...CHAPTER 5. EMPIRICAL RESULTS, FINDINGS AND ANALYSIS 1. Over all graphical analysis For any index the best way to gauge its long term movement is to plot its movement over a period of time. So here to start with the analysis part , first the overall movement of the daily “close” data for S&P CNX NIFTY FIFTY is examined for the period starting from 2nd May 2002 till 3rd Feb 2012. There are in total 2347 observations and the econometric package EViews 7 has been used to track the movement. The plot is shown in Fig No 5.1. [pic] Fig No 5.1. Daily movement of Nifty Fifty “close” during 02/05/2002 – 03/02/2012 From the graph it is clear that Nifty has shown an upward trend over the period of time. While the upward trend is pretty evident from 2002 to 2007 however since 2007 Nifty movement has been somewhat unstable due to frequent market fluctuation and thus the market seems to be more volatile during this period. In terms of volatility another aspect is visible from the graph that is an upward trend is being followed by further upward trend while a downward trend is being followed by further downward trend and this feature is known as “volatility clustering” and this volatility clustering seems to be present in the index. More about the volatility and the movement of the index will be explored in the further subsections where the task of comparing Nifty movement at times is being taken. 2. Over all statistics The performance of Nifty over the years is tabulated...
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...Part 1. Basic Concepts of Statistics Basic Concepts of Statistics • Every four years, we suffer through an affliction, the presidential election. • Months before the election, public media will inform us that a poll conducted by the opinion research shows that a candidate gains support of more than 50 percent of voters. 1 2 Basic Concepts of Statistics • However, the high percent of support will be with a margin of error of plus or minus 3%. • What is meant by the term margin of error? • If you have an ambition to become president, you need to know something about statistics. • If you cannot perform statistics yourself, it would be better to hire a statistician right away. 3 Testing Hypotheses: One-sample tests • One-sample tests • Null hypothesis: – Ho: μ ≧0 • Alternative hypothesis: – Ha: μ ＜0 4 What is a Hypothesis? • A hypothesis is a claim (assumption) about a population parameter: – population mean Example: The mean monthly cell phone bill in this city is μ = $42 The Null Hypothesis, H0 • States the claim or assertion to be tested Example: The average number of TV sets in U.S. Homes is equal to three (H0 : µ = 3 ) • Is always about a population parameter, not about a sample statistic H0 : X = 3 6 – population proportion Example: The proportion of adults in this city with cell phones is π = 0.68 5 H0 : µ = 3 The Null Hypothesis, H0 (continued) The Alternative Hypothesis, H1 • Is the opposite of the null hypothesis ...
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...Project : Compressed Air Energy consumption reduction by 12%. DM A I C Presented to : CII Presented by : SKF Team Category : DMAIC - Utilities SKF Knowledge Engineering Company \ • 100 years of technology progress and innovations. SKF Group • 40,000 employees • 104 factories • 6.5 Bn US$ turnover • 83 production facilities • 1 out of 5 bearings in the world. October 30, 2007 © SKF Group Slide 1 SKF India • 2,000 employees • 4 production plants (Pune, • Bangalore, Haridwar & Ahmedabad) • Rs. 1,600 cr. turnover. • 1 out of 4 bearings in India SKF Bangalore (SDGBB) • 400+ employees • 6 Manufacturing channels • Rs. 315 cr. turnover. SKF & Sustainability Environmental Care @ SKF in India •Environmental Care Score Card for each location •CO2 Emission Reduction • • LightTheFuture Project - Reduction in Energy Consumption (each factory location) Number of projects focused on reduction of Energy Consumption (CO2 emission reduction) happened in • • • 2007 – 1 project closed 2008 – 8 Projects closed 2009 – 21 Projects closed All Factories Together (in Tons of Co2) 2007 2008 2009 w.r.t 2008 w.r.t 2007 29522.42 27172.40 24703.19 9.09% 16.32% 15359 13583.74 14158.93 -4.23% 7.81% Bangalore 3S 2539.01 2377.66 2160.43 9.14% 14.91% SKF India 47420.43 43133.81 41022.55 4.89% 13.49% Pune Bangalore DGBB October 30, 2007 © SKF Group Slide...
