...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 (Buddahulsomsiri...
Words: 944 - Pages: 4
...cordial guidance, suggestions for preparing this report specially our class representative who provided us information about the procedure in making this report. At last, we thank Officer-in-charge in Omar-Sultan Computer lab that provided us important tips in using various computer applications, software in preparing this report. We acknowledge the special assistance provided by Md. Masud Ahmed, System Engineer of ELB Cyber Cafe, Khilghon and Dhaka. Executive Summary In the report we have tried to show the application of the statistical tools upon the general index or all share index of DSE for the year 2003. To achieve the purpose, first of all we have collected the data for the year 2003. Then we have edited the data for the accuracy test. As we have collected the secondary data directly from the DSE the accuracy of the information is in a reasonable standard. Then we have tried to show the application of the statistical tools which are presented in the body part of the report in different chapters. Those chapters contain Presentation of data, Measurement of central tendency, Measurement of dispersion & Major findings. The body part is also supported by a up going methodology and proper graphs and charts when they are subject to the topic. Lastly, the report contains a appendix part that shows the list of tables and graphs which will facilitate the readers to find out those tables and graphs related to the...
Words: 2852 - Pages: 12
...History of the Internet began a string of arms race between the United States and the Soviet Union. In 1957, the Soviet Union launched the first satellite Sputnik 1 that trigger their arms race between the Soviet Union and the United States when Dwight Eisenhower created ARPA agency (later named as DARPA) to lead in the technology. ARPA appointed JCR Licklider to head the IPTO a new organization with a mandate to further research the SAGE program to protect U.S. airspace from nuclear attack. At the IPTO, Licklider promote the idea has benefits communication network across the country and this affects the successor after Lawrence Roberts to take the work to implement the vision. Roberts led the development of new ideas based network packet switched (packet switching), which was discovered by Paul Baran at RAND and a few years later by Donald Davies at the UK National Physical Laboratory. A special computer called Interface Message Processor (Interface Message Processor) developed for the design and realization of the ARPANET began operating in early October 1969. The first communication between research done at the University of California at Los Angeles and the Stanford Research Institute. The first network protocol is used ARPANET Network Control Program (Network Control Program). In 1983, this protocol was replaced by TCP / IP, developed olehRobert Kahn, Vint Cerf and others. The use of this protocol thus be widespread throughout the world. In 1990, the ARPANET was retired...
Words: 747 - Pages: 3
...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...
Words: 604 - Pages: 3
...2000, Statistical Division, Ministry of Planning, Government of Bangladesh, Dhaka. BBS (2002); Statistical Year book of Bangladesh, 2001, Dhaka. Bunce, L. and R. Pomeroy. 2003. Socioeconomic Monitoring Guidelines for Coastal Managers in the Caribbean: SocMon Caribbean. GCRMN. Bunce, L., P. Townsley, R. Pomeroy, and R. Caribbean. GCRMN. Bunce, L., P. Townsley, R. Pomeroy, and R. Pollnac. 2000. Chandra KJ. Fish parasitological studies in Bangladesh: A Review. J Agric Rural Dev. 2006; 4: 9-18. Cochran, W.G. (1977); Sampling Techniques, 3rded. New Delhi: Wiley Eastern. Davis, James A. (1971). Elementary survey analysis. Des Raj (1971); Sampling Theory. Fox, J. 1984: Linear Statistical Models and Ravallion, M. and B. Sen 1996: When Method Matters: Monitoring Poverty in Bangladesh, Economic Development and Cultural Change, 44: 761-792 Gujarati, Damodar.N; Basic Econometrics. 4TH edition; Mcgraw-Hill. Gupta, S.C., Kapoor; Fundamental of Mathematical Statistics. New Delhi. Heyman, W. and R. Graham (eds.). 2000. The voice of the fishermen of Southern Belize, Toledo Institute for Development and Environment. Punta Gorda. Hogg, R. V. and A. T. Craig (2002): Introduction to Mathematical Statistics, 5th edition, Pearson Education (Singapore) Pte Ltd. Islam, M. Nurul; An Introduction to Sampling Methods: Theory and Applications. Jessen, R.J. (1978): Statistical Survey Technique. Mason, D., Robert and Douglas A. Lind (1996): Statistical Techniques...
Words: 340 - Pages: 2
...Bootstrapping (statistics) From Wikipedia, the free encyclopedia In statistics, bootstrapping is a method for assigning measures of accuracy (defined in terms of bias, variance, confidence intervals, prediction error or some other such measure) to sample estimates.[1][2] This technique allows estimation of the sampling distribution of almost any statistic using only very simple methods.[3][4] Generally, it falls in the broader class of resampling methods. Bootstrapping is the practice of estimating properties of an estimator (such as its variance) by measuring those properties when sampling from an approximating distribution. One standard choice for an approximating distribution is the empirical distribution of the observed data. In the case where a set of observations can be assumed to be from an independent and identically distributed population, this can be implemented by constructing a number of resamples of the observed dataset (and of equal size to the observed dataset), each of which is obtained by random sampling with replacement from the original dataset. It may also be used for constructing hypothesis tests. It is often used as an alternative to inference based on parametric assumptions when those assumptions are in doubt, or where parametric inference is impossible or requires very complicated formulas for the calculation of standard errors. Contents * 1 History * 2 Approach * 3 Situations where bootstrapping is useful * 4 Discussion * 4.1 Advantages...
