Independent Samples: Most comparisons of groups use independent samples from the groups, The observations in one sample are independent of those in the other sample Example: Randomized experiments that randomly allocate subjects to two treatments Example: An observational study that separates subjects into groups according to their value for an explanatory variable Dependent samples: Dependent samples result when the data are matched pairs – each subject in one sample is matched with a subject in the other sample Example: set of married couples, the men being in one sample and the women in the other. Example: Each subject is observed at two times, so the two samples have the same subject Categorical response variable: For a categorical response variable - Inferences compare groups in terms of their population proportions in a particular category - We can compare the groups by the difference in their population proportions: (p1 – p2) Example: Experiment: Subjects were 22,071 male physicians Every other day for five years, study participants...
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...Managers Instructor Hayes June 1, 2014 In this essay I am aim to discuss the differences between descriptive statistics and Inferential statistics and the reasons why we use them. I will also discuss hypothesis development and testing, when to select the appropriate statistical test, and how to evaluating statistical results. In this class I learned the difference between descriptive statistics and inferential statistics. We use descriptive statistics to measure and analysis data. There are a number of reasons why we use Descriptive statistics. We use it, because Descriptive statistics numerical summaries measure the central tendency of a data set, it can include graphical summaries that show the spread of the data, and they provide simple summaries about the sample that help interpret and analyze data. First, there are a number of reasons why we use descriptive statistics we use it because descriptive statistics numerical summaries that either measure the central tendency of a data set. In business therefore descriptive statistics helps in making conclusions about various issues and therefore helps in making decision. Description statistics is the first step in analyzing data before making inferences of data, therefore it is important in analyzing any data collected that will help in describing the characteristics of data collected. There are three measurements that we tend to use. One measurement is the mean. The mean is often referred to as the average. The average...
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...and explain the differences between a null hypothesis and an alternative hypothesis. Discuss the meaning of statistical significance. Use the Inference Toolbox to conduct a large sample test for a population mean. Compare two-sided significance tests and confidence intervals when doing inference. Differentiate between statistical and practical “significance.” Explain, and distinguish between, two types of errors in hypothesis testing. Define and discuss the power of a test. AP Outline Fit: IV. Statistical Inference: Estimating population parameters and testing hypotheses (30%–40%) B. Tests of significance 1. Logic of significance testing, null and alternative hypotheses; P-values; one- and two-sided tests; concepts of Type I and Type II errors; concept of power 4. Test for a mean (large sample -- ( known) What you will learn: A. Significance Tests for µ (( known) 1. State the null and alternative hypotheses in a testing situation when the parameter in question is a population mean µ. 2. Explain in nontechnical language the meaning of the P-value when you are given the numerical value of P for a test. 3. Calculate the one-sample z-statistic and the P-value for both one-sided and two-sided tests about the mean µ of a Normal population. 4. Assess statistical significance at standard levels α by comparing P to α. 5. Recognize that significance testing does not measure the size or importance of an effect. 6...
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...LOG OM 5300 Statistical Analysis for Business Decisions Syllabus Spring 2013 Section G01 (11477): T 1855-2135, 132 SSB Instructor: Dr. Alan C. Wheeler Office: ESH 230, 516-6136, awheel@umsl.edu Office hours: MW 1230-1330; T 1400-1500, 1745-1845; or by appointment Text: Statistics for Business and Economics, revised 11th edition, by David R. Anderson, Dennis J. Sweeney, and Thomas A. Williams, South-Western Calculator: hand-held with keys for natural logarithm, mean, and standard deviation Course Description: The role of statistical evidence in the formation of inference and in the selection of strategies in solving business problems is developed. Probability and probability distributions are studied as a basis of statistical inference. An introduction to multivariate analysis is provided, which includes analysis of variance and regression methods. Specifically, the course covers in order most of the material in the following chapters of the text: Chapter Topic 1 Data and Statistics 2 Descriptive Statistics: Tabular and Graphical Presentations 3 Descriptive Statistics: Numerical Measures 4 Introduction to Probability 5 Discrete Probability Distributions 6 Continuous Probability Distributions 7 Sampling and Sampling Distributions 8 Interval Estimation 9 Hypothesis Tests 10 Statistical Inference About Means and Proportions With Two Populations 11 Inferences About Population Variances 12 Tests of Goodness of Fit...
