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Words 1308

Pages 6

MBA 6018

Unit 4 Assignment 1

Scenario #1

You are the manager of the Gander Mountain store in Frogtown, Illinois. Recently, a customer mentioned that they believed your prices for ammunition were lower than the prices of Gander Mountain's primary competitor in the hunting equipment store, Cabela's. You would like to be able to include that statement in a forthcoming print advertisement, so you need statistical evidence to support this assertion.

Identify the null and alternative hypothesis needed to test the contention.

Null Hypothesis: Gander Mountain (u1) < Cabela’s (u2)

Alternative Hypothesis: Gander Mountain (u1) > Cabela’s (u2)

Utilizing the information from the outside consumer the null hypothesis that our brand (u1) prices are less than competitor bran (u2) is rejected, making the alternative hypothesis (u1) with higher prices to be accepted.

Identify the most appropriate sample section technique to gather data for testing the hypothesis.

Use a probability sample or simple random sample technique for both companies; generating random sample purchase dates and various ammunition purchases.

What statistical test should you use to accept or reject this hypothesis using the data you will collect?

If the standard deviation is unknown, we assume the t-test will work wince we have two independent samples. We could also use a t-test or z-test because they have equal value and both tests could be used if the independent sample size is large enough.

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 experimenting with dimple patterns that promote straight flight and feel they have achieved some degree of success. You, however, are worried about the effect that…...

...Solutions Manual for Statistical Inference, Second Edition George Casella University of Florida Roger L. Berger North Carolina State University Damaris Santana University of Florida 0-2 Solutions Manual for Statistical Inference “When I hear you give your reasons,” I remarked, “the thing always appears to me to be so ridiculously simple that I could easily do it myself, though at each successive instance of your reasoning I am baﬄed until you explain your process.” Dr. Watson to Sherlock Holmes A Scandal in Bohemia 0.1 Description This solutions manual contains solutions for all odd numbered problems plus a large number of solutions for even numbered problems. Of the 624 exercises in Statistical Inference, Second Edition, this manual gives solutions for 484 (78%) of them. There is an obtuse pattern as to which solutions were included in this manual. We assembled all of the solutions that we had from the ﬁrst edition, and ﬁlled in so that all odd-numbered problems were done. In the passage from the ﬁrst to the second edition, problems were shuﬄed with no attention paid to numbering (hence no attention paid to minimize the new eﬀort), but rather we tried to put the problems in logical order. A major change from the ﬁrst edition is the use of the computer, both symbolically through Mathematicatm and numerically using R. Some solutions are given as code in either of these languages. Mathematicatm can be purchased from Wolfram Research, and R is......

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...Basic statistical analysis is descriptive analysis, descriptive statistics summarizes responses for large numbers of respondents in a few simple statistics. When a sample is gathered, the sample descriptive statistics are used to make inferences about characteristics of the entire population of interests. Descriptive analysis is describing data in a way that explains the basic characteristics such as tendency, distribution, and variables. 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. Mean, Median, and Modes are descriptive statistics that is used to explain the basic properties of variables. 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. Tabulation 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. Cross Tabulation is used for addressing research questions involving relationships among multiple less than interval variables. One key factor in interpreting a cross tabulation table is comparing the observed table values with the hypothetical values that could result from pure chance. Percentage Cross......

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...information systems problems and how to investigate existing literature about hardware and software solutions to problems. 5. Know the components and functions of computer systems, both hardware and software. 6. Become familiar with the advances in networking, data communications and the Internet and how they affect the way business is conducted. 7. Identify which information technology tools are used to solve various business problems. 8. Develop proficiency solving business problems using modern productivity tools (e.g., spreadsheet, database) or creating custom programs. MIS 301: Statistical Analysis for Business At the end of this course students should be able to: 1. Use data from a sample to make inferences about a population. 2. Apply probability theory in decision making situations. 3. Formulate hypotheses for decision making and research. 4. Analyze data using appropriate statistical techniques. 5. Interpret the results of statistical analysis. 6. Present statistical results using graphics, text, and the spoken word. MIS 302: Introduction to Operations Management At the end of this course students should be able to: 1. Define the role of operations and supply chain in an organization and its interactions with business functions such as accounting, finance, and marketing. 2. Develop the basic business and operations strategies for increased productivity and competitiveness for service and manufacturing. 3. Use descriptive and optimization models and incorporate......

