...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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...Understanding Business Research Terms and Concepts: Part 4 Que McMannis Res 351 03/17/2015 Mark Robinson Abstract The three most commonly used research instruments in quantitative research Studies include Questionnaire, Tests, and Surveys. Validity is the degree to which an instrument measure what it is purports to measure. Imprecise information collection can influence the outcome of a study and eventually lead to unacceptable results. Reliability is the internal consistency or stability of the measuring device over time (Gay, 1996). The collection data of Quantitative methods depend on random sampling and organized data collection instruments that fit different involvement into fixed response groups (www.mbaisherebyravali.com). An important aspect of the quantitative research design is the non-probability method. Through the non-probability method, there are many sampling designs such as convenience sampling, purposive sampling and model instance sampling (www.wilderdom.com). Listed below are two Quantitative strength approaches. The first quantitative strength approach is offering vivid data; for example, permitting us to take pictures of an employer population (www.library.pinpoint.com). Another strength is quantitative research have the ability to translate numbers and data in quantifying graphs and charts. (www.library.pinpoint.com) The first weakness of the quantitative approach is large samples are needed thus causing the study to be more expensive, time-...
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...visit following link: http://wiseamerican.us/product/bis-155-week-7-ilab/ Contact us at: SUPPORT@WISEAMERICAN.US Week 7 iLab BIS 155 Scenario/Summary Hopefully you will find this week’s iLab activity fun and useful. We’ll be exploring the world of statistics from a business perspective this week, allowing you to practice your skills with descriptive statistics, formatting, graphs, and regression analysis. As discussed in the lesson, the value of statistics lies in the ability to analyze data more effectively for the purpose of improving decision making. You might have heard the expression that “statistics never lie, and only liars use statistics”. There is an obvious truth in this statement, in that, depending on the questions being asked and the data collected, the statistics can skew reality. For example, it is true that as ice cream sales increase, accidents at swimming pools increase. Does this mean that the more ice cream that is sold, the more accidents it causes (correlation/causation)? Of course not, but the data, if not interpreted correctly, could lead to false conclusions. It just so happens that both are correlated to a rise in temperature in the summertime. The hotter it is outside, the more kids flock to swimming pools, leading to more accidents, and the more ice cream is sold. So you see, although statistics are vital in the world of decision making, you have to be wise, and ask the right questions. Software Citation Requirements This course uses open-source...
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...Statistics in Business QNT 351 July 21, 2014 Statistics In Business Statistics is a type of mathematical analysis that involves the use of qualified models, summaries, and representations for a given set of empirical data or real world observations (Investopedia, 2014). Levels and Types of Statistics There are two different levels of statistics, descriptive and inferential statistics. Descriptive statistics involves methods of displaying, organizing, as well as describing data using graphs, tables, and summary measures. Inferential statistics describes the population that is based on the sample results. While descriptive and inferential statistics are the main levels; however there are different levels of measurement that is involved in statistics, that being said, there are four levels of measurement. Those levels include ordinal, nominal, ratio, and interval data. Each of these different measurements has different goals, which receives different results in surveys. The Goal of Statistics in Business Decisions Statistics are important when making decisions throughout a company, because businesses statistics that are analyzed properly will help to provide an established market analysis as well as research of competitors in the market. When the results of the statistical analysis are reported correctly, it helps companies to prepare for future as well as current plans based off of the information that is being research. Examples of Statistics One...
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...Hypothesis 1 – Average Income level 5 Descriptive Statistics 5 Hypothesis of the test 5 Statistical Findings 6 Interpretation of results 6 3.2 Question 2: Satisfaction level between gender 7 Descriptive statistics 7 Hypothesis 7 Method used and nature of the test 7 Reporting of statistical analysis 8 Interpretation of results 8 3.3 Question 3: Difference between satisfaction levels across business divisions. 9 Descriptive Statistics 9 Hypothesis 9 Method used and the nature of the test 9 Reporting of Results 9 Interpretation of results 10 3.4 Question 4: Gender composition across divisions 11 Descriptive Statistics 11 Hypothesis 11 Method used 11 Reporting of Statistical analysis 11 Interpretation of results 12 3.5 Question 5: Years of service and overall satisfaction level 12 Null and alternate hypothesis 12 Method used 12 Nature of test used: 13 Reporting of Results 13 3.6 Question 6: Employment basis and their perception of their values. 14 Descriptive Statistics 14 Hypothesis 14 Method used and the nature of the test 14 Reporting of Results 15 Interpretation of results 15 3.7 Question 7: Difference between values employees placed on income and training opportunities 16 Descriptive statistics 16 Hypothesis 16 Method used and nature of the test 16 Reporting of statistical analysis 17 Interpretation of results 17 3.8 Question 8: Value on work social events and numbers of hours 18 Descriptive statistics 18 Hypothesis 18 Method used and nature...