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...Comparing Populations Using Statistical Inference Sunday, September 20, 2015 MBA 6018 | Capella University Practical Application Scenario 1 You are the manager of the Gander Mountain store in Frogtown, Illinois. Recently, a customer mentioned they believed your prices for ammunition were lower than the prices of Gander Mountain's primary competitor in hunting equipment, Cabela's. You would like to include that statement in a forthcoming print advertisement, so you need statistical evidence to support this assertion. * Identify the null and alternative hypotheses needed to test the contention. The null hypothesis is that Gander Mountain’s prices for ammunition are lower than those of their top competitor, Cabela’s. The alternative hypothesis is that Gander Mountain’s prices for ammunition are not lower than Cabela’s. * Then, identify the most appropriate sample selection technique to gather data for testing the hypotheses. The best sample selection technique to use in this scenario would be random probability sampling. * What statistical test should you use to accept or reject this hypothesis using the data you will collect? I think that finding the average or mean prices of Gander Mountain’s ammunition would suffice. Practical Application Scenario 2 Your love of golf has brought you back to the range as the new product manager for UniDun's Straight Flight (SF) line of golf balls. The company's research and development group has been...
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...Uses of Statistical Information in the Workplace HCS 438 November 9, 2011 Uses of Statistical Information in the Workplace The collection, interpretation, analysis and presentation of data can be used in the workplace (Bennett, Briggs, & Triola, 2009). Predictions are made based on the data collected. The workplace is a healthcare organization, such as on a post-partum unit provide opportunities to collect, organize, observe and interpret information regarding this population. The purpose of this paper is to determine which methods of statistics are used as a measuring tool and what levels of measurements are used or could be used to improve the decision-making process for this organization. Statistics in the Workplace Many areas of the hospital facility use statistics in measurement. How do statistics affect this hospital healthcare facility in women services? One example is measuring nurse/patient ratios based on patient census and acuity. Patient acuity and census determines how many nurses are assigned to each patient based on their acuity. This also ensures patient and personnel safety. Patient satisfaction surveys are performed to improve patient care outcome as measured by HCAHPS (Hospital Consumer Assessment of Healthcare Providers and Systems); these tactics were discovered by the Studer Group. HCAHPS encourages leaders and staff members alike to realize the results are meaningful and that they translate to better, more consistent quality care, which...
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...STATISTICAL METHODS STATISTICAL METHODS Arnaud Delorme, Swartz Center for Computational Neuroscience, INC, University of San Diego California, CA92093-0961, La Jolla, USA. Email: arno@salk.edu. Keywords: statistical methods, inference, models, clinical, software, bootstrap, resampling, PCA, ICA Abstract: Statistics represents that body of methods by which characteristics of a population are inferred through observations made in a representative sample from that population. Since scientists rarely observe entire populations, sampling and statistical inference are essential. This article first discusses some general principles for the planning of experiments and data visualization. Then, a strong emphasis is put on the choice of appropriate standard statistical models and methods of statistical inference. (1) Standard models (binomial, Poisson, normal) are described. Application of these models to confidence interval estimation and parametric hypothesis testing are also described, including two-sample situations when the purpose is to compare two (or more) populations with respect to their means or variances. (2) Non-parametric inference tests are also described in cases where the data sample distribution is not compatible with standard parametric distributions. (3) Resampling methods using many randomly computer-generated samples are finally introduced for estimating characteristics of a distribution and for statistical inference. The following section deals with methods...