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...Hypothesis Testing Statistical Method Karl Phillip R. Alcarde MBA University of Negros Occidental-Recoletos DEFINITION DEFINITION Hypothesis testing or significance testing is a method for testing a claim or hypothesis about a parameter in a population, using data measured in a sample. In this method, we test some hypothesis by determining the likelihood that a sample statistic could have been selected, if the hypothesis regarding the population parameter were true. Hypothesis testing or significance testing is a method for testing a claim or hypothesis about a parameter in a population, using data measured in a sample. In this method, we test some hypothesis by determining the likelihood that a sample statistic could have been selected, if the hypothesis regarding the population parameter were true. The method of hypothesis testing can be summarized in four steps. 1. To begin, we identify a hypothesis or claim that we feel should be tested. For example, we might want to test the claim that the mean number of hours that children in the United States watch TV is 3 hours. 2. We select a criterion upon which we decide that the claim being tested is true or not. For example, the claim is that children watch 3 hours of TV per week. Most samples we select should have a mean close to or equal to 3 hours if the claim we are testing is true. So at what point do we decide that the discrepancy between the sample mean and 3 is so big that the claim......

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...Statistical Databases Jaideep Srivastava and Hung Q. Ngo, Department of Computer Science, University of Minnesota, 200 Union street, EE/CS Building, room 4-192, Minneapolis, MN 55455 e-mail: srivasta, hngo @cs.umn.edu, ¡ 1 Introduction A statistical database management system (SDBMS) is a database management system that can model, store and manipulate data in a manner well suited to the needs of users who want to perform statistical analyses on the data. Statistical databases have some special characteristics and requirements that are not supported by existing commercial database management systems. For example, while basic aggregation operations like SUM and AVG are part of SQL, there is no support for other commonly used operations like variance and co-variance. Such computations, as well as more advanced ones like regression and principal component analysis, are usually performed using statistical packages and libraries, such as SAS [1] and SPSS [2]. From the end user’s perspective, whether the statistical calculations are being performed in the database or in a statistical package can be quite transparent, especially from a functionality viewpoint. However, once the datasets to be analyzed grow beyond a certain size, the statistical package approach becomes infeasible, either due to its inability to handle large volumes of data, or the unacceptable computation times which make interactive analysis impossible. With the increasing sophistication of data collection......

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...The Top 50 Business Schools in the United States: A Statistical Paper by Morgan M. Smith Management 215-03 Professor Kirchner 18 April 2007 The top business schools in America are becoming more difficult to get accepted to. It seems almost impossible to get into schools like Harvard, University of Pennsylvania, Stanford, without having a parent who attended, or having a high socioeconomic status. The demographics of the top 50 business school in the United States are the topic of interest in this paper. The following demographics that were found, gathered, and analyzed were in-state vs. out-of-state students, gender, race, class ranking, and overall high school grade point average of the student population at these top schools. The top 50 business school in the United States are the following: Harvard, Stanford, UPenn, MIT, Northwestern, University of Chicago, Dartmouth College, University of California-Berkeley, Columbia University, NYU, University of Michigan-Ann Arbor, Duke University, UVA, Cornell University, Yale, UCLA, Carnegie Mellon, University of North Carolina-Chapel Hill, University of Texas-Austin, Emory, USC, Ohio State, Purdue University, Indiana University-Bloomington, Georgetown University, Georgia Institute of Technology, University of Maryland-College Park, University of Minnesota-Twin Cities, Michigan State University, Texas A&M University, University of Washington, University of Wisconsin-Madison, Washington University in St. Louis,......

