The Companies Ordinance Essay Example for Free

The Companies Ordinance Essay II. REGISTERED OFFICE: The registered office of the company will be situated be situated in the province of Sindh III. OBJECT CLAUSE: The objects for which the Company is established are: 1. To manufacture, treat, produce, process, prepare, pack, bottle, can import, export, buy, Sell, market, distribute or otherwise deal in any or all types of foods including without limitation all kinds of raw, processed and prepared food and food products including without limitation dairy products, meat, fish, vegetables, poultry, fruit and fruit Juices, powders and syrups; 2. To advertise all or any of the business and goods of the Company in any way that may be thought advisable; 3. To open and operate accounts, overdraft cash, credit and loan accounts and to keep fixed and other deposits with banks, firms, corporations and institutions, loan offices and other concerns; 4. To do all or any of the above things in any part of the world either as principals, agents, Contractors or otherwise and either alone or in conjunction with others, but not to act as a managing agent 5. To expend money in experimenting on and testing and improving or seeking to improve any patents, rights, invention, discoveries, processes or information of the Company or which the Company may acquire or propose to acquire; IV. LIABILITY CLAUSE: The liability of the members is limited V. AUTHORIZED CAPITAL: The authorized share capital of the Company is Rs. 8,500,000,000 (Rupees Eight Billion Five Hundred Million) divided into 850,000,000 (Eight Hundred Fifty Million) ordinary shares of the nominal value of Rs.10.00 (Rupees ten) each with the rights, privileges, and conditions attached. Here to as are provided for the time being, with power to increase and reduce the capital of the Company and to divide the shares in the capital for the time being, into several classes.

Facial Emotion Recognition Systems

Facial Emotion Recognition Systems CHAPTER-1 INTRODUCTION 1.1: Introduction Face plays important role in social communication. This is a window to human character, reactions and ideas. the psychological research shown that nonverbal part is the most enlightening channel in social communication. Verbal part offers about 7% of the message, vocal 34% and facial expression about 55%. Due to that, face is a theme of study in many areas of science such as psychology, behavioral science, medicine and finally computer science. In the field of computer science much effort is put to discover the ways of automation the process of face detection and segmentation. Several methods addressing the problem of facial feature extraction have been proposed. The key problem is to provide suitable face representation, which leftovers robust with respect to diversity of facial appearances. The method of face recognition plays an important role in peoples life ranging from commercial to law enforcement applications, such as real time surveillance, biometric personal identification and information security. It is one of the most challenging topics in the interface of computer vision and cognitive science. Over past years, extensive research on face recognition has been conducted by many psychophysicists, neuroscientists and engineers. In general views, the definition of face recognition can be formulated as follows Different faces in a static image can be identified using a database of stored faces. Available collateral information like facial expression may enhance the recognition rate. Generally speaking, if the face images are sufficiently provided, the quality of face recognition will be mainly related to feature extraction and recognition modeling. Facial emotion recognition in uncontrolled environments is a very challenging task due to large intra-class variations caused by factors such as illumination and pose changes, occlusion, and head movement. The accuracy of a facial emotion recognition system generally depends on two critical factors: (i) extraction of facial features that are robust under intra-class variations (e.g. pose changes), but are distinctive for various emotions, and (ii) design of a classifier that is capable of distinguishing different facial emotions based on noisy and imperfect data (e.g., illumination changes and occlusion). For recognition modeling, lots of researchers usually evaluate the performance of model by recognition rate instead of computational cost. Recently, Wright and Mare ported their work called the sparse representation based classification (SRC). To be more specific, it can represent the testing image sparsely using training samples via norm minimization which can be solved by balancing the minimum reconstructed error and the sparse coefficients. The recognition rate of SRC is much higher than that of classical algorithms such as Nearest Neighbor, Nearest Subspace and Linear Support Vector Machine (SVM). However, there are three drawbacks behind the SRC. First, SRC is based on the holistic features, which cannot exactly capture the partial deformation of the face images. Second, regularized SRC usually runs slowly for high dimensional face images. Third in the presence of occluded face images, Wright et al introduce an occlusion dictionary to sparsely code the occluded components in face images. However, the computational cost of SRC increase drastically because of large number of elements in the occlusion dictionary. Therefore, the computational cost of SRC limits it s application in real time area, which increasingly attracts researchers attention to solve this issue. 1.2: Psychological Background In 1978, Ekman et al. [2] introduced the system for measuring facial expressions called FACS Facial Action Coding System. FACS was developed by analysis of the relations between muscle(s) contraction and changes in the face appearance caused by them. Contractions of muscles responsible for the same action are marked as an Action Unit (AU). The task of expression analysis with use of FACS is based on decomposing observed expression into the set of Action Units. There are 46 AUs that represent changes in facial expression and 12 AUs connected with eye gaze direction and head orientation. Action Units are highly descriptive in terms of facial movements, however, they do not provide any information about the message they represent. AUs are labeled with the description of the action (Fig.1). Fig. 1: Examples of Action Units Facial expression described by Action Units can be then analyzed on the semantic level in order to find the meaning of particular actions. According to the Ekmans theory [2], there are six basic emotion expressions that are universal for people of different nations and cultures. Those basic emotions are joy, sadness, anger, fear, disgust and surprise (Fig. 2). Fig. 2: Six universal emotions The Facial Action Coding System was developed to help psychologists with face behavior analysis. Facial image was studied to detect the Action Units occurrences and then AU combinations were translated into emotion categories. This procedure required much effort, not only because the analysis was done manually, but also because about 100 hours of training were needed to become a FACS coder. That is why; FACS was quickly automated and replaced by different types of computer software solutions. 