...Unit 1 - Fundamentals of Statistics Abstract To examine and analyze the results of a survey by focusing on the qualitative and quantitative data. Introduction Data collection is extremely important. Many people use some form of data collection to help support an argument, for research, for planning purposes, etc. People use data collection for many different reasons. However when using data collection one must ensure that the data collected is accurate and unbiais. Chosen Variables The variables that I chose to analyze was gender and intrinsic. This data shows job satisifaction ratings from the male’s point of view and the female’s point of view. As a female in corporate America I was very interested to see how much of a difference would there be when it comes to job satisfaction from each gender’s point of view. We often hear that sometimes men are giving a better advantage over women so I would like to analyze the data to see if there is a significant difference between the two genders. Difference in variable types Qualitative variables and quantitative variables vary because of how it measured. Qualitative variables tend to have a greater influence on the outcome because of its ability to affect other parameters of the study. While quantitative variables are purely mathematical data or data that can be measured.. Qualitative research is typically suggested in earlier phases of research since the research may not know EXACTLY what he or she is...
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...Statistics: Highly Informative Latosha Greer BUS308: Statistics for 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...
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...Define Statistics More than 100 years ago H.G. Wells, an English author and historian, suggested that one day quantitive reasoning will be as necessary for effective citizenship as the ability to read”. This author was ahead of his times because it affected all the aspects of life, business and personal. Statistics is the science of collecting, organizing, presenting, analyzing, and interpreting data to assist in making a more effective decision. We use statistics everyday of our lives from the moment we wake up and start making decisions to the moment we put our heads back in the pillow at night to go to sleep. Almost every type of major requires us take a course of statistics. There are three major reasons; the first one is because there is numerical information everywhere, second one is that statistical techniques are used to make decisions that affect our daily lives. The third reason is that having the knowledge of statistical methods will help us have a better understanding of how decisions are made and how they affect us. Types and Levels of Statistics The types of statistics are, Descriptive Statistics and Inferential Statistics. Descriptive statistics is the method of organizing, summarizing and presenting the data in an informative way. The text use an example of a presentation of the population of the United States from the 1960’s till the present showing the increase on the population throughout the years. Inferential statistics is the method used to...
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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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...Statistical Analysis Report I. Introduction Accenture is a world leading management consulting, technology service and outsourcing company. It went public in 1998 and is listed on the New York Stock Exchange. In this report, I analyzed factors that relate to Accenture’s excess return, including the single index model (CAPM), three-factor model, and tests of the assumptions of OLS. II. Data and data description I collected monthly adjusted closing price of Accenture from July 2001(the earliest available date) to September 2014 and that of 13-week Treasury Bill, as well as Fama French Benchmark Factors (Rm-Rf, SMB, HML) of the same time period. The data description is showed in Table 2, I then tested properties of those variables. 1. Normal Distribution The test statistics of histogram presented in Table 2 follows a chi-square distribution with 9 degrees of freedom. The critical value at 5% significance level is 23.6. So the results show that Rm-Rf and SMB are normally distributed; while RACN-Rf and HML are not normally distributed. 2. Correlation (Multicollinearity) The correlation matrix presented in Table 3 shows that the excess return of Accenture is positive correlated with the three factors. Positive correlation with SMB indicates that Accenture behaves more like a small stock. The positive correlation between independent variables indicates there may be problem of multicollinearity, which need further test. III. Single Index Model (CAPM) I first built the single index...
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...Statistics as a discipline is the development and application of methods, and a collection of mathematical techniques that help to collect, analyze, interpret, and present data. Modern statistical methods involve associated tasks such as the designing and analyzing of experiments and surveys, the quantification of biological, social and scientific phenomenon and the application of statistical principles to understand more about the world around us. Statistics can also imply a second meaning, which is the computed quantity with the help of statistical methods. Thus, it could be said that the main statistics of a particular study are the median age and income of the group. Thus statistics can imply a statistical parameter as well. Statistics can be applied to various different problems and situations but the underlying concepts all remain the same. It can also be broadly classified into descriptive statistics and inferential statistics. The ideas of presenting data and drawing relevant inferences are central to the successful use of statistical theory. In the end, the statistical analysis should be able to tell us something concrete about the sample that we are studying. A number of errors are possible in the interpretation of statistical results and a careful analysis needs to be made to prevent these errors. Basically, statistics is applicable in a variety of fields, and business is not exclusive. Decision making in business is a complex thing. It is not something like “I...