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...ABSTRACT An algorithmic information theoretic method is presented for object-level summarization of meaningful changes in image sequences. Object extraction and tracking data are represented as an attributed tracking graph (ATG), whose connected subgraphs are compared using an adaptive information distance measure, aided by a closed-form multi-dimensional quantization. The summary is the clustering result and feature subset that maximize the gap statistic. The notion of meaningful summarization is captured by using the gap statistic to estimate the randomness deficiency from algorithmic statistics. When applied to movies of cultured neural progenitor cells, it correctly distinguished neurons from progenitors without requiring the use of a fixative stain. When analyzing intra-cellular molecular transport in cultured neurons undergoing axon specification, it automatically confirmed the role of kinesins in axon specification. Finally, it was able to differentiate wild type from genetically modified thymocyte cells. Index Terms: Algorithmic information theory, Algorithmic statistics, Information distance, Gap statistic, Clustering. Various portions of this research were supported by the Center for Subsurface Sensing and Imaging Systems, under the Engineering Research Centers Program of the National Science Foundation (Award Number EEC-9986821), and by Rensselaer Polytechnic Institute. 1. INTRODUCTION Given a set of image sequences, we...
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...learn to cope with it. To minimize and control these unpleasant occurrences there is police. Police has mandate to ensure a safe life for citizens by controlling and minimizing occurrence of criminal activities, which is evident from reduced criminal cases year after year. The data on criminal cases are logged and a suitable statistical analysis can be made to see if really there is any significant drop in criminal cases in subsequent years. Statistics is the branch of knowledge in which data pertaining to different variables are either collected or generated by suitable experimental design. However, the data is generally very huge in volume and the useful information and underlying trends are hidden in the noise. Therefore, data needs to be processed to extract suitable information and underlying trends. There are many statistical procedures, which helps in extracting suitable information and underlying trends from a large pool of data. One such procedure is “Descriptive Statistics”, which provides useful statistics pertaining to a variable like mean, median, mode, standard deviation, kurtosis, skewness etc. These statistics provide information about central tendency, dispersion and skewness of the data and are very useful information about the variable under consideration. This test speaks about a single variable. Many times we need to know more about a pair of sample and also about the interrelationship between the two variables in the pair. It may be...
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...Introduction Regression analysis was developed by Francis Galton in 1886 to determine the weight of mother/daughter sweet peas. Regression analysis is a parametric test used for the inference from a sample to a population. The goal of regression analysis is to investigate how effective one or more variables are in predicting the value of a dependent variable. In the following we conduct three simple regression analyses. Benefits and Intrinsic Job Satisfaction Regression output from Excel SUMMARY OUTPUT Regression Statistics Multiple R 0.616038 R Square 0.379503 Adjusted R Square 0.371338 Standard Error 0.773609 Observations 78 ANOVA df SS MS F Significance F Regression 1 27.81836 27.81836 46.48237 1.93E-09 Residual 76 45.48382 0.598471 Total 77 73.30218 Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Lower 95.0% Upper 95.0% Intercept 2.897327 0.310671 9.326021 3.18E-14 2.278571 3.516082 2.278571 3.516082 X Variable 1 0.42507 0.062347 6.817798 1.93E-09 0.300895 0.549245 0.300895 0.549245 Graph Benefits and Extrinsic Job Satisfaction Regression output from Excel SUMMARY OUTPUT Regression Statistics Multiple R 0.516369 R Square 0.266637 Adjusted R Square 0.256987 Standard Error 0.35314 Observations 78 ANOVA ...