1.3: Facial emotion recognition systems The aim of FERS is to replicate the human visual system in the most analogous way. This is very thought-provoking job in the area of computer vision because not only it needs effective image/video analysis methods but also well-matched feature vector used in machine learning process. The primary principle of FER system is that it would be easy and effective. That relates to full automation, so that no extra manual effort is obligatory. It is also chosen for such system to be real-time which is particularly significant in both: human-computer interaction and human-robot interaction applications. Besides, the theme of study should be permitted to act impulsively while data is being captured for examination. System should be intended to evade limitations on body and head movements which could also be an important source of data about shown emotion. The limitations about facial hair, glasses or extra make-up should be reduced to lowest. Furthermore, handling the occlusions problem looks to be a test for a system and it should be also considered. Other significant features that are wanted in FER system are user and environment independence. The prior means that, any user should be permissible to work with the system, regardless of of skin color, oldness, gender or state. The latter relates to conduct the complex background and diversity in lightning conditions. Further advantage could be the view independence in FERS, which is likely in systems based on 3D vision. Face Detection As it was stated earlier, FER system comprises of 3 steps. In the first step, system takes input image and does some image processing methods on it, to detect the face region. System can function on static images, where this process is called face localization or videos where we are working with face tracking. Main problems which can be come across at this step are different scales and orientations of face. They are generally produced by subject movements or changes in remoteness from camera. Substantial body actions can also reason for severe changes in position of face in successive frames what makes tracking tougher. What is more, difficulty of background and variety of lightning circumstances can be also quite puzzling in tracking. For example, when there is more than one face in the image, system should be able to differentiate which one is being tracked. Finally, obstructions which usually give the impression in impulsive reactions need to be handled as well. Problems stated overhead were a challenge to hunt for methods which would crack them. Among the methods for face detection, we can differentiate two groups: holistic where face is treated as a whole unit and analytic where co-occurrence of characteristic facial elements is considered. 1.3.2. Feature Extraction Afterward the face has been situated in the image or video frame, it can be examined in terms of facial action occurrence. There are two types of features that are frequently used to define facial expression: geometric features and appearance features. Geometric features quantity the displacements of certain parts of the face such as brows or mouth corners, while appearance features define the variation in face texture when specific action is done. Apart from feature type, FER systems can be separated by the input which could be static images or image sequences. The job of geometric feature measurement is generally connected with face region analysis, exclusively finding and tracking vital points in the face region. Possible problems that arise in face decomposition job could be obstructions and incidences of facial hair or glasses. Besides, defining the feature set is tough, because features should be expressive and possibly not interrelated. Recognition of Expression The latter part of the FER system is based on machine learning theory; exactly it is the classification job. The input to the classifier is a set of features which were recovered from face region in the previous stage. The set of features is designed to describe the facial expression. Classification needs supervised training, so the training set should consist of labeled data. Once the classifier is trained, it can distinguish input images by assigning them a specific class label. The most usually used facial expressions classification is finished both in terms of Action Units, proposed in Facial Action Coding System and in terms of common emotions: happiness, unhappiness, fury, surprise, disgust and fear. There are a lot of different machine learning methods for classification job, viz.: K-Nearest Neighbors, Artificial Neural Networks, Support Vector Machines, Hidden Markov Models, Expert Systems with rule based classifier, Bayesian Networks or Boosting Techniques (Adaboost, Gentleboost). Three major problems in classification job are: picking good feature set, effective machine learning method and diverse database for training. Feature set should be composed of features that are discriminative and characteristic for expression. Machine learning method is chosen usually by the sort of a feature set. In conclusion, database used as a training set should be adequate and contain various data. Methods described in the literature are presented by categories of classification output. 1.4: Applications Enormous amount of different information is encoded in facial movements. Perceiving someones face we can absorb about his/her: †¢ Affective state, connected with emotions like fear, anger and joy and moods such as euphoria or irritation †¢ Cognitive activity (brain activity), which can be seeming as attentiveness or boredom †¢ Personality features like sociability, nervousness or unfriendliness †¢ Honesty using analysis of micro-expressions to disclose hidden emotions †¢ Psychological state giving information about illnesses helpful with diagnosis of depression, mania or schizophrenia. Due to the variety of information visible on human face, facial expression analysis has applications in various fields of science and life. Primarily, teachers use facial expression investigation to correct the struggle of the exercise and learning pace on a base of reaction visible on students faces. Virtual tutor in e-learning planned by Amelsvoort and Krahmer [26] offers student with suitable content and alters the complexity of courses or tasks by the information attained from students face. Additional application of FERS is in the field of business where the measurement of peoples fulfilment or disappointment is very important. Usage of this application can be found in many marketing methods where information is collected from customers by surveys. The great chance to conduct the surveys in the automatic way could be able by using customers facial expressions as a level of their satisfaction or dissatisfaction . Furthermore, prototype of Computerized Sales Assistant, proposed by Shergill et al.   chooses the appropriate marketing and sales methods by the response taken from customers facial expressions. Facial behavior is also studied in medicine not only for psychological disorder diagnosis but also to help people with some disabilities. Example of it could be the system proposed by Pioggial et al.   that aids autistic children to progress their social skills by learning how to recognize emotions. Facial expressions could be also used for surveillance purposes like in prototype developed by Hazel hoff et al.. Suggested system automatically perceives uneasiness of newborn babies by recognition of 3 behavioral states: sleep, awake and cry. Furthermore, facial expression recognition is broadly used in human robot and human computer interaction Smart Robotic Assistant for people with disabilities based on multimodal HCI. Another example of human computer interaction systems could be system developed for automatic update of avatar in multiplayer online games. 