Information System Management Essay Example for Free

Information System Management Essay A new concept in today’s IT world is offshore sourcing in Information Systems which is a paradigm shift from the traditional Business Process Outsourcing. Although the latter has been facilitated by the lucrative IT infrastructure, it is important to focus and emphasis on what has brought about offshore Information System sourcing. This journal article has theoretically explored on factors which the authors considers are the key drivers that can influence a company to go offshore. Additionally, to practically drive the point home, the article has exclusively carried out and explained a case study on ComputerInc, an Australian IT services company. Consequently, some of the key-driving factors include becoming and remaining competitive, reducing operational costs, and increasing the market share. Nevertheless, key challenges include overall strenuous management, staff demoralization, and lowered quality of services. This article is relevant in that the roles of the management for instance that of Chief Information Officer is critical in creating viable decisions. Such include venturing into IS offshore sourcing so as to drive organizations technologically and reap benefits such as reduced operation costs (McNurlin, et al. 2009, pp. 10-13). In the article, ComputerInc is argued to have increased its global market share as a result of IS offshore outsourcing (Beverakis et al. 2009, p. 35). In this regard, McNurlin, et al. (2009, pp. 17-20) have classified roles of IT infrastructure into three: working inward, working outside, and working across. Benefits include global positioning, partnering, and increasing the clientele base if/when proper IS planning is done by the decision makers. The article explains how viable decisions made by ComputerInc’s management drove the company into offshore outsourcing, attain a global position, and command a competitive market share (McNurlin, et al. 2009, p. 18). This corresponds to the learning outcomes of this course and for that reason I would award the article a value of four out of five on a score-scale. The Journal of Global Information Management is an academic journal published by the Information Resources Management Association. The Editor-in-chief for this journal is called Felix B Tan of Auckland University of Technology in New Zealand. Journal of Global Information Management is published on quarterly basis every year. In each year, a single volume is released but in each quarter the released journal is assigned a separate issue number. The journal is both online and in print and it is accessible at a personal level and to institutions. However, to access the journal subscription is a must whereby printed journal goes is sold at $ 545 and $ 195 to an institution and individual respectively (JGIM 2010). In all the issues of the Journal of Global Information Management emphasizes on all the concepts related to management of worldwide information resources. The journal creates a forum in which professionals and researchers disseminate current and surfacing information in both theoretical and practical perspective in relation to information technology and information resource management at global level. Therefore its main objective is put main emphasis on organizational and managerial aspects of Information Technology resources management. It covers on a range of issues such as policies, failure, usage, success, applications, and strategies of IT in business enterprises both in developed and emerging economies (JGIM 2010). The Journal of Global Information Management has laid out procedures whereby professionals and researchers especially in the field of information system management share their knowledge in regard to emerging challenges, posed by IT developments. Through its case studies, the journal proposes means on how to integrate information technology techniques into current managerial strategies. Therefore, it covers on the learning outcomes of this course such as role of IS managers, importance of IS/IT in driving companies to the global center-stage (JGIM 2010). In this article, it is acknowledged that information system offshore outsourcing has over the years increased drastically. In the research study, a number of steps involved in making such decisions at the management level have been identified. It states that IT managers weighs on the benefits and challenges of offshore outsourcing, evaluates the all the logistics involved in the process, and determines the prevailing geography. The research is based on literature review of existing companies’ statistical reports as presented in annual releases. The article states that offshore outsourcing surpasses onshore outsourcing in terms of benefits and risks involved. USA, UK, France, and Germany are the major IS offshore outsourcers in countries such as India, and China. Reduction in operation costs has been identified as the key motivator whereas it also stresses that quality services, security, and provider location must be considered. The key factors identified by Reyes, Jose and Juan (2006, p. 234) that influence offshore outsourcing are market and economic globalization, savings on operational costs in terms of salaries paid to the staff, shortage of skilled manpower, the need to reduce IS projects’ development time-cycle, and the growth or access to internet by large number of people (McNurlin, et al. 2009, pp. 7 33-39). This article demonstrates and emphasizes on key steps that information system managers of any organization have to take while making decisions on whether to outsource some of their services offshore. In comparison, McNurlin, et al. 2009, p. 11) in their book acknowledges the same that CIO’s should design policies, analyze possible benefits and risks so as to make informed decisions. In regard to the learning outcomes of ECOM20001, the article is explicit on what chief information officers are expected to do, have presented statistical evidence on how companies expand due to outsourcing and the impact of globalization on company activities in terms of risks and benefits. Considering such coverage on E-enablement and globalization I would award the article a score of five.