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...According to teenpregnancystatistics.org, the drop out rate among pregnant teens is 90% throughout the United States ("Drop Out Rates Among Pregnant Teens."). This rate is even higher for African American teens or teens from disadvantaged backgrounds. Hikiela Williams was in a constant battle against these statistics. As a teen parent, Hikiela had to demonstrate work ethic and determination in order to achieve her goal of becoming a nurse. Her early life was not easy and several different events influenced her to becoming a nurse. Hikiela not only strived to become a nurse to provide for her family, but also to deliver the compassion to others that she once received during her various times spent in the hospital. With the achievements she has made in her career, Hikiela has become an inspiration to several individuals. Hikiela Williams is an inspiration to all future nurses or students entering the medical field because she demonstrates hard work, provides for her family, and is truly passionate about her career. Living in the slums of New Jersey and being surrounded by careless people, Hikiela was not provided with the most fundamental childhood. At the age of four, she was hit by a car and rushed to the hospital. It was in that hospital in which Hikiela experienced compassion....
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...Unit 1 - Fundamentals of Statistics Patricia Schneider American InterContinental University Abstract This paper is about the difference between qualitative data and quantitative data. If also will show how a qualitative data chart looks like and how the information is retrieved, it shows what type of information is put in a quantitative chart and how it is also retrieved. What standard deviation and variance is? Why charts and graphs are important tool for communicating facts and figures? Introduction The data that I chose for the qualitative data was the gender, and the quantitative data that I chose was the intrinsic. In this essay you will learn what the differences between qualitative data and quantitative data is? Why graphs and charts are so important in businesses and why they are used in communicating the facts? Chosen Variables The data that I have chosen is the gender and the intrinsic. The gender is qualitative data and the intrinsic is the quantitative data. Difference in variable types The difference between qualitative and quantitative variables is the qualitative has no value or is just a label where the quantitative has a value. The qualitative is a label there is no value of information to be measured. Qualitative is a non numerical measurement on a set of people or objects (Segal, 2011). Quantitative is numerical measurement for a set of people or objects (Segal, 2011). Descriptive statistics: Qualitative variable | | Qualitative by Gender...
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...are encouraged to actively access the interactive help of SPSS and ask your tutor questions if you get lost. You are expected to spend time outside of class (i) familiarising yourself with SPSS and (ii) completing the homework exercises to complete the Assignment 2 (Research Report) for assessment (20% of your MKTG7510 Grade). SPSS is accessible in all computer labs in Building 39A and 35. Please check current times of access to the computer labs and plan your study time accordingly. 1. How to create a new project? (1) Click on the ‘Start’ menu. Click on ‘All Programs’. (2) You will find the folder ‘Data Analysis and Stats Programs’ - click it to open the folder. (3) Click on the ‘IBM SPSS statistics’ folder. Click on the ‘IBM SPSS Statistics’ icon. (4) Once SPSS is open, the screen (see Fig. 1) asks you what you would like to do: either type in the data manually, or find an existing data file (as circled below). Figure 1: Create a New Project MKTG7510 Market and Consumer Research S1-2014 P. 1/20 2. What are the main components of an SPSS ‘project’? When you open a new ‘project’ you will see the Data Editor window where the raw data are visible. However, there are multiple windows that allow you to interact with the data in different ways. The following is a list of the main components of a ‘project’. i. Data Editor: screen containing all the data and information for one study. You can have multiple worksheets open when working in SPSS. However, the Data Editor...
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...Introduction: This project is a focus on how variables such as population, ethnicity, and income affect crime rates throughout different neighborhoods throughout a city. I feel that this information finding this information could be useful to many people. For example if you are looking to buy a new home or even start a new business, you’ll probably want it located in a safe neighborhood. This study will help identify the signs of a safe neighborhood. Knowing why crime rates are higher in some areas may also help prevent the crime rate to rise in other neighborhoods. For example, if crime rates are higher in neighborhoods with a higher percent of vacant houses, a city might give incentives for home buyers to buy houses in those neighborhoods to fill those vacant houses. Data and Variables: Most of my data came from the 2010 census, so my sample is only from 2010. With only one year of data some results may be skewed. I tried getting data from other censuses but it was not easily retrievable and some parts of the data were missing. The different quantitative variables I am using separated by neighborhood are median household income, total population, ethnic makeup (divided into white, Hispanic/Latino, Black/African America, ect…) total crimes, and housing units (vacant, occupied). The categorical variables I am using are neighborhood, community (group of neighborhoods, direction (North, South, East, West), income level (above or below average), and crime rates (above or below...