1.5: Thesis Organization The thesis is organized as follows: The thesis is opened with an introduction i.e., Chapter 1, in which it is discussed about the introduction, physiological background and facial emotion recognition systems along with the Thesis organization and the Tools used for the whole project. Chapter 2 discussed about the literature survey in which the brief explanation of previous works is given and explained. Chapter 3 discussed about the proposed system in which the each part of the face was detected and the emotion of the person is detected based on extreme sparse learning. Here we use the spatial-temporal descriptor and optimal flow method to recognize the emotion. Chapter 4 plays key role in this project which gives the information of software that used for the project i.e. MATLAB. The results and discussions are presented in Chapter 5. This chapter describes the results that are obtained for the proposed system. Chapter 6 discussed about the advantages of the proposed system and disadvantages of the existing systems. Hence the conclusion and future work, references are presented in chapter 7. Then the references are mentioned in the chapter8. 1.6: Tools Used Image processing toolbox MATLAB R2013a(version 8)

Applying The Anova Test Education Essay

Applying The Anova Test Education Essay Chapter 6 ANOVA When you want to compare means of more than two groups or levels of an independent variable, one way ANOVA can be used. Anova is used for finding significant relations. Anova is used to find significant relation between various variables. The procedure of ANOVA involves the derivation of two different estimates of population variance from the data. Then statistic is calculated from the ratio of these two estimates. One of these estimates (between group variance) is the measure of the effect of independent variable combined with error variance. The other estimate (within group variance) is of error variance itself. The F-ratio is the ratio of between groups and within groups variance. In case, the null hypothesis is rejected, i.e., when significant different lies, post adhoc analysis or other tests need to be performed to see the results. The Anova test is a parametric test which assumes: Population normality data is numerical data representing samples from normally distributed populations Homogeneity of variance the variances of the groups are similar the sizes of the groups are similar the groups should be independent ANOVA tests the null hypothesis that the means of all the groups being compared are equal, and produces a statistic called F. If the means of all the groups tested by ANOVA are equal, fine. But if the result tells us to reject the null hypothesis, we perform Brown-Forsythe and Welch test options in SPSS. Assumption of Anova: Homogeneity of Variance. As such homogeneity of variance tests are performed. If this assumption is broken then Brown-Forsythe test option and Welch test option display alternate versions of F-statistic. Homogeneity of Variance: If significance value is less than 0.05, variances of groups are significantly different. Brown-Forsythe and Welch test option: If significance value is less than 0.05, reject null hypothesis. Anova: If significance value is less than 0.05, reject null hypothesis. Post Hoc analysis involves hunting through data for some significance. This testing carries risks of type I errors. Post hoc tests are designed to protect against type I errors, given that all the possible comparisons are going to be made. These tests are stricter than planned comparisons and it is difficult to obtain significance. There are many post hoc tests. More the options, stricter will be the determination of significance. Some post hoc tests are: Scheffe test- allows every possible comparison to be made but is tough on rejecting the null hypothesis. Tukey test / honestly significant difference (HSD) test- lenient but the types of comparison that can be made are restricted. This chapter will show Tukey test also. One way ANOVA Working Example 1 : One-way between groups ANOVA with post-hoc comparisons Vijender Gupta wants to compare the scores of CBSE students from four metro cities of India i.e. Delhi, Kolkata, Mumbai, Chennai. He obtained 20 participant scores based on random sampling from each of the four metro cities, collecting 100 responses. Also note that, this is independent design, since the respondents are from different cities. He made following hypothesis: Null Hypothesis : There is no significant difference in scores from different metro cities of India Alternate Hypothesis : There is significant difference in scores from different metro cities of India Make the variable view of data table as shown in the figure below. Enter the values of city as 1-Delhi, 2-Kolkata, 3-Mumbai, 4-Chennai. Fill the data view with following data. City Score 1 400.00 1 450.00 1 499.00 1 480.00 1 495.00 1 300.00 1 350.00 1 356.00 1 269.00 1 298.00 1 299.00 1 599.00 1 466.00 1 591.00 1 502.00 1 598.00 1 548.00 1 459.00 1 489.00 1 499.00 2 389.00 2 398.00 2 399.00 2 599.00 2 598.00 2 457.00 2 498.00 2 400.00 2 300.00 2 369.00 2 368.00 2 348.00 2 499.00 2 475.00 2 489.00 2 498.00 2 399.00 2 398.00 2 378.00 2 498.00 3 488.00 3 469.00 3 425.00 3 450.00 3 399.00 3 385.00 3 358.00 3 299.00 3 298.00 3 389.00 3 398.00 3 349.00 3 358.00 3 498.00 3 452.00 3 411.00 3 398.00 3 379.00 3 295.00 3 250.00 4 450.00 4 400.00 4 450.00 4 428.00 4 398.00 4 359.00 4 360.00 4 302.00 4 310.00 4 295.00 4 259.00 4 301.00 4 322.00 4 365.00 4 389.00 4 378.00 4 345.00 4 498.00 4 489.00 4 456.00 Click on Analyze menuÆ’Â  Compare MeansÆ’Â  One-Way ANOVAà ¢Ã¢â€š ¬Ã‚ ¦.One-Way ANOVA dialogue box will be opened. Select Student Score(dependent variable) in Dependent List box and City(independent variable) in the Factor as shown in the figure below. Click Contrastsà ¢Ã¢â€š ¬Ã‚ ¦ push button. Contrasts sub dialogue box will be opened. See that all the settings remain as shown in the figure below. Click Continue to close this sub dialogue box and come back to One-Way