College Should Not be a Playground :: College Admissions Essays

College Should Not be a Playground University students today have it pretty good. At decent-sized schools, students have access to any number of low-cost services that civilians would donate organs for. We get gyms and fitness centers for free or close to it. We have computer labs, lounges and more clubs and societies arriving every semester. With little or no fees, on-campus coffee bars and pick-up basketball games make traveling into the real world increasingly ludicrous. Sure, we pay more in tuition rates to help off set the cost, but college students these days shouldn't sweat the bill's bundled-in activity fees - it's simply worth it to fork over a little extra cash for the added convenience. Besides, with college rates continually on the rise, these resource charges amount to a drop in a very large bucket. On the other hand, shouldn't a University provide for its students without bleeding them dry? After all, without the learners, the educators and administrators would be jobless. So why should students pay for access to increasingly basic and common services? Students have come to expect these tasty perks, as if our Universities owe us for passing through their hallowed halls. But have we come to expect too much? Do we truly deserve extravagant bonuses? My own school has for years given students free, unlimited, high-speed access to the Internet. All rooms in all dorms have long had an Ethernet port, intended to help us with our studies. Any student can plug in, call up the library's extensive database subscriptions, and hunt for journals, articles and other information on a boundless range of topics. Of course, with such power comes responsibility, for students can also visit the seedier and less, shall we say, academic nooks of the World Wide Web. In light of this, UMD began cracking down on Internet access and Networking capabilities on campus last year. First, the students' file-sharing capabilities were restricted. Many students grumbled, but the administration remained firm. Most recently, filters blocked the transfer of certain controversial file types. Student outcry led to a scaled-back version of the sentinel software, but the students haven't finished crusading. The school, they say, has infringed on our rights by installing restrictive programs between the Internet and us. University literature promises "free, unlimited" Internet access, and

Dry Shampoo

How do marketers assess the need of a product to market it†¦ In today’s competitive, global environment, new products and innovation are critical to a company’s growth and sustainability. Many companies today focus only on cost reduction. Generating revenue via new, differentiated products should also be part of the corporate strategy. Product development must be done within a strategic context that takes into account emerging market trends, environmental and regulatory rulings and trends, customer and employee needs and wants, and financial considerations.The development and launching of new products is perceived as a risk due to uncertainties of success after significant investments. In addition, the product development process is not well understood by most firms. Finding, developing, and exploiting new product growth can help corporations to maximize latent value in their new innovative products and growing markets, while diversifying risk. It also allows busin esses to focus on evolving macro and micro markets and to enhance customer satisfaction and competitive advantage. Begin market planning by clearly identifying the market you want to target.This may or may not be the market you are working with now. The idea is to think creatively about your product to determine what set of customers are going to give your business the cash flow, profit and growth it needs. Suppose I am going to start a business of â€Å"DRY SHAMPOO† as it is quite a new product for Bangladesh perspectives†¦ The so-called â€Å"French shower,† that curious Napoleonic custom of applying perfume or deodorant over unwashed flesh, went out of style with pantaloons, and certainly never spread to these more hygienic shores.Right? Hello, dry shampoo. Touted as a water and timesaving way to stay quote-unquote gorgeous on the go, these wildly popular shampoo substitutes allow the busiest exec to head straight from the bedroom to the boardroom without a pes ky shower in between. Just apply a cumulus of powder to the scalp, wait two minutes before brushing it out, and Fabulist achieved. Dry shampoo is a powdered substance used to clean the hair when you want to extend the length of a blowout or when it is not practical to use water and traditional shampoo.The market for dry shampoos, which are sold in both spray-on and powdered formulas, has exploded over the past few years. Name a high-end hair-care brand—Frederic Fekkai, Bumble and bumble, Oscar Blandi, Rene Futerer—and the chances are good that a revolutionary new dry-shampoo product is one of the top-selling items in the company's inventory. Their average price overs around $20 for about 3 ounces—not exactly a bargain. So, how well do they work? Old-fashioned wet shampoo cleans hair of all of the assorted gunk and free radicals that accumulate over the course of a day, as well as its natural oils, which are known as sebum.Dry shampoo, which usually has a base of talc, cornstarch, potato, or rice, soaks up rather than washes away sebum and dirt. When you brush out the powder, you're also (allegedly) brushing out the grime, too. Because the soak-up/brush-out method doesn't rid the hair of as much sebum, you can safely use dry shampoos once or several times between regular shampooing. But alert: Because dry shampoos are essentially spray-on powders, they can, even after vigorous brushing, lighten the crown of your head, which can be good or bad, depending on your desired hair color.Why Use Dry Shampoo? Fab Hair, Fewer Washes Over washing your hair can dry it out and cause hair color to fade. To preserve your color and maintain moisturized, sleek hair, it is best to wash your hair only 2-3 times per week. Additionally, if you’re strapped for time and you need to freshen up your locks (after the gym, before a night out, etc. ), dry shampoo serves as a fantastic option. Modern, busy women swear by dry shampoos! So the question is.. Who wi ll buy my product? Why will they buy my product? What will they pay for my product?Where do they expect to find this product? When spoke to a half-dozen dry shampoo devotees about their reliance on these potions. One â€Å"natural† said dry shampoo helps her disguise suspicious roots on the brink of her next highlight appointment, since the powder tends to lighten the hair. Another turns to it when she can't submit to the 45-minute blow-dry required to tame her frizzy curls. Then the third one who use it after midday workouts and the partygoers who want to refresh their appearance in the office bathroom.The marketer may recruit 2 or three testers for analyzing the range of hair types who are chemically enhanced one with thick hair and a schedule that only allows her to hit the gym during lunch. The second has thin hair that looks flat and oily by the end of the workday; she'd prefer to take a second shower before any nighttime assignations. The third one has thick, wavy, jet- black hair that requires herculean efforts to manage. All three work full-time and shampoo daily. generally obedient hair that never, ever wash on a daily basis, having been taught early on that too-frequent washing strips and damages hair over the long run.

