...Assess the strengths and limitations of using official statistics for investigating the effects of material deprivation on educational achievement. Material deprivation is a technical name for poverty and its effects on educational achievement. It is a theory put forward by sociologist Harker. Official statistics is a form of secondary data produced and published by the government and its agencies. These are collected in three main ways: government surveys, registration and record-keeping. The strengths of official statistics are that they are easily accessible and cheap. They are an up-to-date source of data which means the researcher doesn’t have to spend time and money collecting information. Official statistics can be useful in providing relevant statistics in areas such as household income. They cover large populations and are therefore representative, which is useful when conducting a large, quantitative study. This information can be linked to educational achievement by studying income alongside educational achievement rates in schools in poor and wealthy areas. Official statistics have shown that students who are from materially deprived backgrounds and who are entitled to free school meals and bus passes are the children who tend to have lower results when they finish school. Attendance is also a factor that has been found through official statistics, to affect how a student will achieve in school. They have also shown that EMA used to affect the achievement of...
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...US Persons Employed in Educational Administrative Services Introduction: Using historical data, predictions were made using Crystal Ball Predicator for persons employed in educational administrative services in the United States. The predictions were made by comparing the total number of persons in the US workforce, the number of men, and the number of women in educational services. The data set analyzed for Crystal Ball Predictor is comprised of historical data provided by the United States Census Bureau. The ranges of dates for the data begin in 2000 and ended in the year 2010. Respectively, the predictions began in 2011 and ended in 2020. The charts provided in this summary were generated by a report within Crystal Ball Predictor. Additional historical information and more recent data were gathered from the United States Bureau of Labor Statistics. The writer of this summary compared the Crystal Ball predictions with predictions provided by the US Bureau of Labor Statistics. While the percentages of the Crystal Ball Predictor slightly varied, the Crystal Ball Predictor and United States Bureau of Labor Statistics predictions provided similar conclusions. Crystal Ball Predictor and the US Bureau of Labor Statistics (How do they compare) Female compared to Males in Education Services The data set suggest that there are more female educational administrators compared to men, however, the data also suggest that the male population is steadily growing. Compared...
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...will incorporate both descriptive and inferential statistics to evaluate his or her results and create a credible conclusion. Descriptive statistics provides information focused on an immediate group of data. After defining what needs to be analyzed, the descriptive statistics will help the analyzer abridge the data to a more meaningful and comprehendible form, which will then provide patterns in his or her research that, will provide a foundation to his or her thesis. For example, a person could use descriptive statistics to evaluate the answers on an exam taken by 400 American students, and use descriptive statistics to determine the overall performance of the 400 students at that school. By using descriptive statistics, the analyzer can use his or her findings, to provide useful information regarding which subjects students need to improve most in, and which minority group or grade level are grasping the educational tools provided at the school more effectively, then those not grasping the provided educational tools and still need more room for improvement. While descriptive statistics helps an analyzer assess an immediate group of data from a single population, inferential statistics allow an analyzer to collect data using bits and pieces of samples which are portions of a collection of data focusing on the group or population of interest in which the analyzer research is concentrated on at the time. Inferential statistics will allow the analyzer to create a conclusion which...
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...BIPLAB SARKAR M.SC (2 yr.) in Statistics Email: biplab.stat@gmail.com Mathematics & Statistics Department Contact no: +91 8670327205 Indian Institute of Technology Kanpur D.O.B-11.10.1989 EDUCATIONAL QUALIFICATIONS Relevant Course: Regression Analysis, Time Series analysis, Analysis of Variance, Statistical & AI Data mining, Stochastic Processes , Linear Programming and Extensions , Computer Programming and Data structure, Econometrics, Economics Problem & policy, Multivariate Analysis , Sampling Theory , Game theory. EDUCATIONAL ACHIEVEMENTS & WORK EXPERIENCE: ϖ secured All India Rank 229 in the Joint Admission to M.Sc. (JAM) examination (2010). ❖ Application of ARCH, GARCH model Abstract: GARCH model has been used to understand and model large variability of the adjusted closing prices of S&P 500 index under Dr .Amit Mitra, Dept. of Mathematics & Statistics, IIT Kanpur ❖ Analyzing factors effecting crime rate Abstract: Analysis on how different causes effect crime rate of U.S.A using appropriate regression model. The areas covered are multicollinearity, variable selection, and residual analysis under Dr. Sharmishta Mitra, Dept. of Mathematics & Statistics, IIT Kanpur. ϖ Study of Short term Labor market statistics in OECD Countries using PCA, Classification tree & regression tree, clustering under Dr Amit Mitra. ϖStudy of GDP as a function of its components and lags...