ANOVA dialogue box. Click Post Hocà ¢Ã¢â€š ¬Ã‚ ¦ push button. Post Hoc sub dialogue box will be opened. See that all the settings remain as shown in the figure below. Click Tukey test and Click Continue to close this sub dialogue box and come back to One-Way ANOVA dialogue box. Also note that significant level in this sub dialogue box is 0.05, which can be changed according to the need. Click Optionsà ¢Ã¢â€š ¬Ã‚ ¦ push button. Options sub dialogue box will be opened. Select the Descriptive and Homogenity of variance test check box and see that all the settings remain as shown in the figure below. Click Continue to close this sub dialogue box and come back to One-Way ANOVA dialogue box. Click OK to see the output viewer. The Output: ONEWAY Score BY City /STATISTICS DESCRIPTIVES HOMOGENEITY /MISSING ANALYSIS /POSTHOC=TUKEY ALPHA(0.05). Descriptives Student Score N Mean Std. Deviation Std. Error 95% Confidence Interval for Mean Minimum Maximum Lower Bound Upper Bound Delhi 20 447.3500 104.69016 23.40943 398.3535 496.3465 269.00 599.00 Kolkata 20 437.8500 79.75771 17.83437 400.5222 475.1778 300.00 599.00 Mumbai 20 387.4000 67.25396 15.03844 355.9242 418.8758 250.00 498.00 Chennai 20 377.7000 68.49287 15.31547 345.6443 409.7557 259.00 498.00 Total 80 412.5750 85.54676 9.56442 393.5375 431.6125 250.00 599.00 Test of Homogeneity of Variances Student Score Levene Statistic df1 df2 Sig. 2.371 3 76 .077 Since, homogeneity of variance should not be there for conducting Anova tests, which is one of the assumptions of Anova, we see that Levenes test shows that homogeneity of variance is not significant (p>0.05). As such, you can be confident that population variances for each group are approximately equal. We can see the Anova results ahead. ANOVA Student Score Sum of Squares df Mean Square F Sig. Between Groups 73963.450 3 24654.483 3.716 .015 Within Groups 504178.100 76 6633.922 Total 578141.550 79 Table above shows the F test values along with degrees of freedom (2,76) and significance of 0.15. Given that p Multiple Comparisons Student Score Tukey HSD (I) Metro City (J) Metro City Mean Difference (I-J) Std. Error Sig. 95% Confidence Interval Lower Bound Upper Bound Delhi Kolkata 9.50000 25.75640 .983 -58.1568 77.1568 Mumbai 59.95000 25.75640 .101 -7.7068 127.6068 Chennai 69.65000* 25.75640 .041 1.9932 137.3068 Kolkata Delhi -9.50000 25.75640 .983 -77.1568 58.1568 Mumbai 50.45000 25.75640 .213 -17.2068 118.1068 Chennai 60.15000 25.75640 .099 -7.5068 127.8068 Mumbai Delhi -59.95000 25.75640 .101 -127.6068 7.7068 Kolkata -50.45000 25.75640 .213 -118.1068 17.2068 Chennai 9.70000 25.75640 .982 -57.9568 77.3568 Chennai Delhi -69.65000* 25.75640 .041 -137.3068 -1.9932 Kolkata -60.15000 25.75640 .099 -127.8068 7.5068 Mumbai -9.70000 25.75640 .982 -77.3568 57.9568 *. The mean difference is significant at the 0.05 level. Using Tukey HSD further, we can conclude that Delhi and Chennai have significant difference in their scores. This can be concluded from figure above and figure below. Student Score Tukey HSDa Metro City N Subset for alpha = 0.05 1 2 Chennai 20 377.7000 Mumbai 20 387.4000 387.4000 Kolkata 20 437.8500 437.8500 Delhi 20 447.3500 Sig. .099 .101 Means for groups in homogeneous subsets are displayed. a. Uses Harmonic Mean Sample Size = 20.000. Working Example 2 : One-way between groups ANOVA with Brown-Forsythe and Weltch tests Aditya wants to see that there exists a significant difference between collecting information (internet use) and internet benefits. He collects data from 29 respondents and finds the solution through one way Anova. Note: The respondents count in the working example is kept small for showing all the 29 responses in data view window in figure ahead. Null Hypothesis : There is no significant difference in collecting information and internet benefits. Alternate Hypothesis : There is significant difference in collecting information and internet benefits. Internet Use Collecting Information(Info) [see figure below] Internet Benefits Availability of updated information(Use1) Easy movement across websites(Use2) Prompt online ordering(Use3) Prompt query handling(Use4) Get lowest price for product/service purchase(Compar1) Easy comparison of product/service from several vendors(Compar2) Easy comparison of price from several vendors(Compar3) Able to obtain competitive and educational information regarding product/ service(Compar4) Reduced order processing time(RedPTM1) Reduced paper flow(RedPTM2) Reduced ordering costs(RedPTM3) Info (Collecting Information) : 1(Never), 2(Occasionally), 3(Considerably), 4(Almost Always), 5(Always) Internet Benefits : 1(Not important), 2(Less important), 3(Important), 4(Very Important), 5(Extremely Important) Enter the variable view of variables as shown in the figure below. Enter the data in the data view as shown in the figure below. Click AnalyzeÆ’Â  Compare MeansÆ’Â  One-Way ANOVAà ¢Ã¢â€š ¬Ã‚ ¦. The One-Way ANOVA dialogue box will be opened. Insert all the internet benefits variables in dependent list and internet use variable in the factor as shown in the figure below. Click Post Hocà ¢Ã¢â€š ¬Ã‚ ¦ push button to open its sub dialogue box. See that significance level is set as per need. In this case, we have used 0.05 significance level. Click Continue to close the sub dialogue box. Click Optionsà ¢Ã¢â€š ¬Ã‚ ¦ push button in the One-Way ANOVA dialogue box. Select the Descriptive, Homogeneity of variance test, Brown-Forsythe and Welch check boxes and click continue to close this sub dialogue box. Click OK to see the output viewer. The OUTPUT ONEWAY Use1 Use2 Use3 Use4 Compar1 Compar2 Compar3 Compar4 RedPTM1 RedPTM2 RedPTM3 BY InfoG2 /STATISTICS HOMOGENEITY BROWNFORSYTHE WELCH /MISSING ANALYSIS. Test of Homogeneity of Variances Levene Statistic df1 df2 Sig. Availability of Updated information 1.117 3 25 .361 Easy Movement across around websites .475 3 25 .703 Prompt online ordering .914 3 25 .448 Prompt Query handling 2.379 3 25 .094 Get lowest price for product / service purchase 1.327 3 25 .288 Easy comparison of product / service from several vendors .755 3 25 .530 Easy comparison of price from several vendors 3.677 3 25 .025 Able to obtain competitive and educational information regarding product / service 1.939 3 25 .149 Reduced order processing time .326 3 25 .806 Reduced Paper Flow 1.478 3 25 .245 Reduced Ordering Costs 2.976 3 25 .051 Table above shows that Easy comparison of price from several vendors has significantly different variances according to levene statistic and showing significant level of only 0.025 (which is below 0.05 for 5% level of significance) as such anova result may not be valid for this variable. Therefore, Brown-Forsythe and Welch tests are performed for analyzing this particular variable. ANOVA Sum of Squares df Mean Square F Sig. Availability of Updated information Between Groups .702 3 .234 1.775 .178 Within Groups 3.298 25 .132 Total 4.000 28 Easy Movement across around websites Between Groups 2.630 3 .877 1.817 .170 Within Groups 12.060 25 .482 Total 14.690 28 Prompt online ordering Between Groups 1.785 3 .595 2.154 .119 