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...Comparative Phylogeography of Freshwater Habitats in Southern Australia: Using Palaeodrainage Reconstructions to Investigate Population Structure and Historic Population Connectivity Sarah Jackson A thesis submitted in partial fulfilment for the degree of Bachelor of Animal and Veterinary Bioscience with Honours Department of Genetics School of Molecular Science La Trobe University October, 2011 Contents Abstract ........................................................................................................................... iii 1. Introduction .................................................................................................................. 1 1.1 Phylogeography....................................................................................................... 1 1.2 Palaeodrainage........................................................................................................ 4 1.3 Background on Study Species .................................................................................. 6 1.4 Aims and Hypothesis ............................................................................................... 8 2. Materials and Methods ............................................................................................... 10 2.1 Locations ............................................................................................................... 10 2.2 Expectations of Hypothesis.....................................

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...Executive Summary We find that there are some relationships with gender and salary in that firm referring to employee database (in Appendix ) because it is obvious that male employees' salary differ from female employees' salary. In order to confirm this doubt, it seems necessary to do some statistical analyses including a two-sample t test of male salaries against female salaries and a multiple regression to explain salary using age ,experience, and an indicator variable for gender . Through these statistical analyses, we can see that the discrimination truly exists in that firm and what affects the result of difference in salary between males and females. Introduction Just looking at the employee database, we can find easily that the average of females' salary is lower than average of males' salary by $9,000. However gender seems not be the only factor can affect the final result , some other things also looks important such as age , experience and training level. So our purpose is to indentify if gender is an important factor for final result and to exclude influences by other factors. Therefore, the result is as we expected, we are sure that the discrimination truly exists in that firm. Analysis and Methods This section begins with summaries of males' salary and females' salary. There are 28 females with average salary $39635.46 and 43 males with average salary $48726.84, From the histograms in Figure (a) and Box plot(b),we can see a big difference......

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...Jim Albert and Ruud H. Koning (eds.) Statistical Thinking in Sports CRC PRESS Boca Raton Ann Arbor London Tokyo Contents 1 Introduction Jim Albert and Ruud H. Koning 1.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.1.1 Patterns of world records in sports (2 articles) . . . . . . . 1.1.2 Competition, rankings and betting in soccer (3 articles) . . 1.1.3 An investigation into some popular baseball myths (3 articles) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.1.4 Uncertainty of attendance at sports events (2 articles) . . . 1.1.5 Home advantage, myths in tennis, drafting in hockey pools, American football . . . . . . . . . . . . . . . . . . . . . 1.2 Website . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.3 Acknowledgements . . . . . . . . . . . . . . . . . . . . . . . . . Reference . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Modelling the development of world records in running Gerard H. Kuper and Elmer Sterken 2.1 Introduction . . . . . . . . . . . . . . . . . . . . . 2.2 Modelling world records . . . . . . . . . . . . . . 2.2.1 Cross-sectional approach . . . . . . . . . . 2.2.2 Fitting the individual curves . . . . . . . . 2.3 Selection of the functional form . . . . . . . . . . 2.3.1 Candidate functions . . . . . . . . . . . . . 2.3.2 Theoretical selection of curves . . . . . . . 2.3.3 Fitting the models . . . . . . . . . . . . . . 2.3.4 The Gompertz curve in more......

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...reference to a named country, evaluate attempts to manage population change (15) During 1959 China experienced a catastrophic famine due to relaxed population control and the promotion of large families, due to this famine 20 million people died. As a result China’s Communist government under the power of Chairman Mao introduced a number of management policies, including the ‘later, longer, fewer’ program and the more extreme ‘one child policy’. In the early 1970’s a policy known as the ‘later, longer, fewer’ program was introduced. It was the first real attempt to control population growth in China. The authorized age of marriage was raised to 25 for men and 23 for women, whilst couples were encouraged to wait later to begin their families, allow for longer spacing in between children and have fewer children overall. Contraceptive advice became freely available in an attempt to elongate the time before the first child was born. The policy was partially successful. It began to reduce fertility rates, although not fast enough to really slow down population growth due to the demographic momentum that had already developed. The One Child Policy was launched in 1979 when the total population reached 1 billion. The initial goal was to stabilise China’s population at 1.2 billion, but due to the slow effects of the ‘later, longer, fewer’ program and the two child family, had to be revised to keep the population under 1.4 billion until 2010. There were a number of......