Words: 4741 - Pages: 19
...Tutorial on Classification Igor Baskin and Alexandre Varnek Introduction The tutorial demonstrates possibilities offered by the Weka software to build classification models for SAR (Structure-Activity Relationships) analysis. Two types of classification tasks will be considered – two-class and multi-class classification. In all cases protein-ligand binding data will analyzed, ligands exhibiting strong binding affinity towards a certain protein being considered as “active” with respect to it. If it is not known about the binding affinity of a ligand towards the protein, such ligand is conventionally considered as “nonactive” one. In this case, the goal of classification models is to be able to predict whether a new ligand will exhibit strong binding activity toward certain protein biotargets. In the latter case one can expect that such ligands might possess the corresponding type of biological activity and therefore could be used as ‘’hits” for drug design. All ligands in this tutorial are described by means of an extended set of MACCS fingerprints, each of them comprising 1024 bits, the “on” value of each of them indicating the presence of a certain structural feature in ligand, otherwise its value being “off”. Part 1. Two-Class Classification Models. 1. Data and descriptors. The dataset for this tutorial contains 49 ligands of Angeotensin-Converting Enzyme (ACE) and 1797 decoy compounds chosen from the DUD database. The set of "extended" MACCS fingerprints is used as...
Words: 5674 - Pages: 23
...selecting the particular method of data collection for a selected product/service. The selected product that I will be collecting data for is an anti-depression medication made my HML (Hutchinsons Medical Limited). The data collecting method I’ve chosen is face to face interviews in order to get the respondents full attention and collect data without any distractions. I intend on finding out the respondent’s age, whether they suffer from depression/anxiety, whether they take medication for their depression/anxiety, and which form of our product they would prefer to purchase (roll on, cream, liquid, pills). The sampling method that I chose was probability sampling. I chose this method because it means everyone gets the chance to be picked meaning that the results will represent the whole group of interest. Another example of the fairness in this method is someone reaching into a box full of names and picking one out. The selection process ensures that everyone has a chance of winning. The advantages are that it’s not bias and as aforementioned it represents the whole group of interest. The results can also be used to calculate any further statistical analysis. I rejected non random sampling as it’s an unfair selection process as you get to pick people which makes in an unequal selection process. The people that are picked out might not represent the rest of the group and unlike probability sampling you can’t use the results to calculate any further statistical analysis. Unlike probability...
Words: 595 - Pages: 3
...elements of statistical sampling and other mathematical techniques. While, this unit provides opportunities for students to deal with situations involving probability, descriptive and inferential statistics, regression analysis and other basic mathematical tools used by business analysts at their workplaces, it intends to further expand student’s ability and interest into the area of economic research. Though the unit may seem mathematical at first there are certain characteristics that distinguish it from any mathematics unit. The unit provides students with the necessary tools to interpret results rationally. Finally, it provides the basis for a unit in econometrics at the higher level. The student shall be able to: • • • • • • • • • • • Differentiate the two general bodies of methods that together constitute the subject called statistics: descriptive and inferential statistics Understand the usage of graphical descriptive methods to summarise and describe sets of data understand the usage of numerical descriptive measures to summarise and describe sets of data Manage the data collection and sampling process Understand the basic concepts behind the rules and techniques of probability Acquire knowledge on the characteristics of probability distributions including binomial and other discrete probability distributions. Understand the importance of normal distribution and its practical usage. Link numerical descriptive statistics and probability distributions to statistical inference...
Words: 519 - Pages: 3
...Statistics Section 1-2 1. Statistical significance is indicated when methods of statistics are used to reach a conclusion that some treatment or finding is effective, but common sense might suggest that the treatment or finding does not make enough of a difference to justify its use or to be practical. Yes, it is possible for a study to have statistical significance but not a practical significance. If the source of the data can benefit from the results of the study, it is possible that an element of bias is introduced so that the results are favorable to the source. A voluntary response sample is a sample in which the subjects themselves decide whether to be included in the study. A voluntary response sample is generally not suitable for a statistical study because the sample may have a bias resulting from participation by those with a special interest in the topic being studied. Even if we conduct a study and find that there is a correlation, or association, between two variables, we cannot conclude that one of the variables is the cause of the other. There does appear to be a potential to create a bias. There does not appear to be a potential to create a bias. There does not appear to be a potential to create a bias. There does appear a potential to create a bias. The sample is a voluntary response sample and is therefore flawed. 2. 3. 4. 5. 6. 7. 8. 9. 10. The sample is a voluntary response sample and is therefore flawed. 11. The sampling method appears to be sound. 12...