Within Groups 6.905 25 .276 Total 8.690 28 Prompt Query handling Between Groups 1.742 3 .581 2.132 .121 Within Groups 6.810 25 .272 Total 8.552 28 Get lowest price for product / service purchase Between Groups .059 3 .020 .074 .974 Within Groups 6.631 25 .265 Total 6.690 28 Easy comparison of product / service from several vendors Between Groups .604 3 .201 .617 .610 Within Groups 8.155 25 .326 Total 8.759 28 Easy comparison of price from several vendors Between Groups 6.630 3 2.210 4.582 .011 Within Groups 12.060 25 .482 Total 18.690 28 Able to obtain competitive and educational information regarding product / service Between Groups 1.302 3 .434 2.212 .112 Within Groups 4.905 25 .196 Total 6.207 28 Reduced order processing time Between Groups .273 3 .091 .259 .854 Within Groups 8.762 25 .350 Total 9.034 28 Reduced Paper Flow Between Groups .140 3 .047 .110 .954 Within Groups 10.619 25 .425 Total 10.759 28 Reduced Ordering Costs Between Groups .647 3 .216 .453 .718 Within Groups 11.905 25 .476 Total 12.552 28 Table above shows the F test values along with significance in case of collecting information (Internet use). Comparing the F test values and significance values, we see that all the anova comparisons favour the acceptance of null hypothesis. Please note that significance values are greater than 0.05 in all the variables except easy comparison of price from several vendors, according to homogeneity rule, this variable will not be judged by Anova F statistic. For this variable, we have performed Welch and Brown-Forsythe tests. Robust Tests of Equality of Meansb,c,d Statistica df1 df2 Sig. Availability of Updated information Welch 1.123 3 7.172 .401 Brown-Forsythe 1.244 3 6.530 .368 Easy Movement across around websites Welch 1.659 3 8.402 .249 Brown-Forsythe 2.051 3 17.509 .144 Prompt online ordering Welch 1.633 3 7.896 .258 Brown-Forsythe 2.178 3 11.593 .145 Prompt Query handling Welch . . . . Brown-Forsythe . . . . Get lowest price for product / service purchase Welch . . . . Brown-Forsythe . . . . Easy comparison of product / service from several vendors Welch .560 3 8.014 .656 Brown-Forsythe .682 3 12.935 .579 Easy comparison of price from several vendors Welch . . . . Brown-Forsythe . . . . Able to obtain competitive and educational information regarding product / service Welch 1.472 3 7.457 .298 Brown-Forsythe 1.827 3 9.211 .211 Reduced order processing time Welch .219 3 8.155 .881 Brown-Forsythe .278 3 14.596 .840 Reduced Paper Flow Welch .119 3 8.021 .946 Brown-Forsythe .122 3 15.144 .946 Reduced Ordering Costs Welch .735 3 8.066 .560 Brown-Forsythe .525 3 16.006 .671 a. Asymptotically F distributed. b. Robust tests of equality of means cannot be performed for Prompt Query handling because at least one group has 0 variance. c. Robust tests of equality of means cannot be performed for Get lowest price for product / service purchase because at least one group has 0 variance. d. Robust tests of equality of means cannot be performed for Easy comparision of price from several vendors because at least one group has 0 variance. Table above shows the Welch and Brown-Forsythe tests performed on the internet benefits and particularly help in analyzing easy comparison of product / service from several vendors. The significance values are much higher then required 0.05. The Statistics and significance values indicate the acceptance of null hypothesis. The analysis and conclusion from output: Homogeneity of Variance test Anova test Brown-Forsythe test Welch test Accept Null Hypothesis Use1 Æ’Â ¼ Æ’Â ¼ Æ’Â ¼ Use2 Æ’Â ¼ Æ’Â ¼ Æ’Â ¼ Use3 Æ’Â ¼ Æ’Â ¼ Æ’Â ¼ Use4 Æ’Â ¼ Æ’Â ¼ Æ’Â ¼ Compar1 Æ’Â ¼ Æ’Â ¼ Æ’Â ¼ Compar2 x x Æ’Â ¼ Æ’Â ¼ Æ’Â ¼ Compar3 Æ’Â ¼ Æ’Â ¼ Æ’Â ¼ Compar4 Æ’Â ¼ Æ’Â ¼ Æ’Â ¼ RedPTM1 Æ’Â ¼ Æ’Â ¼ Æ’Â ¼ RedPTM2 Æ’Â ¼ Æ’Â ¼ Æ’Â ¼ RedPTM3 Æ’Â ¼ Æ’Â ¼ Æ’Â ¼ All the results verify the Null Hypothesis acceptance. Hence, we accept null hypothesis, i.e., There is no significant difference in collecting information and internet benefits. Working Example 3 : One-way between groups ANOVA with planned comparisons Ritu Gupta wants to know the sales in four different metro cities of India in Diwali season. She assumes the sales contrast of 2:1:-1:-2 for Delhi:Kolkata:Mumbai:Chennai, respectively. She collects sales data from 10 respondents each from the four metro cities, collecting a total of 40 sales data. Open new data file and make variables as shown in the figure below. The values column in the city row consists of following values: 1 Delhi 2 Kolkata 3 Mumbai 4 Chennai Enter the sales data of 40 respondents as shown below: City Sales (Rs. Lacs) 1 500.00 1 498.00 1 478.00 1 499.00 1 450.00 1 428.00 1 500.00 1 498.00 1 486.00 1 469.00 2 500.00 2 428.00 2 439.00 2 389.00 2 379.00 2 498.00 2 469.00 2 428.00 2 412.00 2 410.00 3 421.00 3 410.00 3 389.00 3 359.00 3 369.00 3 359.00 3 349.00 3 349.00 3 359.00 3 400.00 4 289.00 4 269.00 4 259.00 4 299.00 4 389.00 4 349.00 4 350.00 4 301.00 4 297.00 4 279.00 Click AnalyzeÆ’Â  Compare MeansÆ’Â  One-Way ANOVAà ¢Ã¢â€š ¬Ã‚ ¦. This will open One-Way ANOVA dialogue box. Shift the Sales variable to Dependent List and City variable to Factor column. Click Contrastsà ¢Ã¢â€š ¬Ã‚ ¦ push button to open its sub dialogue box. Enter the coefficients as shown in the figure below. Notice that the coefficient total should be zero. Click continue to close the sub dialogue box and come back to previous dialogue box. Click Post Hocà ¢Ã¢â€š ¬Ã‚ ¦ push button to check the significance level in the Post Hoc sub dialogue box. In this case it is 0.05. Click continue to close this sub dialogue box. Click Optionsà ¢Ã¢â€š ¬Ã‚ ¦ push button to open its sub dialogue box. Select descriptive and homogeneity of variance test and click continue to close this sub dialogue box. This will open previous dialogue box. Click OK to see the output viewer. The Output: ONEWAY Sales BY City /CONTRAST=2 1 -1 -2 /STATISTICS DESCRIPTIVES HOMOGENEITY /MISSING ANALYSIS. Descriptives Sales (Rs.Lacs) N Mean Std. Deviation Std. Error 95% Confidence Interval for Mean Minimum Maximum Lower Bound Upper Bound Delhi 10 480.6000 24.87837 7.86723 462.8031 498.3969 428.00 500.00 Kolkata 10 435.2000 41.99153 13.27889 405.1611 465.2389 379.00 500.00 Mumbai 10 376.4000 26.45415 8.36554 357.4758 395.3242 349.00 421.00 Chennai 10 308.1000 41.33992 13.07283 278.5272 337.6728 259.00 389.00 Total 40 400.0750 73.46703 11.61616 376.5791 423.5709 259.00 500.00 Test of Homogeneity of Variances Sales (Rs.Lacs) Levene Statistic df1 df2 Sig. 1.377 3 36 .265 The Levene test statistic shows that p>.05. As such, assumption of ANOVA for homogeneity of variance has not been violated. ANOVA Sales (Rs.Lacs) Sum of Squares df Mean Square F Sig. Between Groups 167379.475 3 55793.158 46.581 .000 Within Groups 43119.300 36 1197.758 Total 210498.775 39 The Anova F-ratio and significance values suggests that season does significantly influence the sales in the cities, F(3,36) = 46.581, p The contrast coefficients, as assumed are shown in the table below. Contrast Coefficients Contrast Metro City Delhi Kolkata Mumbai Chennai 1 2 1 -1 -2 Contrast Tests