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...1. 2. QBUS5001 3. QBUSS5002 topic1 - 12 EXCEL 4. word ----- 5. zh.lai@foxmail.com R-XIANG George Jackie HD QBUS5001 ~ Jack 2015/6/7 目录 ........................................................................................................................................................................................... 1 TOPIC 1 ....................................................................................................................................................................................... 4 TOPIC 2 PROBABILITY ........................................................................................................................................................ 4 2.1 EVENTS & PROBABILITIES 2.2 JOINT ............................................................................................................................................ 4 MARGINAL& CONDITIONAL PROBABILITIES 2.3 PROBABILITIES TREES ....................................................................................... 4 .......................................................................................................................................................... 4 2.4 BAYER’S THEOREM ......................................................................................................................................................................... 4 2.5 POPULATION MEAN & VARIANCE ................
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...Using a spot the difference puzzle to find out whether being a lark or an owl affects your alertness in the morning or the evening. Results were then tested on significance using the Wilcoxon T test to decide whether the results found were reliable, or just down to chance. In theory, a lark should be more alert in the morning than in the evening compared to an owl who would be more alert in the evening. To test thins the participants were given two spot the difference puzzles and had to complete one in the morning and one in the evening. This in using the repeated measures method. This was tested on 49 participants who completed an MEQ (morning evening questionnaire) particapnats were tested in their homes at set times and given 1 minute to complete the spot the difference puzzles. Results were then tested on significance using the Wilcoxon T test to decide whether the results found were reliable, or just down to chance. Introduction The aim of the investigation was to find out the answer to the question ‘are people more alert at their preferred time of day?’ To test this, the procedures were based on the theory that A lark should be more alert in the morning and so should be able to score higher in the morning than the evening and an owl should be more alert in the evening than the morning therefore scoring higher than the larks in the evening. This hypothesis is a One-tailed Hypothesis because it is directional, these are the 4 possible hypothesis that can be tested...
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...One Way ANOVA using SPSS Introduction The one-way analysis of variance (ANOVA) is used to determine whether there are any significant differences between the means of two or more independent (unrelated) groups (although you tend to only see it used when there are a minimum of three, rather than two groups). For example, you could use a one-way ANOVA to understand whether exam performance differed based on test anxiety levels amongst students, dividing students into three independent groups (e.g., low, medium and high-stressed students). It is important to realise that the one-way ANOVA is an omnibus test statistic and cannot tell you which specific groups were significantly different from each other; it only tells you that at least two groups were different. Since you may have three, four, five or more groups in your study design, determining which of these groups differ from each other is important. You can do this using a post-hoc test (N.B., we discuss post-hoc tests later in this guide) Example A manager wants to raise the productivity at his company by increasing the speed at which his employees can use a particular spreadsheet program. As he does not have the skills in-house, he employs an external agency which provides training in this spreadsheet program. They offer 3 courses: a beginner, intermediate and advanced course. He is unsure which course is needed for the type of work they do at his company, so he sends 10 employees on the beginner course, 10 on the intermediate...
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...Assignment 2 Part A QUESTION 1 Table 1: Descriptive Statistics of Age | | n | Minimum | Maximum | Mean | Std. Deviation | Age (years) | 250 | 20 | 59 | 39.16 | 10.438 | Based on Table 1, the respondents in the sample have mean age of 39.16 years with a standard deviation of 10.438 years. The 95% confidence interval on the mean age is calculated as below: Y±1.96σnN-nN-1 =39.16±1.9610.4382502000-2502000-1 =39.16±1.96 0.62 =39.16±1.22 =37.94 , 40.38 Conclusion: We are 95% confident that the mean age of the population of the respondents with smoking habits is between 37.94 years and 40.38 years. QUESTION 2 Table 2(a): Descriptive Statistics of Income for Male | | n | Minimum | Maximum | Mean | Std. Deviation | Monthly Income ($) | 120 | 1800 | 5800 | 3605.83 | 1171.962 | Table 2(b): Descriptive Statistics of Income for Female | | n | Minimum | Maximum | Mean | Std. Deviation | Monthly Income ($) | 130 | 1800 | 5800 | 3746.15 | 1244.844 | Table 2(a) shows that the mean and standard deviation of the income for male respondents are $3, 605.83 and $1, 171.96 respectively. Table 2(b), on the other hand, shows that the mean and standard deviation of income for female respondents are $3, 746.15and $1, 244.84. (i) The sample mean is calculated as below: YST= NiYiN = N1Y1+ N2Y2N = 11003605.83+9003746.152000 = 73379482000 = 3668.974 (ii) (iii) The standard error is calculated as below: SE= 1NNi2Ni-niNi-1si2ni ...