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...exclusive. That is, if one is true, the other must be false; and vice versa. Formulate an Analysis Plan The analysis plan describes how to use sample data to accept or reject the null hypothesis. It should specify the following elements. * Significance level. Often, researchers choose significance levels equal to 0.01, 0.05, or 0.10; but any value between 0 and 1 can be used. * Test method. Use the one-sample z-test to determine whether the hypothesized population proportion differs significantly from the observed sample proportion. Analyze Sample Data Using sample data, find the test statistic and its associated P-Value. * Standard deviation. Compute the standard deviation (σ) of the sampling distribution. σ = sqrt[ P * ( 1 - P ) / n ] where P is the hypothesized value of population proportion in the null hypothesis, and n is the sample size. * Test statistic. The test statistic is a z-score (z) defined by the following equation. z = (p - P) / σ where P is the hypothesized value of population...
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...Quantitative Techniques in Business Introduction to Statistics In the business world, and in fact, in practically every aspect of daily living, quantitative techniques are used to assist in decision making. Why? Unlike the classroom, in the “real world” there is often not enough information available to be guaranteed of making a correct decision. For instance, if advertisers would like to know how many households in the United States with televisions are tuned to a particular television show, at a particular date and time, it would be impossible to determine without the complete cooperation of every household and an astonishing amount of time and money. If a consumer protection agency wanted to determine the true proportion of prescription drug users who also use herbal non-regulated over-the-counter supplements, this information would most likely not be available. As a result of the inability to determine characteristics of interest, the application of statistics, and other quantitative techniques has developed. Statistics is defined as the process of collecting a sample, organizing, analyzing and interpreting data. The numeric values which represent the characteristics analyzed in this process are also referred to as statistics. When information related to a particular group is desired, and it is impossible or impractical to obtain this information, a sample or subset of the group is obtained and the information of interest is determined for the subset. For instance...
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...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, and to guide readers theme. In this article, there are some methods such as descriptive statistic, probablity distruction, possion ditruction and multivariate methods. For the descriptive statistic, they are tabular, graphical and Numerical Methods basic characteristics of the sample It can be described. While these methods may be the same Used to describe the entire group, they more...
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...2014 | Maggie L. Moore | Customers have begun to complain that the bottles of Lonice’s Soda contain less than the advertised sixteen ounces of soda. Today I asked my employees to randomly pull thirty bottles off the lines at random from all the shifts at the Lonice’s bottling plant. After asking them to measure the amount of soda in each bottle we came up with the following data. (See attached spread sheet). To calculate the mean, median and standard deviation for the ounces in the bottles I first imported the data in excel. After importing the data I use the Data analysis function where I choose descriptive statistics. Once choosing descriptive statistics I clicked ok and input my data range. Afterward I selected summary statistics and press OK. The forwarding data was provided; the mean equaling 14.87, median equaling 14.8, standard deviation equaling 0.550. To construct a ninety-five confidence interval for the ounces in the bottles I use the following information. X equal to 14.87 which is the mean of the data from the bottles, n equal to 30, because of the amount of bottles we use, standard deviation equals zero point five five zero which was calculate with the data from the bottles. I use the formula [ ] which gave me the lower limit of 14.673 and an upper limit of 15.067. Finally I conducted a hypothesis test to verify if the claim that a bottle contains less than sixteen ounces is supported. With the claim that the bottles contains less...
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...population. As to better represent the population’s age distribution, we should have ideally sampled participants with ages ranging between 17 & 65. In regards to the nationality, we would have preferred a higher percentage of Dutch participants, preferably around 75%; however we managed to retain an above average ratio of Dutch participants, specifically 56%. Conversely, to decrease the possibility of a subjective outcome our sample participants diversified greatly. 2. Coding the PITT scale (2 points) The survey includes the Personal Innovativeness in the Domain of Information Technology scale. 2.1 Did you transform any of the items in the scale before the analysis? How? Before we did the analysis we recoded the variable ‘in general I am hesitant to try out new information technologies’ to create consistency among our PITT questions. We changed the scale so 1 became 7, 2 became 6 etc. After this a high score on a PITT question indicates a more positive attitude towards information technologies. 2.2 What...
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