Regression Of Financial Techniques Example For Free - Free Essay Example

Sample details Pages: 14 Words: 4234 Downloads: 9 Date added: 2017/06/26 Category Finance Essay Type Analytical essay Did you like this example? The Pearson Correlation Coefficient (r) or correlation coefficient for short is a measure of the degree of linear relationship between two variables. While in regression the emphasis is on predicting one variable from the other, in correlation the emphasis is on the degree to which a linear model may describe the relationship between two variables. In regression the interest is directional, one variable is predicted and the other is the predictor; in correlation the interest is non-directional, the relationship is the critical aspect. Don’t waste time! Our writers will create an original "Regression Of Financial Techniques Example For Free" essay for you Create order The coefficient of correlation can vary from positive one (indicating a perfect positive relationship), through zero (indicating the absence of a relationship), to negative one (indicating a perfect negative relationship). As a rule of thumb, correlation coefficients between .00 and .30 are considered weak, those between .30 and .70 are moderate and coefficients between .70 and 1.00 are considered high. Correlation between NSE and FII Correlations FII NSE FII Pearson Correlation 1 .313** Sig. (2-tailed) .002 N 96 96 NSE Pearson Correlation .313** 1 Sig. (2-tailed) .002 N 96 96 **. Correlation is significant at the 0.01 level (2-tailed). The above table is showing the relationship among the NSE and FII which is taken from year Jan 2003 to Dec 2010. The Pearson correlation coefficient measures the linear association between two scale variables. The correlation reported in the table is 0.313. This suggests that NSE and FII are showing a weak positive relationship i.e. with the increase in FII, NSE indices increases and vice versa. Correlation between Sensex and FII Correlations FII SENSEX FII Pearson Correlation 1 .306** Sig. (2-tailed) .002 N 96 96 SENSEX Pearson Correlation .306** 1 Sig. (2-tailed) .002 N 96 96 **. Correlation is significant at the 0.01 level (2-tailed). The above table is showing the relationship among the Sensex and FII which is taken from year Jan 2003 to Dec 2010. The Pearson correlation coefficient measures the linear association between two scale variables. The correlation reported in the table is 0.306. This suggests that Sensex and FII are showing a weak positive relationship i.e. with the increase in FII, Sensex indices increases and vice versa. Correlation between Consumer Durables and FII Correlations FII CONSUMER DURABLES FII Pearson Correlation 1 .310** Sig. (2-tailed) .002 N 96 96 CONSUMER DURABLES Pearson Correlation .310** 1 Sig. (2-tailed) .002 N 96 96 **. Correlation is significant at the 0.01 level (2-tailed). The above table is showing the relationship among the Consumer Durables and FII which is taken from year Jan 2003 to Dec 2010. The Pearson correlation coefficient measures the linear association between two scale variables. The correlation reported in the table is 0.310. This suggests that Consumer Durables and FII are showing a weak positive relationship i.e. with the increase in FII, Consumer Durables indices increases and vice versa. Correlation between Capital Goods and FII Correlations FII Capital Goods FII Pearson Correlation 1 .265** Sig. (2-tailed) .009 N 96 96 Capital Goods Pearson Correlation .265** 1 Sig. (2-tailed) .009 N 96 96 **. Correlation is significant at the 0.01 level (2-tailed). The above table is showing the relationship among the Capital Goods and FII which is taken from year Jan 2003 to Dec 2010. The Pearson correlation coefficient measures the linear association between two scale variables. The