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...0 0 a Equal-variances test statistic Rejection region: –2.074 or 2.074 = = .43, p-value = .6703. There is not enough evidence to infer that the population means differ. b Equal-variances test statistic Rejection region: –2.074 or 2.074 = = .04, p-value = .9716. There is not enough evidence to infer that the population means differ. c The value of the test statistic decreases and the p-value increases. d Equal-variances test statistic Rejection region: –1.960 or 1.960 = = 1.53, p-value = .1282. There is not enough evidence to infer that the population means differ. e The value of the test statistic increases and the p-value decreases. f Rejection region: –2.074 or 2.074 = = .72, p-value = .4796. There is not enough evidence to infer that the population means differ. g The value of the test statistic increases and the p-value decreases. 13.7 a Unequal-variances estimator = 64.8 (rounded to 65, approximated by ) = (63 – 60) 1.667 = 3 4.59 b Unequal-variances estimator = 63.1 (rounded to 63, approximated by ) = (63 – 60) 1.671 = 3 10.38 c The interval widens. d Unequal-variances estimator = 131 (approximated by ) = (63 – 60) 1.656 = 3 3.22 e The interval narrows. 13.8 = 0 0 a Unequal-variances test statistic = 200.4 (rounded to 200) Rejection region: = = .62, p-value = .2689. There is not enough evidence to infer that is greater than . b Unequal-variances test statistic = 223.1 (rounded...
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...RSSMAIN and RSSTMT → Calculate but don’t use in ANOVA * Also need RSSBLOCKS, RSSPP, and RSSM (CT and RSSTOTAL) * F values are calculated using the error from the same block * For t-test * Standard errors: * Error (b)n for interaction 9.78583 * 2 × Error (b)n for Factor M 2 × 9.78586 * 2 × Error (a)n for Factor PP 2 × 4.96756 * 2 critical-t values → t at 2 and t at 4 df i.e. 4.303 and 2.776 * Could ask: do ANOVA and t-test, or ANOVA and interpret results from F; Standard error for the difference (a or b); Conclusion: levels differ/do not differ at 1% etc. NS 13 – Non-parametric tests * Parametric tests for data with normal distribution (t, F or X2 distribution) * Non-parametric tests for * Categorical data, * Quantitative data divided into class intervals, * Small data sets, * Data sets without repetition of the TMTs. * Non- parametric tests * Medians, not Means * Usually rank your data * Single sample: * Sign test (No assumptions about distribution) * Rank test (assumes data comes from symmetrical distribution) * Wilcoxon’s symmetry test * For 2 independent samples * Mann-Whitney U test (Assumes distributions have same shape and equal...
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...Applying ANOVA and Nonparametric Tests Simulation As the Quality Assurance Manager for Praxidike Systems, it is my job to make sure delivery is on time and that the clients are satisfied. First I had to decide which type of test to use. In order to be able to use ANOVA you have to make three major assumptions: 1. Errors are random and independent of each other 2. Each population has normal distribution 3. All populations have the same variance In order to check whether or not the population has a normal distribution, you need to use the chi-square test for goodness of fit. The hypotheses in this case are: 1. H0: The population has a normal distribution. 2. HA: The population does not have a normal distribution. The outcome was that the test statistic lies outside the acceptance area and you should reject the null hypothesis. As a result, you cannot presume that the population has a normal distribution; you should use the nonparametric Kruskal-Wallis test. The second objective I learned was that you cannot always use the blocking technique. Blocking allows you to see a treatment effect with a smaller sample. It is difficult to set up blocks and it is necessary to determine if creating the block was worth the effort. When setting up a block, you need to match the variable with two or more factors. This may not always be an option. To find out if the block is optimal, you can calculate the relative efficiency. In the case of this simulation, the block design works...
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...0 | .0% | 300 | 100.0% | Sex * Chatting | 300 | 100.0% | 0 | .0% | 300 | 100.0% | Sex * News/Weather | 300 | 100.0% | 0 | .0% | 300 | 100.0% | Sex * Music Crosstab | | Music | Total | | 0 | 1 | | Sex | 1 | Count | 91 | 97 | 188 | | | % within Sex | 48.4% | 51.6% | 100.0% | | | % within Music | 65.0% | 60.6% | 62.7% | | 2 | Count | 49 | 63 | 112 | | | % within Sex | 43.8% | 56.3% | 100.0% | | | % within Music | 35.0% | 39.4% | 37.3% | Total | Count | 140 | 160 | 300 | | % within Sex | 46.7% | 53.3% | 100.0% | | % within Music | 100.0% | 100.0% | 100.0% | Chi-Square Tests | | Value | df | Asymp. Sig. (2-sided) | Exact Sig. (2-sided) | Exact Sig. (1-sided) | Pearson Chi-Square | .611a | 1 | .434 | | | Continuity Correctionb | .438 | 1 | .508 | | | Likelihood Ratio | .612 | 1 | .434 | | | Fisher's Exact Test | | | | .474 | .254 | Linear-by-Linear...