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...Final Conclusion Memo Team B: RES/341 Nov 10, 2011 * Introduction The research conducted provided information to support the team’s collaborative efforts to provide a cause for the wage disparity between men and women. Credible information such as, the U.S. Census Bureau has presented statistics on women’s and men’s earnings for several decades. By analyzing a series of data, it was feasible to understand the trends in the wages. According to research analysis prepared by Consad Research Corporation, there is a difference. In pay directly associated with gender that dates back to three decades (Consad Research Corporation, 2011). Confidence Interval In addition to the Consad Research, information includes addressing data analysis using descriptive statistics, which included central tendency, dispersion, and skew data and statistical data using graphic and tabular techniques, was provided. A confidence interval was also computed to support the team’s conclusion. From our research the confidence interval is M 73.0 – 4.8 = 69.1 < M 73.9 > 73.9+ 4.8= 78.7Females earn 73.9% + 4.87 of male wages. The formulated problem statement supports a series of data collected from comparable scenarios. When we used statiscal analysis to project or reflect the earnings of women and men, we found no evidence on which we could base a prediction for a closing (or widening)...
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...coefficient on seat belt useage is now negative and the coefficient is statistically significant. The estimated value of .. βsb_useage is.0.00577, so that a 10% increase in seat belt useage (so that sb_useage increases by 0.10) is estimated to lower the fatality rate by .000577 fatalities per million traffic miles. States with more dangerous drving conditions (and a higher fatality rate) also have more people wearing seat belts. Thus (1) suffers from omitted variable bias. c. [pic] [pic] The results change. The coefficient on seat belt useage is now negative and the coefficient is statistically significant. The estimated value of βsb_useage=-0.0037 d. The time effects are statistically significant .. the F-statistic .. 10.91 with a p-value of 0.00. The results in (3) are the most reliable. e. A 38% increase in seat belt useage from 0.52 to 0.90 is estimated to lower the fatality rate by 0.00372* 0.38= 0.0014 fatalities per million traffic miles. The average number of traffic miles per year per state in the sample is 41,447. For a state with the average number of traffic miles, the number of fatalities prevented is 0.0014 *41,447 =58 fatalities. f. sb_useage= 0.206 * primary + 0.109 *secondary + (speed65, speed70, ba08, drinkage21, ln(income), age, time...
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...snaptutorial.com PSYCH 625 Week 1 Individual Assignment Basic Concepts in Statistics Worksheet PSYCH 625 Week 1 Individual Assignment Reliability and Validity Matrix PSYCH 625 Week 1 Individual Assignment Time to Practice – Week One PSYCH 625 Week 2 Individual Assignment Time to Practice – Week Two PSYCH 625 Week 2 Learning Team Assignment Statistics Project Import Data Into IBM ® SPSS ® Software PSYCH 625 Week 3 Individual Assignment Time to Practice – Week Three PSYCH 625 Week 3 Learning Team Assignment Hypothesis Testing Problem Worksheet PSYCH 625 Week 3 Learning Team Assignment Statistics Project Descriptive Statistics PSYCH 625 Week 4 Individual Assignment Time to Practice – Week Four PSYCH 625 Week 4 Learning Team Assignment Statistics Project Comparing Means PSYCH 625 Week 5 Individual Assignment Programmatic Assessment Time to Practice – Week Five PSYCH 625 Week 5 Learning Team Assignment Statistics Project Correlations PSYCH 625 Week 6 Individual Assignment Overview of Important Statistical Tests PSYCH 625 Week 6 Learning Team Assignment Statistics Project Presentation ----------------------------------------- PSYCH 625 Week 1 Individual Assignment Basic Concepts in Statistics Worksheet For more classes visit www.snaptutorial.com Complete the following questions. Be specific and provide examples when relevant. Cite any sources consistent with APA guidelines. What are statistics and how are they used in the behavioral sciences? Your answer should be...