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...summarize the types, levels, and the role of statistics in business decision making, followed by examples of statistics in action. Types, Levels, and the Role in Business Decision Making The two categories of statistics are descriptive and inferential. Descriptive statistics is the analysis of data that describes, or summarizes the data in a meaningful way (Laerd Statistics, 2013). Business leaders can organize large amounts of data into a comprehensive format utilizing descriptive statistics. But descriptive statistics do not make conclusions about the data. An inferential statistic is calculated by taking a sample of the data from the population, and drawing a conclusion about the whole based on the small amount (Lind, Marchal & Wathen). Decision makers move forward based on a conclusion drawn from statistical inference. The data gathered for statistics is classified into four levels of measurement: nominal, ordinal, interval and ratio (Lind, Marchal & Wathen). Data at the lowest level is nominal and has a qualitative variable divided into categories or outcomes. Ordinal data is qualitative and represented by sets of labels or names such as bad, good, excellent. The interval measurement is utilized to gather quantitative data classified by the amount of a particular characteristic such as the temperature in a city. The highest level of measurement is quantitative data recorded in a ratio. The amount of the characteristic dictates the order (Lind, Marchal...
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...the information is part of the methodology of psychology. In the field of psychology, research is what makes it possible to better understand human behavior and gives the knowledge needed to help others. Research and statistics are tracking methods that must stay accurate. Research must be conducted with precision and accountability it is of utter importance, both ethically and morally or it can lead to disastrous results. The methods of primary, secondary data and the branches of statistical methods are key factors used in research. Research is an attempt to find information in a scientific manner (Word Net,2011). By using the scientific methods the results from the research are valid, reliable, and accurate. The scientific method is a process where secondary data is compiled, analyzed, hypothesized, and tested. Using this method decreases human error and subjective thinking from having a influence on the results and interpretations (Pervez & Gronhaug, 2005). While nothing is without fault by using the scientific method of hypothesizing and testing the hypothesis decreases the frequency and degree of errors from happening. Primary data is where it all starts. Primary data is the collection of raw information. Raw information simply means the data has never been used. The compiling of figures and numbers are used to make a final report. Secondary data is information used from the resources of primary data. Secondary data normally follows primary data and either...
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...One of the most basic statistical analysis is descriptive analysis. Descriptive statistics can summarize responses from large numbers of respondents in a few simple statistics. When a sample is obtained, the sample descriptive statistics are used to make inferences about characteristics of the entire population of interests. Descriptive analysis is the transformation of data in a way that describes the basic characteristics such as tendency, distribution, and variables. A examples of this would be if a company wanted to find out what type of bonus employees prefer. Descriptive statistics are used to explain the basic properties of these variables. One descriptive statistics that is used to explain the basic properties of variables is Mean, Median, and Modes. These terms all would be descriptive statistics for the above example by describing the central tendency in different ways. The mean would reflect the average answer that is given. The Median would provide the answer that is the central or middle range answer. The mode would be the answer that was given the most often. A second descriptive statistic that is used to explain the basic properties of variables is Tabulation. This refers to the orderly arrangement of data in a table or other summary format. When the tabulation process is done by hand, the term tallying is used. Simple tabulation tells how frequently each response or bit of information occurs. A third descriptive statistic used to explain the basic properties...
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...Week 4 - Reflection Learning Team “B” University of Phoenix Online QNT/351 Quantitative Analysis for Business April 13, 2014 The last four weeks have been challenging but also fulfilling for our team. We have formed a collaborative learning team that works well together using problem solving techniques and work experiences. Our collaborative reflection for the last four weeks is as follows: The Steps in Testing a Research Hypothesis Hypothesis testing begins with a statement and assumption that determines the population of the mean, (Lind, 2011, p.288.). The five steps listed in Lind are as follows: 1. State null and alternate hypothesis 2. Select a level of significance 3. Identify the test statistic 4. Formulate a decision rule 5. Take a sample and arrive at decision However in McClave, 2011, pages 324 and 325 the steps for testing of a hypothesis are listed as “Elements of a Test of Hypothesis” and instead of five steps they list seven. The seven steps from McClave are listed as follows: 1. Null hypothesis: A theory about the specific values of one or more population parameters. The theory generally represents the status quo, which we adopt until it is proven false. The theory is always stated as Ho: parameter = value. 2. Alternative (research) hypothesis (Ha): A theory that contradicts the null hypothesis. The theory generally represents that which we will adopt when sufficient evidence exists to establish its truth. 3. Test statistic: A sample...
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