...Statistics in Economics The Rivalry: Wins per Season Phillies vs. Mets Throughout the years two teams laid the groundwork for what would be considered one of the greatest rivalries of all time. Baseball is America’s pastime and one of my favorite sports. I decided to do an in-depth statistical analysis of both team’s histories regarding wins since 1965. Surprisingly both teams, the Philadelphia Phillies and the New York Mets, have strong numbers but the Phillies came out on top when comparing both data sets. The Phillies have a mean number of wins at 79.72 and the Mets with a mean of 77.28 wins. So, by looking at these data sets from a comparative angle, the Phillies come out on top with an average of about 2-3 wins per season. Taking a look at the medians, you are able to see that Phillies also have a higher mean of 80.5 wins as opposed to the Mets’ 78 wins. By looking at the easy to find statistical values you are able to see which team has been statistically better throughout the past 45 years. After I took a look at the descriptive statistics I ran three different tests: the F-test, the t-test, and the empirical tests to test for each data set’s normality. The first test that I ran in Excel was the F-test, which is a test comparing statistical models to identify the model that best fits the population from which the data was sampled. My results of the F-test came out with a high variance for each team: the Phillies with 133.41 and the Mets with 220.21...
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...Economics 30330: Statistics for Economics Problem Set 8 - Suggested Solutions University of Notre Dame Instructor: Julio Gar´ ın Spring 2012 Hypothesis Testing (80 Points) 1. Consider the following hypothesis test: H0 : µ ≥ 20 HA : µ < 20 A sample of 40 provided a sample mean of 19.4. The population standard deviation is 2. (a) Create a 95% confidence interval for the mean. We know σ, therefore we should use the z − table. This is a one-tailed (lower tail) test, so the 95% confidence interval will be given then by σ x − z.05 √ , ∞ ¯ n 2 19.4 − 1.65 √ , ∞ 40 The 95% confidence interval is µ ∈ [18.878, ∞). (b) What is the p-value? The p-value is the area in the lower tail. First, we calculate the z-value: z= 19.4 − 20 √ = −1.9 2/ 40 Using the normal table with z = -1.9, p-value =.0287. (c) At α = 0.01, what is your conclusion? p-value > .01, so we fail reject H0 at the 99% level. (d) What is the rejection rule using the critical value? What is your conclusion? c Reject H0 at the 99% level if z ≤ zα =-2.33. In this example, -1.9 > -2.33, so we fail to reject H0 at the 99% level. 2. Consider the following hypothesis test: H0 : µ = 15 HA : µ = 15 A sample of 50 provided a sample mean of 14.5. The population standard deviation is 3. 1 (a) Create a 95% confidence interval for the mean. We know σ, therefore we should use the z − table. This is a two-tailed test, so the 95% confidence interval will be given then by σ σ x − z.025 √ , x +...
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...Statistical Project Assignment | Statistics for Business & Economics | | DATASET 1: SIMPLE REGRESSION ANALYSIS Variable Definition Xi = Weight of car (pounds) Yi = Price of car ($) 1. (a) Regression Model using X to predict Y Weight and Price of Car Sales | | | | | | | | | | | | | Regression Statistics | | | | | | Multiple R | 0.212585295 | | | | | | R Square | 0.045192508 | | | | | | Adjusted R Square | 0.038951936 | | | | | | Standard Error | 7883.368653 | | | | | | Observations | 155 | | | | | | | | | | | | | ANOVA | | | | | | | | df | SS | MS | F | Significance F | | Regression | 1 | 450055137.6 | 450055137.6 | 7.241725381 | 0.007915154 | | Residual | 153 | 9508567701 | 62147501.31 | | | | Total | 154 | 9958622839 | | | | | | | | | | | | | Coefficients | Standard Error | t Stat | P-value | Lower 95% | Upper 95% | Intercept | 9854.041192 | 2894.819474 | 3.404026151 | 0.000847889 | 4135.063875 | 15573.01851 | Weight | 2.843766419 | 1.056751555 | 2.691045407 | 0.007915154 | 0.756058281 | 4.931474557 | Table 1 – Simple Linear Regression Model (Y and X) Simple linear regression equation Ŷi=b0+b1Xi From Table 1, we can see that b0 = 9854.0412 and b1 = 2.8438 Ŷi=9854.0412+2.8438Xi Figure 1 – Scatter Plot – Weight of Car vs Price of Car (b) Interpret the slope b1 measures the estimated change in the average value of Y as a result of...