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...Measurement scales A topic which can create a great deal of confusion in social and educational research is that of types of scales used in measuring behaviour. It is critical because it relates to the types of statistics you can use to analyse your data. An easy way to have a paper rejected is to have used either an incorrect scale/statistic combination or to have used a low powered statistic on a high powered set of data. * Nominal * Ordinal * Interval * Ratio Nominal The lowest measurement level you can use, from a statistical point of view, is a nominal scale. A nominal scale, as the name implies, is simply some placing of data into categories, without any order or structure. A physical example of a nominal scale is the terms we use for colours. The underlying spectrum is ordered but the names are nominal. In research activities a YES/NO scale is nominal. It has no order and there is no distance between YES and NO. and statistics The statistics which can be used with nominal scales are in the non-parametric group. The most likely ones would be: mode crosstabulation - with chi-square There are also highly sophisticated modelling techniques available for nominal data. Ordinal An ordinal scale is next up the list in terms of power of measurement. The simplest ordinal scale is a ranking. When a market researcher asks you to rank 5 types of beer from most flavourful to least flavourful, he/she is asking you to create an ordinal scale of preference...
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...probability and statistics are worked with in the business. Understanding how important statistics and its value to any business will lead the way to success. Overview of the Data Set The variables consist of both quantitative and qualitative data in regards to quantitative data that is known to have a numeric value as this data set includes age, job satisfaction and benefits (Cernauskas, Grey, Hemphill & Segal, 2011). The Qualitative data has a nonnumeric value and the variables for this data set consist of gender, position and department (Cernauskas et al, 2011). The two categories together provide the statistical data for the AIU staff analysis. Use of Statistics and Probability in the Real World The probability theory has survived for over three hundred years and it has grown to be a major part of everyday life (Vidal, n. d.). We have all made decisions based on the simplest activity such as picking straws or rolling dice. However, decisions are also based on likelihood of an event or simply by a hunch (Cernauskas et al, 2011). There are substantial ways that statistics are being used today ranging from areas in genetics, economics, and medicine. Another important use for statistics is for education; the National Center for Educational Statistics (NCES) provides analysis and reports on the data collected in regards to the condition of education in the US as well as the conducting and reporting of educational activities worldwide (National Center for Statistics, n. d.)....
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...this option please state you are my student and the class name and section or I will not accept invitation.) Faculty Service Center: (513)732-5335 Electronic Communication This is an online course which uses Blackboard linked to MyMathLab. Course Description: This course develops fundamental knowledge and skills for applying statistics to business decision making. Topics include descriptive statistics, probability distributions, confidence intervals and hypothesis testing and the use of computer software for statistical applications. Learning Outcomes: The successful Business Analytics I student should be able to: 1) Organize and summarize data using appropriate descriptive statistics and graphical methods. 2) Understand the concept of probability and to be able to calculate probabilities required in order to perform statistical inferences. 3) Understand the concept of a random variable and use discrete and continuous random variable and their corresponding distributions to calculate probabilities. 4) Understand the concept of a sampling distribution and be familiar with the primary sample statistics and their distributions. 5) Estimate population parameters using...
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...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 statistics in graph. 15 Dependency Ratio: how many people in your community who can support the dependents. Calcuation on page 225. Should do for both census tracts. 16 Data summarization facilitates ease of reading and spotting trends/patterns in data IV. Summary Statistics 17 Rates-vital statistics 18 Percentages-population characteristics 19 Ratios-sex,...