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...Data Analysis-Why? A. To study and examine data collected 1 Synthesis of data B. Identify community strengths C. Identify community health needs D. Determine need for further data collection: 1. find if research has been done. 2. Data gaps: determine need for further data collection. Make sure you can support data gaps with information from assessment. E. Look for trends/patterns; how often do you see a recurrent theme? F. Discovery of causative relationships: the R/T portion. I. Basic Steps of Data Analysis(4) 2 Categorize-e.g. by demographics, commonalities. E.g. intra/extra community for health and social services 3 Summarize 4 Compare 5 Inference/Interpretation II. Categorize Data 6 There are many ways to sort and categorize data e.g. demographically by age groups, by problem type 7 Geographic approaches may be used 8 Use of model; we are using the wheel from Neuman’s model. 9 Look for data convergence when categorizing-e.g. how many times do we see data converging in different categories? 10 Look for commonalties, health resources that are available. SEC, age, etc. III. Data Summary 11 Summary statements-summarize each table. 12 Summary statistics-put data into percentages and rates so that different areas/communities can be compared. Raw numbers will not work to compare different areas. 13 Graphic methods of data summary: 14 Remember that tables need concise summary data. P. 222, can put population...
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...Exploratory Data Analysis AJ Davis Project Part A January 18, 2013 Overview of Analysis 3 Individual Variable Analysis 4 Relationship between variables 8 Conclusion 10 Bibliography 11 Overview of Analysis AJ Davis is a department store chain, which has many credit customers and wants to find out more information about these customers. The department store provided a sample of 50 customers. The following data was provided for analysis: 1. Location a. Rural b. Urban c. Suburban 2. Income (measured in $1000’s) 3. Household size 4. Years in current location 5. Store credit balance Individual Variable Analysis Five individual variables were provided for analysis: Location, Income, Household size, Years at location and Credit balance. Below is a summary of the key points on the following individual variables: Location, Size and Credit Balance. Variable: Location The first individual variable summary presented is Location. The variable location has 2 subcategories from which data was reported: Rural, Urban and Suburban. As this is qualitative data only no measures of central tendency or descriptive statistics will be given. Based on the frequency distribution and the pie chart, the maximum number of customers from the sample live in Urban locations, with this variable representing 44% of the sample. Customers in the represented in the Rural areas is 26% and customers in the Suburban area represents 30% of the...
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...BA Homework 3 Part -1 Data summarization, exploration, and modeling. Data Exploration 1. Missing and/ or invalid values. There are missing values as na.fail displays there are missing values in the data. 2. Summary Statistics 3. Distribution The above Image shows distribution according to Income. Similarly, the data can be distributed based on various field criteria. 4. Conditional summary – Multivariate Analysis Scatter plots are the best possible way to carry out Multivariate analysis. This type of analysis can be carried out between different variables. 5. Correlation A week positive linear relationship between V13 and V15 (same with V11 and V15) 6. Direct your data exploration towards identifying the characteristics that may differ between those whose income is greater than $50K versus others. Solution: Age can be an important factor. Education level can also impact the salary. We will see this more in the classification tree. 2. Create a classification model using the CART algorithm in R (rpart package) to determine whether the income of an individual is greater than $50K. Identify the top 2/3 criteria that distinguish those whose income is greater than $50K and those whose income is less than $50K. Installing packages (rpart and rpart.plot) To predict V15 and classify further Thus “<= 50K” there are 24720 results. For >50K there are 7841 results (by using table function). Lets take an Ordered...