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...Handelshögskolan i Stockholm 2013-04-23 Projektarbete på kurs NDH802 VT 2013 – Delrapport 1 1. Introduktion Projektarbetet syftar till att med statistik metodik, ge förmåga att sammanställa, beräkna sammanfattade mått och presentera olika slag av datamaterial. Syftet är också att ge förmåga att göra enklare sannolikhetsberäkningar samt på basis av statistisk bedömningsmetodik, dra slutsatser om okända egenskaper hos olika typer av populationer. Tonvikten läggs på förståelse och aktiv tillämpning av de statistiska modellerna och användning av statistisk programvara (SPSS). 2. Beskrivning av vårt primära kluster Beskriv ert val av variabler. Ni väljer själva vilka variabler som ni anser vara relevanta, men ni måste inkludera Q17_1 – Q17_23, Q25, Q27 – Q30 och Q34 samt ”dagar” och ”ålder”. 2.1 Varibel Q17 När man är på semester utomlands finns det möjlighet att ägna sig åt många olika aktiviteter. Nedan anges ett antal sådana aktiviteter. Vi ska nu demostrera i vilken utsträckning dessa aktiviteter intresserar individerna i vårt kluster. * Notera här att normalfördelningen kan användas för att approximera sannolikhetsfunktionerna för ett brett intervall av stokastiska variabler. * Det vi kan konstatera är att en stor andel av resenärerna i vårt segment föredrar att under semestern njuta av vädret, ha tillgång till en strand och ta del av det lokala köket. Det som inte uppskattas lika mycket under semestern är golf, ridning, segling, alkoholen och att umgås...
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...CORAL DIVERS RESORT Introduction Coral Divers Resort had a comfortable niche in the scuba diving industry, one that had been enhanced by its owner, Jonathan Greywell's promotional strategy. According to the case study,” over the years, Greywell had established a solid reputation for the Coral Divers Resort as a safe and knowledgeable scuba diving resort. It offered not only a diverse selection of diving activities, but a beachfront location. As a small well-regarded all-around dive resort in the Bahamas, many divers had come to prefer his resort to other, crowded tourists resorts in the Caribbean. "Greywell found this niche by creating short weekend and midweek diving ventures, a service that intrigued the public. Coral Divers Resort has targeted both the aficionado diver, and the tyro, both of which want maximum diving pleasure for minimum expense. The main issue in this case is what Greywell should do to enhance business, which has become increasingly flat. This paper shall consider some of the strategic options open to him, after first performing this abbreviated S.W.O.T. analysis. A classic method of performing competitive analyses of any new, emerging or maturing products the use of a SWOT analysis that stands for Strengths, Weaknesses, Opportunities, and Threats. This analysis of Coral Divers Club will use this method. STRENGTHS * The industry sector (sports diving) is strong and getting stronger.* Greywell has developed a good name and reputation in the industry.* the...
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...Journal of Economic Literature Vol. XXXIV (March 1996), pp. 97-114 The Standard Error of Regressions By D E I R D R E N . M C C L O S K E Y and STEPHEN T. ZILIAK University of Iowa Suggestions by two anonymous and patient referees greatly improved the paper. Our thanks also to seminars at Clark, Iowa State, Harvard, Houston, Indiana, and Kansas State universities, at Williatns College, and at the universities of Virginia and Iowa. A colleague at Iowa, Calvin Siehert, was materially helpful. T cant for science or policy and yet be insignificant statistically, ignored by the less thoughtful researchers. In the 1930s Jerzy Neyman and Egon S. Pearson, and then more explicitly Abraham Wald, argued that actual investigations should depend on substantive not merely statistical significance. In 1933 Neyman and Pearson wrote of type I and type II errors: HE IDEA OF Statistical significance is old, as old as Cicero writing on forecasts (Cicero, De Divinatione, 1. xiii. 23). In 1773 Laplace used it to test whether comets came from outside the solar system (Elizabeth Scott 1953, p. 20). The first use of the very word "significance" in a statistical context seems to be John Venn's, in 1888, speaking of differences expressed in units of probable error; Is it more serious to convict an innocent man or to acquit a guilty? That will depend on the consequences of the error; is the punishment death or fine; what is the danger to the community of released...
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...Running head: ECONOMIC FORECASTING 1 Economic Forecasting Apryl Wolfe, Avier Bashey, Mike Barette, Erik Nutt ECO/372 March 16, 2015 Wawa Ngenge Economic Forecasting There are many databases to research historical economic data and forecast future economic data. This week’s topics help us understand the data we are looking at, which once understood can help us improve the economic future within our country. Understanding our strengths and weaknesses will also help us find economic data to improve the economy. The Federal Reserve Economic Data, also known as FRED, The United States Census Bureau, Bureau of Labor Statistics or BLS, and Data.gov are just a few examples of great resources to gather historical economic data as well as economic forecast data. FRED FRED is an online database with thousands of economic data from different resources. It is maintained by the Research Department at the Federal Reserve Bank of St. Louise. FRED combines data with other tools that help users understand, interact and display the data. This is important because the data can become overwhelming and difficult to comprehend, but once learned you can learn the science of the economy. Census Bureau Most people think of the census bureau as only providing statistical numbers pertaining to the people of the US, it also provides quantitative data showing the figures from the economic census giving the total numbers of oil and gas production and many other types...