Contrast Value of Contrast Std. Error t df Sig. (2-tailed) Sales (Rs.Lacs) Assume equal variances 1 403.8000 34.60865 11.668 36 .000 Does not assume equal variances 1 403.8000 34.31443 11.768 22.101 .000 Since, the assumptions of homogeneity of variance were not violated, you can discuss with assume equal variances row of upper table. The t value of 36 is highly significant (p The descriptive table shows that during Diwali season, Delhi has maximum sales and Chennai has least sales according to the respondents. To obtain F value, the above T value will be squared, i.e. F=T2 = 11.668*11.668=136.142224. Also note that, df1 for planned comparison is always 1, i.e. df1=1 and df2 will be shown in the within groups estimate of ANOVA table above, i.e., df2=36. As such we can write the result as F(1,36)=136.142224, p Two way ANOVA Two way ANOVA is similar to one way ANOVA in all the aspects except that in this case additional independent variable is introduced. Each independent variable includes two or more variants. Working Example 4 : Two way between groups ANOVA Neha gupta wants to research that whether sales (dependent) of the respondents depend on their place(independent) and education (independent). She assigns 9 respondents from each metro city. Each respondent can select three education levels. Place: 1(Delhi), 2(Kolkata), 3(Chennai) Education: 1(Under graduate), 2(Graduate), 3(Post Graduate) A total of 3x3x9 = 81 responses were collected. She wants to know whether : The location influences sales? The education influences the sales? The influence of education on sales depends on location of respondent? Make the data file by creating variables as shown in the figure below. Enter the data in the data view as shown in the figure below. Click AnalyzeÆ’Â  General Linear ModelÆ’Â  Univariateà ¢Ã¢â€š ¬Ã‚ ¦. This will open Univariate dialogue box. Choose sales and send it in dependent variable box. Similarly, choose place and education to send them in fixed factor(s) list box. Click Options push button to open its sub dialogue box. Click Descriptive Statistics, Estimates of effect size, Observed power and Homogeneity tests check boxes in the Display box and click continue. Previous dialogue box will open. Click OK to see the output. The Output : UNIANOVA Sales BY Place Education /METHOD=SSTYPE(3) /INTERCEPT=INCLUDE /PRINT=ETASQ HOMOGENEITY DESCRIPTIVE OPOWER /CRITERIA=ALPHA(.05) /DESIGN=Place Education Place*Education. Between-Subjects Factors Value Label N Place 1 Delhi 9 2 Kolkata 9 3 Chennai 9 Education 1

Cause and Effects of Smallpox Essay -- Biology Medical Biomedical Dise

Cause and Effects of Smallpox Smallpox is caused by the variola virus that emerged in human populations thousands of years ago. Smallpox is a specific, infectious, and highly contagious febrile disease known only to be transmitted by humans. It is caused by a virus from air currents which are eventually passed on from person to person. Smallpox varies from a mild form without skin manifestations to a highly fatal hemorrhagic form. Edward Jenner, an English physician, discovered a means of preventing smallpox through vaccination. Gradually mass vaccination programs were introduced in many parts of the world. Smallpox was the first disease conquered by human beings and was eradicated by vaccination. The last known cases of naturally occurring smallpox were isolated in 1977 in Somalia. Smallpox had been one of the world’s most feared diseases which killed hundreds of millions of people and scarred and blinded millions more. Smallpox, which is caused by variola virus, is a severe, often fatal, highly contagious disease. The name smallpox is derived from the Latin word for â€Å"spotted† and refers to the raised bumps that appear on the face and body of an infected person. It is characterized by high fever and distinctive skin rash that frequently leaves permanent deep-pitted scars. Smallpox varies in severity from a mild, difficult-to-recognize form without skin manifestations to a highly fatal hemorrhagic form. From the 15th century through the 18th records of the disease in Europe show its catastrophic effect on the lives of people and the political and economic history of nations. Even survivors were frequently disfigured for life. (Henderson, 1947) Smallpox was caused by a virus that spread from person to person through the air. I... ...revention. However, in the aftermath of the events of September and October, 2001, there is heightened concern that the variola virus might be used as an agent of bioterrorism. For this reason, the U.S. government is taking precautions for dealing with a smallpox outbreak. (CDC, 2005). Cause and Effects of Smallpox†¦6 References Center for Disease Control. (2004,December).Smallpox Disease Overview. Center for Disease Control. Date Retreived: http://www.bt.cdc.gov/agent/smallpox/overview/disease-facts.asp Henderson. D. 1947. World Book Encyclopedia. Chicago, IL: Scott Fetzer. Thomas, R. (1907). Variola. The Ecletric Practice of Medcine. Date Retreived: July 21, 2005: http://www.ibiblio.org/herbmed/eclectic/themes/smallpox.html World Health Organization. Smallpox. World Health Org. July 21, 2005: http://www.who.int/mediacentre/factsheets/smallpox/en/print.html

indo-canadians :: essays research papers

  Ã‚  Ã‚  Ã‚  Ã‚  Canada is referred to as a multicultural country because it openly accepts new immigrants from around the world (Gabor, 1994; Nodwell and Guppy, 1992). It has been documented that approximately 11.2% of Canada’s total population identify themselves as visible minorities (Varma-Joshi, Baker, and Tanaka, 2004; Fantino and Colak, 2001). Starting a life in a new country not only brings happiness, but also anxiety and a fear of losing one’s identity. Often feelings of being an outsider act as a catalyst for gang related violence and crime, especially in the Indo-Canadian community. However, there is not enough documented evidence explaining why violence is so prominent amongst Indo-Canadian youth. Although there is not enough evidence accumulated by researchers on this topic, based on research that I have gathered about other minorities involved in gang related violence, I will show that there is a tendency for Indo-Canadians to follow the same pattern a s other minorities who become involved in gang activities. The lack of academic research on Indo-Canadian gang violence is significant to the practice of social work because it is the absence of research which makes it difficult for social workers to pinpoint key signs of gang violence and how they maybe related to their clients. As a result of a lack of academic based research on Indo-Canadian gangs, it limits one from finding possible solutions to deter future incidents of gang violence.   Ã‚  Ã‚  Ã‚  Ã‚  Even though gang violence is not a new phenomenon there has been a noticeable lack of Canadian based research done on this topic (Gordon, 2000; Varma-Joshi, Baker, and Tanaka, 2004). Although there is limited knowledge about gang violence, research shows that males are more likely to engage in gang activities (Gordon, 2000; Jemmott, B., Jemmott, S., Hines, and Fong, 2001). There are several factors that contribute to why many youths become involved in gangs. One of the main reasons why visible minority youth become submerged in gangs is because they are searching for a sense of identity and belonging (Gordon, 2000; Meloff and Silverman, 1992; Nodwell and Guppy, 1992; Fantino and Colak, 2001). Gordon (2000) finds that, â€Å"they want to belong to a friendly, supportive group that include their friends or close relatives and this includes a desire to be with individuals from the same cultural and ethnic group; gang members felt ethnically marginalized† (pg. 51). The reason why minorities are attracted to gangs is because they create a family setting which embraces their differences as opposed to being judged on their differences by mainstream society.