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...Data analysis report Divine Elegance: A Restaurant Table of Contents Introduction 2 Preliminary Analysis 2 Data Screening and Hypothesis Testing 2 1. How much are potential patrons willing to pay for the entrées? Is the $18 amount from the forecasting model correct? 2 2. Can Michael expect all patrons to spend an average of $200 a month on food? 4 3. Which zip code area(s) provide the best location for the restaurant? Does the expected average monthly spend differ between potential patrons residing in different zip codes 6 4. Does the likelihood to patronize the restaurant different between people with different income levels? 8 5.How elegant should the décor be? Would potential patrons prefer simple or elegant décor? 9 6. Should there be live entertainment? Would potential patrons prefer a string quartet or a jazz combo? 10 7. Which radio station(s) should Michael select for advertising? Which type of radio programming do people most likely to patronize the restaurant listen to? 10 8. Can the likelihood of patronizing the restaurant be explained in terms of evaluations on restaurant preference variables (Variables 11-20), age, family size and gender – when all of these variables are considered simultaneously? (*Hint, you may need to recode the ‘Year Born’ variable to determine age). 11 9. Does the average age between a probable and non probable patron differ? 13 10. Is there a relationship between gender and whether or not someone is a probable patron...
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...Chapter 1: Introduction – Defining the Role of Statistics in Business • Statistical Analysis: helps extract information from data and provides an indication of the quality of that information • Data mining: combines statistical methods with computer science & optimization in order to help businesses make the best use of the information contained in large data sets • Probability: helps you understand risky and random events and provides a way of evaluating the likelihood of various potential outcomes 1.1 - Why should you learn statistics? o Advertising. Effective? Which Commercial? Which markets? o Quality control. Defect rate? Cost? Are improvements working? o Finance. Risk – how high? How to control? At what cost. o Accounting. Audit to check financial statements. Is error material? o Other – economic forecasting, measuring and controlling productivity 1.2 – What is statistics? • Statistics: the art and science of collecting and understanding data o A complete and careful statistical analysis will summarize the general facts that apply to everyone and will also alert you to any exceptions. 1.3 – The Five Basic Activities of Statistics 1. Design Phase: will resolve these issues so that useful data will result a. Designing the Study involves planning the details of data gathering. Can avoid the costs & disappointment of find out – too late – that the data collected are not adequate to answer the important questions...
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...[Type the document title] | [Type the document subtitle] | | | [Pick the date] | [Type the abstract of the document here. The abstract is typically a short summary of the contents of the document. Type the abstract of the document here. The abstract is typically a short summary of the contents of the document.] | Contents Executive Summary 3 Introduction 3 Analysis and methods section 3 Do charges incurred by a patient depend on the type of insurance the patient has? If so how? 5 Do charges incurred by patients depend on which doctor treats them? If so how? 5 Bibliography 7 Appendix 8 Executive Summary Hospital are required to bill for individual items or services provided to a patient. Patients admitted to hospitals are charged for their room, supplies, drugs, labs, x-rays, operating room time and other care. It is important to know that hospitals submit a bill to the insurance company for all the services provided to the patient and the payor determines the amount owed to the hospital based upon the insurance company’s contract with the hospital for specific services [ (Henry Ford Health) ]. Introduction The purpose of this case study is to understand the relationship between hospital charges for certain physicians and insurance carriers. Cost finding and cost analysis are the techniques of allocating data that we were provided as part of the case study. The information that we are going to use to analyze cost between Commercial Care...
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...Ben Leigh American Intercontinental University Unit 5 Individual Project BUSN311-1301B-10: Quantitative Methods and Analysis Instructor Leonidas Murembya April 23, 2013, Abstract This paper will be discussing regression analysis using AIU’s survey responses from the AIU data set in order to complete a regression analysis for benefits & intrinsic, benefits & extrinsic and benefit and overall job satisfaction. Plus giving an overview of these regressions along with what it would mean to a manager (AIU Online). Introduction Regression analysis can help us predict how the needs of a company are changing and where the greatest need will be. That allows companies to hire employees they need before they are needed so they are not caught in a lurch. Our regression analysis looks at comparing two factors only, an independent variable and dependent variable (Murembya, 2013). Benefits and Intrinsic Job Satisfaction Regression output from Excel SUMMARY OUTPUT Regression Statistics Multiple R 0.018314784 R Square 0.000335431 The portion of the relations explained Adjusted R Square -0.009865228 by the line 0.00033% of relation is Standard Error 1.197079687 Linear. Observations 100 ANOVA df SS MS F Significance F Regression 1 0.04712176 0.047122 0.032883 0.856477174 Residual 98 140.4339782 1.433 Total 99 140.4811 Coefficients Standard Error t Stat...
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