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...Chapter 10 Statistical Inference About Means and Proportions with Two Populations Learning Objectives 1. Be able to develop interval estimates and conduct hypothesis tests about the difference between two population means whenandare known. 2. Know the properties of the sampling distribution of . 3. Be able to use the t distribution to conduct statistical inferences about the difference between two population means whenandare unknown. 4. Learn how to analyze the difference between two population means when the samples are independent and when the samples are matched. 5. Be able to develop interval estimates and conduct hypothesis tests about the difference between two population proportions. 6. Know the properties of the sampling distribution of . ...

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...Descriptive and Inferential Statistical Method RES/351 Researchers have many tools available to them in order test and analyze a specific hypothesis. Some of these tools help gather data, while others help ensure accurate and relevant analysis. Data collection can take the form of quantitative and qualitative methods. In a qualitative method, your data is more interpretive. This data is used when trying to discover more of a meaning of specific question rather than the frequency (Cooper, Schindler 2014). This data is generally obtained through interviews, participant observation, or focus groups. On the other hand quantitative data is the precise measurement of a specific behavior or phenomena (Cooper, Schindler 2014). Quantitative data is generally gathered by experiments, standardized testing, surveys, or non-participant observation (Cooper, Schindler 2014). While gathering both types of data, it is important to focus on the type of sampling method you utilize. For example, simple random sampling, or probability sampling, can be used to test a targeted representation of a test population (Cooper, Schindler 2014). There are other sampling methods as well. Stratified random sampling, for instance, is probability sampling that draws from each strata of population. One study I found to demonstrate descriptive statistics used stratified random statistics. In this study, they used stratified random sampling to test accuracy in medical billing......

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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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...Experiment: Tentative Language We have a strong tendency to confuse facts and inferences by just simply listening to the words that might people say such as common names or verbs and interactions. As an exercise in becoming more sensitive to the difference between facts and inferences, I used tentative language to enhances my sensitivity in distinguishing the two types of statements. In the past twenty four hours, as I pay attention to every word I uttered in describing people and interactions, which I used tentative language, I found instances in which tentative language can be more accurate. In general, tentative language doesn't make absolute certainties, meaning you have to use limiting words, modal verbs and softening or hedging verbs in order not to be sound so factual and becomes more accurate. For example, after watching a concert last night and had a small conversation with a friend of mine. Instead of saying this words to him "The Lead vocalist of the band is very popular in Asia and is loved and respected by his fans" we can paraphrase it in this way "The Lead vocalist of the band appears to be very popular in Asia and seems to be loved and respected by his fans" in this way of saying it, you will lessen or taking out the confusion between facts and inferences. In relation, if you try to look at the first phrase, you will notice the lead vocalist is very popular to all the people in Asia. In addition, I used the word ‘appears’ as a softening or hedging verbs,...

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...important decisions such as job relocation and provide information for crime prevention. Some of the different groups that might be interested in our information could be public safety forces, recent college graduates entering the workforce, or even new parents in search of a safe city to raise a family. Section 2: Literature Review This topic has gained the attention of various publications in recent times. Researchers often use this topic to help them determine public safety efficiency; one such example is seen in the study named, “Measuring the Relative Efficiency of Police Precinct Using Data Envelopment Analysis” investigated in Taipei City, Taiwan. This study used DEA to examine “The relative efficiency of the fourteen police precincts in Taipei City, Taiwan”. Results indicated “How DEA may be used to evaluate these police precincts from commonly available police statistical data”. Our analysis however seeks to analyze and rank states across America based on crime rates instead of analyzing the efficiency police precincts. Unlike the found literature, our data is not considered to be information that solves for efficiency in that there is not positivity in crime rates. We have found that in the past similar data has been used to analyze various societal problems ranging from life decisions such as child development, job relocation, and personal safety. Section 3: Data Description The data was selected as part of the 2010 Census which defined......

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