Words: 3143 - Pages: 13
...about that depending on the data is nothing but an application of statistics. Sometimes this happens if assumptions of a statistical method are violated. For example, linear regression based on OLS estimation makes assumptions about the distribution of the variable you are trying to predict. It is also possible that a particular statistical measure isn't the best for understanding some situation. For example, average household income is heavily right skewed, which means median income is often more representative of a typical household income than the mean. Finally, sometimes statistics can be misrepresented due to incorrect sampling methods. One common mistake is to "sample on the dependent variable". For example, say a journalist is interested in how well a random set of 100 stocks performed over the past 20 years. If the journalist took a random sample of 100 stocks currently trading today, this would be incorrect and overstate returns due to "survivor bias". One would need to instead pull a sample of 100 random stocks that were trading 20 years ago, as some then might go bankrupt or merge under adverse circumstances, reducing the mean returns. Threading sample: I completely agree with your in depth analysis. You really do point out some of the most leading facts about the subject. Sometimes statistics can be misrepresented due to incorrect sampling methods. One common mistake is to "sample on the dependent variable". For example, say a journalist is interested in how well...
Words: 355 - Pages: 2
...Contract No.: ED-01-CO-0039 (0004) MPR Reference No.: 8936-600 The Effectiveness Of Educational Technology: Issues and Recommendations for the National Study Draft May 9, 2003 Roberto Agodini Mark Dynarski Margaret Honey, Education Development Center Douglas Levin, American Institutes for Research | | | |Submitted to: |Submitted by: | | | | |Institute of Education Sciences |Mathematica Policy Research, Inc. | |U.S. Department of Education |P.O. Box 2393 | |80 F Street NW |Princeton, NJ 08543-2393 | |Washington, DC 20208 ...
Words: 15926 - Pages: 64
...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...
Words: 1439 - Pages: 6
...Email: dklein99@terpmail.umd.edu Class Information Classroom: Van Munching Hall, Room 1330 Meeting Times: 10:00 a.m. - 1:10 p.m. Meeting Dates: June 2 - 5 (Monday – Thursday) June 9 - 12 (Monday – Thursday) June 16 – 19 (Monday – Thursday) Information regarding official university closings and delays can be found at the campus website or by calling the weather emergency phone line (301-405-7669). If a class is cancelled, the dates on the Course Outline will be changed to reflect this. Students will be notified of such changes by an email from me. Please refer to the inclement weather policy on page 3. Required Course Materials Text: Basic Statistical Ideas for Managers, 2nd ed, D. Hildebrand, R. Ott and J. Gray, Duxbury Press (Thompson-Brooks/Cole), 2005, ISBN 0-534-37805-6. The text comes with a CD-ROM containing an Excel Add-in and Data Sets. If your text does not have the CD, that is okay since I can post the data sets and we will not be using Excel. Optional Supplement Student Solutions Manual for Hildebrand, Ott and Gray, 2005, ISBN 0-534-38291-6. It is not necessary to buy the Student Solutions manual. It contains detailed solutions to some of the exercises in the book. This resource is for those students who want to look at detailed solutions to exercises not covered in class or assigned as HW. Whether or not a...
Words: 2335 - Pages: 10
...Vol. 2, No. 2 International Business Research Case Study of Factors Influencing Jobs Satisfaction in Two Malaysian Universities Edward Sek Khin Wong (Corresponding author) Faculty of Business & Accountancy University of Malaya, 50603, Kuala Lumpur, Malaysia E-mail: edwardwong@graduate.uwa.edu.au Dr Teoh Ngee Heng Universiti Tunku Abdul Rahman 46200, Petaling Jaya, Malaysia E-mail: tiohnh@utar.edu.my Abstract This work identifies the factors that measure job satisfaction of faculty members at two selected and major universities in Malaysia, using ten major factors corresponding to job satisfaction using the Herzberg Two-factor Theory to determine how these selected factors are related to job satisfaction of Malaysian faculty members. The conclusions drawn from this study are that the major sources of job satisfaction for Malaysian faculty members are shown to be policy, administration, and salary. The relevant sources of dissatisfaction are personal achievement, personal growth, interpersonal relations, recognition, responsibility, supervision, the work itself, and the overall working conditions. This study has a number of practical implications for institutional administrators, because if the educational institution has no instrument designed to measure faculty perceptions of their jobs and work, these administrators could elect to use the same instrument that investigates the areas of job satisfaction to gain similar results. Keywords: Higher education, Herzberg Two-factor...
Words: 7780 - Pages: 32