Analysis of Cartoon Cultures in Walt Disney Stories

Disney and his studio do not only aim to create entertainment but present a meaningful thesis as well; â€Å"All our dreams can come true if we have the courage to pursue them† (Williams & Denney, 2004, p. 69). Usually, Disney’s stories like to present the royal romance comprising of love, courage and dream.By comparing Cinderella (Geronimi, Luske, & Jackson, 1950) and his latest princess iteration, The Princess and The Frog (Clements, 2009), we could clearly see the critical influence of Cinderella that has affected the cartoon culture of Disney’s stories, as highlighted by the appearance of the Fairy Godmother, the importance of animal characters and the narrative power of the songs. Many of Disney’s stories are concerned with a goal that the leading character has to achieve, passing obstacles along the way in order to realize his dream.As such, a correlation can be seen between Cinderella (1950) and The Princess and The Frog (2009); both the young ladie s work very hard to achieve their aspirations. Before Cinderella was transformed by Fairy Godmother with an opportunity to attend the royal ball, she was a poor girl, and seemingly under distress. Her stepmother and stepsisters take over all her benefits and mistreat her. She is abused and made to serve as the housekeeper and maid for her family. However, she does not give up on dreaming and wishing, and has faith that one day her dream will come true.On the other hand, Tiana is a poor African-American young lady who works very hard to accomplish her goal of owning a restaurant in New Orleans. Although, her dream seems big and difficult for her to achieve, she never thinks about giving up. She continues tracing her dream; her hopes remain high even after she is accidently transformed into a frog by kissing the cursed Prince Naveen. Although these two Disney princesses are coming from different nationalities and generations, they both represent the same story structure of Disney†™s fairytales.Secondly, Disney’s Cinderella has become the dominant version in Western culture, since it was the first one to be aired on the big screen. It was the first time a character like the Fairy godmother was presented straight from a Disney tale. Her task was strictly to prepareeverything for Cinderella so she could attend the royalty ball. The story of Cinderella has historically portrayed a strong character in the Fairy godmother; a prominent companion has become an important element in the life of a cheerful heroine in the follow up stories of Disney.Looking into The Princess and The Frog (Clements, 2009)Mama Odie is representing a new type of Fairy godmother as a blind voodoo priestess. She plays a guiding role in the story, full of magic and power to help the frogs transform back into humans. The Fairy godmother has thus become an iconic character since it was first presented in Cinderella. It plays an important part in shaping the cartoons’ structures, influencing many other stories to portray the same character in other forms, enriching the story with color and inspiration.Thirdly, by featuring animal characters in Cinderella, Disney and his animators have developed a new structure of cartoon culture. Animal characters have carved a niche in Disney’s animations. However, these animal characters only play supporting or minor roles in the full-length animated films. Due to the strong connection between animal characters and Disney’s animations in the public’s mind, Disney and his animators have recently inclined towards creating much more detailed versions of the animal characters found in Cinderella.As a result, the mice have gained more screen time than the step-mother and sisters, and one of the most memorable scenes in the film regarding the manufacturing process of the ball dress revolves aroundbirds and mice which work together to prepare a beautiful gown for Cinderella. This scene appears for nearly 8 m inutes in the film. It shows the importance of animals in story-telling. Moreover, those animals carry their own personality, as seen for example in the red bird who would much enjoyperforming a vocal accommodation where as the character Jaq, who was portrayed as a leader of the mice while Gus was the cute and childish one.Moreover, the relationship between Cinderella and the mice provided clues to reveal her real personality. Her kindness could be observed through her interest in making clothes and dresses for the animals, rescuing them from traps, and feeding them with enough food. As such, in the story of Cinderella, animals do not feature as minor characters. They are highlighted to be very important in the narrative. Since that story, animal characters have moved up in ranks, taking important roles as support for the main characters in reaching their goals.In The Princess and The Frog (Clements, 2009)those small animal characters follow Cinderella’s tradition in that the y play important roles as support for the frogs so they could return to their human forms, carrying their own personality and dreams. For example, Ray is a firefly, who knows Mama Odie and agrees to help the frogs find her and nearly sacrifices himself fighting with Dr. Facilier. From his lover to the star, we can see that his personality is very gentle and kind. Moreover, Louis is a friendly neurotic alligator, who dreams to become human and joins a jazz band as the trumpet player.He is not just a background image and accompanies the frogs in their adventure. Their appearances make the narratives to be more balanced, completed and interesting. Thanks to the success of theanimal characters in Cinderella, animal characters have continued to play an important role in developing plots and, in turn, have had stories revolve around them instead of the other way round. Lastly, over the years Disney’s animations have developed an indivisible relationship with songs and musical eleme nts.Songs provide a useful tool to breathe more definition into the characters. For example, in Cinderella, the song â€Å"A Dream is A Wish Your Heart Makes† (Geronimi, Luske, & Jackson, 1950), we can observe that Cinderella is faced with many obstacles, but her dream is still kept alive in her heart, thanks to her positive attitude. This