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...e of the reasons for ethnic differences in educational achievement. [12 marks] • Using material from Item A, and elsewhere, assess the contribution of functionalist sociologists to our understanding of the role of the education system in society. [20 marks] Research Methods The research methods section of this paper is one of the more straightforward sections that you will have. It consists of four questions that with the following values: 2 marks 4 marks 4 marks 20 marks Question 6 (2 marks) will be a basic identify and explain question, usually a methodological concept, e.g. describe and explain what is meant by validity. Question 7 and 8 will be identify and explain two aspects of something, for example: Suggest two advantages of using official statistics in sociological research. The best examples of which can be seen in the January, 2011, exam paper where the candidate has, very briefly, stated the advantages and offered a brief explanation and moved on. Question 9 is the essay question within research methods and is worth 20 marks. It is typically an examine question which will require you to look closely at the strengths and limitations of using that method. For example, what type of data will that specific method produce? Why is that type of data preferable? Why is it not preferable? Who would use that type of data? Who would not want that type of data? This is also the point where using the P E R V E R T anagram becomes useful: P –...
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...Topic Proposal Basic Math Skills and Scholastic Performnce among Grade III pupils of Dona Pilar Learning Center Foundation, Inc. Mathematics is the foundation of learners to improve their reasoning and thinking skill. It is widely recognized not only as a core component of the curriculum but also as a critical contributor to many educational and career opportunities (Scriphai, S. & Damongpanit, S.,et al, 2011). In reality, mathematics encompasses a wide variety of skills and concepts. These skills and concept are related each other but there’s a chance that you can easily master to some and still struggles with others, (Nathan V. Lauren, Sarah Lee Adam 2000). Early academic skills appear to be the strongest predictor of subsequent scholastic success – early math skills more so than early reading skills. http://news.uci.edu/features/kids-skilled-early-in-math-do-better-in-school/ In its report on the 2006 PISA results, the OECD outlines the importance of math skills in today’s world: With the growing role of science, mathematics and technology in modern life, the objectives of personal fulfilment, employment and full participation in society increasingly require that all adults, not just those aspiring to a scientific career, should be mathematically, scientifically and technologically literate. The performance of a country’s best students in mathematics and related subjects may have implications for the role that that country will play in tomorrow’s advanced...
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...Uses of Statistical Information Darryl Lowery Statistical Applications/HCS438 February 28, 2012 Julieanne Hessler, RN MSN, MBA Introduction Statistics are used in every phase in the delivery of health care. This is particularly true as it relates to the cost of providing health care services (Eaton, 2006). At Mercy Medical Center, not unlike any other health care facility, the use of statistics is pervasive throughout the organization. First and foremost Mercy uses statistics to develop and maintain its financial imperatives (Minnis, 2008). Simply stated if actual cost of providing health care services exceeds the revenue generated the organization will have difficulty keeping its doors open. This paper will discuss examples of descriptive and inferential statistics in use at Mercy Medical Center. Also discussed will be how data at nominal, ordinal, interval, and ratio levels of measurement are used within the organization. Finally, the advantages of accurate interpretation of statistical data and improved decision making within the organization will be discussed. Descriptive Statistics An example of a descriptive statistic used at Mercy Medical Center is time spent by the Emergency Department on yellow alert status. Yellow alert is defined by the Maryland Institute for Emergency Medical Services Systems (2012) as ambulance diversion from a designated emergency department that is unable to effectively manage additional patient volume at that time...
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...Other Topics Uses of Statistical Data In: Other Topics Uses of Statistical Data Uses of Statistical Information Darryl Lowery Statistical Applications/HCS438 February 28, 2012 Julieanne Hessler, RN MSN, MBA Introduction Statistics are used in every phase in the delivery of health care. This is particularly true as it relates to the cost of providing health care services (Eaton, 2006). At Mercy Medical Center, not unlike any other health care facility, the use of statistics is pervasive throughout the organization. First and foremost Mercy uses statistics to develop and maintain its financial imperatives (Minnis, 2008). Simply stated if actual cost of providing health care services exceeds the revenue generated the organization will have difficulty keeping its doors open. This paper will discuss examples of descriptive and inferential statistics in use at Mercy Medical Center. Also discussed will be how data at nominal, ordinal, interval, and ratio levels of measurement are used within the organization. Finally, the advantages of accurate interpretation of statistical data and improved decision making within the organization will be discussed. Descriptive Statistics An example of a descriptive statistic used at Mercy Medical Center is time spent by the Emergency Department on yellow alert status. Yellow alert is defined by the Maryland Institute for Emergency Medical Services Systems (2012) as ambulance diversion from a designated emergency...
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