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...A STUDY ON TRAFFIC RULES VIOLATIONS IN CHENNAI (VANDALUR AND PERANGALUTUR) Submitted By: Praveen Kumar A, MBA. INTRODUCTION Violations in traffic laws are very common in a highly populated country like India. The conditions are even worse in metro cities like Delhi, Mumbai Bangalore and Chennai. The accidents associated with these violations cause a huge loss to life and property. Same is the case in Chennai. Being a metro city and a highly populated one also, has a lot of road accidents every year. Despite this the violations in traffic laws do not reduce. A lot of people disobey the rules every day sometimes willingly and sometimes because they are forced to do so because of others. The major traffic laws in India are wearing a helmet in case of two wheelers, putting a seat belt in case of cars, driving on the right side of the road including overtaking from the right direction, over speeding in certain restricted areas, not obeying the traffics signals and stooping the car after the finish line. It is mainly because of these violations that major accidents occur. It should be recognized that the highway is a social situation, in which people are interacting. However the drivers are unknown to each other in most of the cases and the interaction between them very brief and non-recurring. The communication between them is very limited and that also through mechanical aids like lights and horns. The main objective of these laws is to minimize the confusion...
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...distributions are skewed to the left. d. The population variance is unknown and is estimated by the sample variance s2. e. As the sample size increases beyond 120, the t and Z distributions are indistinguishable. Feedback The correct answer is: The t distributions are skewed to the left. Question 2 Incorrect Mark 0 out of 1 Not flaggedFlag question Question text Which of the following involves a test of two-independent samples? Select one: a. Test of differences in the percent of men and women who are or are not members of Greek organizations on campus b. Test of the average incomes of magazine subscribers of Southern Living verses Better Homes and Gardens c. Test of whether the mean salary of professors at Metro University is higher than the national average for university professors Incorrect d. Test of whether a change occurred in the likelihood of heart disease among people who switched to a diet high in fish e. Test of differences in ad recall among three experimental groups (each of which saw a different advertisement) and a control group Feedback The correct answer is: Test of the average incomes of magazine subscribers of Southern Living verses Better Homes and Gardens Question 3 Correct Mark 1 out of 1 Not flaggedFlag question Question text Under which of the following conditions must the null hypothesis be rejected? Select one: a. p value < equation Correct b. p value > equation c. p value = equation d. p value cannot be determined e. p value...
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...beginning Tuesday 9th October 2012 PGM1010 Timetable: please go to http://www.aber.ac.uk/en/grad-school/res-skills-training/res-train-dev/mod-timetable/ Module Overview: This module is taught in lectures and practical classes. This includes sessions on quantitative methods and an appropriate statistical package. Module aims: This module aims to give students a broad knowledge of a range of methodological and analytical skills, which they can apply in a variety of research contexts. As well as giving students a grounding in the basic principles of quantitative research methodology, the module will (i) look at how data can be described, (ii) introduce a range of statistical tests commonly used, and (iii) explore what the results mean in terms of the research question posed. The module comprises lectures, and the delivery will involve hands-on lab work using an appropriate statistical package. Module Objective Learning Outcomes: On completion of this module, students should be able to demonstrate: • An ability to apply a variety of different statistical methods to their research project. • An ability to collect, store, manage, test and interpret data. • An understanding of the epistemological and methodological difficulties associated with different techniques. • An ability to use computer software associated with the analysis of quantitative data. • An awareness of the ethical issues surrounding data collection and analysis. Skills Taught: On completion...
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...research hypothesis from the same provided data sets (Wage and Wage Earners) using ratio or interval numerical data; however, this week we will use a nonparametric hypothesis test to find our answer. In the next following paragraphs, the team will clearly affirm a hypothesis statement that will provide the base for our survey, perform a five-step hypothesis test on information concerning our choice and apply the concepts of nonparametric testing learned in this course, and describe how the results of our findings answer our research question. Finally, we will conclude this study with a brief summary that will examine the main points, the purpose, and conclusions of this final third week’s study on nonparametric testing. Perform the five-step hypothesis test on the data Nonparametric tests are statistical tests that analyze data that does not require assumptions about the distribution of shape of the population from which that data is drawn. These statistical tests should be well thought-out when the data analysis cannot be assumed to have come from any particular distribution. Nonparametrics are often active data that consists of ranks and ordinal data. Usually, nonparametric statistical tests are easier to compute. This is because fewer assumptions need to be complete to use nonparametric tests. They...
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