way, songs contain the power of presenting deeper content, which normal speech may not effectively communicate. Besides, the audience can see and feel the threat pertaining to the character of Dr.Facilier, for example, by virtue of the song â€Å"Friends on the Other Side† (Clements, 2009). Walt Disney has seemingly pioneered the tradition of using songs and music to replace boring conversations, because the song can portray an idea in much more detail. For example, the Fairy godmother’s song â€Å"Bibidi-Bobidi-Boo† (Geronimi, Luske, & Jackson, 1950), played when she transforms the pumpkin into a coach, the mice into a horse, and Cinderella’s appearance for the ball, etc. relayed a magical element only music could provide.The song, in this context, is a much more powerful tool to present the idea than normal speech, because it gives the audience an extra vocal dimension to remember the scene with, adding to the visual impact. Moreover, the songs and music also add to the dynamics of the picture in order to present the inner meaning of the stories. By comparing the versions of â€Å"Sing Sweet Nightingale† (Geronimi, Luske, & Jackson, 1950) as sung by Cinderella and her stepsisters, we can see the difference in nature of both parties, Cinderella being the gentler much more caring personality.At another instance, we can see a deeper relationship between the frogs, through the song Never knew I Needed, instead of simply saying, I think I am falling in love with you (Clements, 2009). It surrounds the narrative with a profundity in an implicit way. Disney’s animation, hence, reall y has the ability to capture the fantasy-oriented imagination of the audience providing entertainment both for the adults and children alike. Incorporating these qualities into Disney’s cartoon culture has enabled the creation of some of the most excellent pieces of animated and other motion-picture films that history has ever seen.

Everyday Use Essay

â€Å"Everyday Use† is a highly symbolic story with manifold themes and interpretations. It not only locates conflict that existed within the Afro-American community and presents life in relation to modern and traditional concept of Afro-American heritage but it further takes into account the feminist themes and agendas. It is a representative story of Black movement of 1960s but it does not manifest the Afro-American quest for socio-cultural identity like other Black Movement literature. The central conflict is symbolized by two main characters; Mrs. Johnson and Dee. Dee is an epitome of shallow materialism and an adherent of prevailing concept of heritage where heritage is revered only for trendiness and aesthetic attraction whereas Mrs. Johnson admires heritage for its practical utility and personal importance. Central conflict is between Maggie and Dee and it is about whether legacy and heritage subsists in belongings or in spirit. Dee’s longing for heritage is for ostentatious reasons. Contemporary periodical necessities make her cherish and celebrate her Afro-American heritage. But Mrs. Johnson and Maggie have learnt to live with their heritage. Dee is captivated by the beauty of â€Å"churn top† and wanted to have it to be used as centerpiece for her alcove table whereas Mrs. Johnson has used it practically for churn butter hitherto. Walker utilizes the butter churn to demonstrate Mrs. Johnson’s intrinsic understanding of heritage. When [Dee] finished wrapping the dasher the handle stuck out. I took it for a moment in my hands. You didn’t even have to look close to see where hands pushing the dasher up and down to make butter had left a kind of sink in the wood. In fact, there were a lot of small sinks; you could see where thumbs and fingers had sunk into the wood. It was a beautiful light yellow wood, from a tree that grew in the yard where Big Dee and Stash had lived. (Walker, â€Å"Everyday Use† use page number from your textbook) The narration of the story is in first-person where everything is reported and understood through the lenses of Mrs. Johnson. Her observation is astute as she provides minute details about the actions of her daughters. Alice Walker has introduced various symbols and figurative language to communicate some thematic expressions. The conflict of heritage becomes more evident as well as critical as the quilts are brought in the story. It signifies the procedure from which the insignificant and worthless may be changed into the valued and functional. The development of Dee into Wangero shows various facets and phases through which black identity passed during late 1960s and 1970s. Predilection for appearance as compared with spirit remained hallmark of this era and this trend is manifested through Dee’s transformation into Wangero. Social standing of females was a favorite subject to the writers at the start of 20th century. Society was dominated by patriarchy, male chauvinism and supremacy whereas women were perceived as fragile and dependant. Every Day Use is a thematic expression of feminism and manifests it at various levels and in various orientations. It also revolves around the same theme which was basically about their feminist struggles. In â€Å"Everyday Use†, Alice juxtaposes the female character against the pathos and miseries of life and demonstrates their strong will and mental capacity to stand against those hardships. They are subservient to any male character and do not needs their companionship. Hence Alice augments their characterization by inculcating a female identity. Although Feminism stands for women to have the same status as men in the society but Alice exhibits another manifestation of Feminism i. e. not a wish to have equal status but a practical demonstration to contribute equally to the socio-cultural milieu of the time. Dee is an epitome of shallow materialistic feminism and an adherent of prevailing concept of feminism that believes in things and materialistic feministic achievements whereas Mrs. Johnson looks at the spirit of feminism and contributes toward it at her own level. She raises up two daughters by her own. This implies she has already taken the position of a father. These kinds of female have the ability to make their own decisions and have independence of thought and actions. The mother even shows how useful and strong a woman is. In the story, Alice Walker pointed out that the mother is â€Å"a large, big-boned woman with rough, man-working hands†. So Everyday Use is not a mere feministic wish to acquire equal status and should be considered and treated on equal terms but it is a practical manifestation of this feminist thought and ideology. Overall, Walker has invited us start living with our heritage instead of merely cherishing it. She further wants us to search our roots in the American soil instead of locating it on other continents. This story has eternalized the Afro-American themes in particular and feminist theme in general. The characteristic that makes the story universal and eternal is symbolic representation f the above-mentioned themes. It can be read and interpreted in various ways and at various levels.