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...Institute of Management Sciences Peshawar Bachelors in Business Studies Course Plan Course Title: Statistics for Business Instructor: Shahid Ali Contact Email shahid.ali@imsciences.edu.pk Semester/Duration: 16 Weeks Course objectives : To introduce students to the concepts of statistics and to equip them with analytical tools to be used in business decision making. The course is intended to polish the numeric ability of the students to identify business problems, describe them numerically and to provide intelligible solutions by data collection and inferential principles. Course pre-requisites Intermediate statistics Attendance Policy: Late arrivals are highly discouraged. Any student coming late to a class late by 5 minutes after the scheduled start time will be marked as absent for the day. The teacher reserves discretion, however, to allow or disallow any student, to sit in the class in case of late arrivals. Attendance is not be entertained once the attendance register is closed. Class Project Students will be divided in groups for a class project. Each group will have to nominate a group leader. The details of the project will be made available to the group leader. Class Presentations Each student will have to make at least one individual presentation and one group presentation in the class. The group presentation will be on the project explained earlier. The individual presentations will...
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...Implementing entities and U Secretariat partners: DESA jointly with ECA (iii) Background Statistics are an important tool in the development-policymaking processes of countries and regional organizations. They are needed for assessing the current development situation, setting objectives and targets for the future and measuring progress and development. However, a substantial gap still exists between the demand for information and the ability of most countries in the Southern African Development Community (SADC) region to routinely provide it. The SADC Regional Indicative Strategic Development Plan recognizes statistics as one of the cross-sectoral areas that need to be strengthened to foster regional cooperation and integration over the next 15 years. This project is therefore designed to improve the availability and reliability of basic data required for development planning in the SADC region, with special emphasis on data requirements for the internationally agreed development goals and the Millennium Development Goals. The project is aimed at facilitating subsequent networking among subregions through interactive sharing and management of knowledge. Furthermore, the project will strengthen links between producers and users of statistics. The project builds upon lessons learned from three statistical development projects implemented by the Department of Economic and Social Affairs Statistics Division in the Caribbean Community, Association of South-East Asian Nations (ASEAN)...
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...TERM END EXAMINATIONS,MARCH-2013 BACHELOR OF COMMERCE, YEAR – III ELEMENTARY STASTISTICS Time: 3 hours M.Marks:60 SECTION A Note: - Attempt any 4 questions. All questions carry equal marks. (4 X 5) The answer should be limited upto 200 words. 1) What is statistics? Explain the nature and limitations of statistics? 2) What is frequency distribution? What are the different types of frequency distribution? 3) What is frequency curve? Explain cumulative frequency curve with example? 4) Suppose mean of a series of 5 item is30.four values are respectively, 10, 15, 30 and 35.estimate the missing 5th value of the series. ANSWER : Mean = (10+15+30+35+x)/5=30 Therefore, x=(30*50)-( 10+15+30+35) i.e x = 150-90, hence x=60 5) Calculate median of the following distribution of data. Class interval | 0-5 | 5-10 | 10-20 | 20-30 | 30-50 | 50-70 | 70-100 | frequency | 12 | 15 | 25 | 40 | 42 | 14 | 8 | n= 12+15+25+40+42+14+8=156 Hence median is at the average of n/2 & (n/2 +1) positon i.e 78th & 79th position Class interval | 0-5 | 5-10 | 10-20 | 20-30 | 30-50 | 50-70 | 70-100 | frequency | 12 | 15 | 25 | 40 | 42 | 14 | 8 | Position 12 27 52 92 134 148 156 6) Calculate the coefficient of correlation...
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...Statistics Statistics is used more often than people realize. They are used for many reasons such as to help one make a difficult decision in their personal or professional life. Statistics is also used to help companies promote their merchandise. Have you ever seen a commercial that used numerical information to show viewers that their product is preferred over their competitors’ product? That is just one of the many times one has probably seen statistics used without even realizing it. Statistics is the result of numerical information that is collected, classified, summarized, organized, analyzed, and interpreted. Once the data is collected and compared, one can draw a conclusion otherwise known as the statistic. There are two types of statistical applications in business, descriptive statistics, and inferential statistics. Descriptive statistics uses methods of organizing, summarizing, and presenting data in an informative and convenient form. Inferential statistics uses sample data to make estimates or predictions about a larger set of data (McClave et al. 2011). In business decision-making, statistics is used both internally and externally. Internally, business owners and managers use statistics to help them make important decisions such as wages, merchandise, operating hours, and the future of the company. For example, a company may view statistics between employees earning hourly and salary wages verse employees earning commission wages. The company can use their findings...
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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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...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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...QNT/351, June 01, 2015 Question 5: Descriptive Statistics: Your supervisor treated you fairly. Finding Data |Descriptive statistics | | | | | | | | | | | | | | | | | |Q5 | | | | | |cumulative | | | lower | |upper |midpoint |width | frequency |percent | frequency |percent | | |1 |< |2 |2 |1 |12 |15.4 |12 |15.4 | | |2 |< |3 |3 |1 |18 |23.1 |30 |38.5 | | |3 |< |4 |4 |1 |15 |19.2 |45 |57.7 | | |4 |< |5 |5 |1 |15 |19.2 |60 |76.9 | | |5 |< |6 |5 |1 |18 |23.1 |78 |100.0 | | | | | | | | | | | | | | | | | | |78 |100.0 | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |Descriptive Analysis Based on above survey question, the intention of descriptive analysis is to provide a clear understanding by use of graphical and numerical result to visualize unique trends of statistic data set. The survey question was engineered to...
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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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