correlation reported in the table is 0.265. This suggests that Capital Goods and FII are showing a weak positive relationship i.e. with the increase in FII, Capital Goods indices increases and vice versa. Correlation between FMCG and FII Correlations FII FMCG FII Pearson Correlation 1 .340** Sig. (2-tailed) .001 N 96 96 FMCG Pearson Correlation .340** 1 Sig. (2-tailed) .001 N 96 96 **. Correlation is significant at the 0.01 level (2-tailed). The above table is showing the relationship among the FMCG and FII which is taken from year Jan 2003 to Dec 2010. The Pearson correlation coefficient measures the linear association between two scale variables. The correlation reported in the table is 0.340. This suggests that FMCG and FII are showing a moderate positive relationship i.e. with the increase in FII, FMCG indices increases and vice versa. Correlation between Health Care and FII Correlations FII Health_Care FII Pearson Correlation 1 .375** Sig. (2-tailed) .000 N 96 96 Health_Care Pearson Correlation .375** 1 Sig. (2-tailed) .000 N 96 96 **. Correlation is significant at the 0.01 level (2-tailed). The above table is showing the relationship among the Health Care and FII which is taken from year Jan 2003 to Dec 2010. The Pearson correlation coefficient measures the linear association between two scale variables. The correlation reported in the table is 0.375. This suggests that Health Care and FII are showing a moderate positive relationship i.e. with the increase in FII, Health Care indices increases and vice versa. Correlation between IT and FII Correlations FII IT FII Pearson Correlation 1 .337** Sig. (2-tailed) .001 N 96 96 IT Pearson Correlation .337** 1 Sig. (2-tailed) .001 N 96 96 **. Correlation is significant at the 0.01 level (2-tailed). The above table is showing the relationship among the IT and FII which is taken from year Jan 2003 to Dec 2010. The Pearson correlation coefficient measures the linear association between two scale variables. The correlation reported in the table is 0.337. This suggests that IT and FII are showing a moderate positive relationship i.e. with the increase in FII, IT indices increases and vice versa. Linear Regression Regression between NSE FII Variables Entered/Removedb Model Variables Entered Variables Removed Method 1 FIIa . Enter a. All requested variables entered. b. Dependent Variable: NSE Model Summary Model R R Square Adjusted R Square Std. Error of the Estimate 1 .313a .098 .088 1449.98694 a. Predictors: (Constant), FII ANOVAb Model Sum of Squares df Mean Square F Sig. 1 Regression 2.146E7 1 2.146E7 10.205 .002a Residual 1.976E8 94 2102462.122 Total 2.191E8 95 a. Predictors: (Constant), FII b. Dependent Variable: NSE Coefficientsa Model Unstandardized Coefficients Standardized Coefficients t Sig. B Std. Error Beta 1 (Constant) 3157.700 167.700 18.829 .000 FII .062 .019 .313 3.195 .002 a. Dependent Variable: NSE The above Regression is between NSE and FII the above tables are showing R, the multiple correlation coefficient, is the linear correlation between the observed and model-predicted values of the dependent variable. Its large value indicates a strong relationship. R is 0.313 that is showing strong correlation between NSE and FII and also R Square, the coefficient of determination, is the squared value of the multiple correlation coefficient. R ² which is 0.098 that is 10% of the variations are showing by this model. The significance value of the F statistic is less than 0.05, which means that the variation explained by the model is not due to chance. The ANOVA table is a useful test of the models ability to explain any variation in t he dependent variable; it does not directly address the strength of that relationship. Regression between Sensex FII Variables Entered/Removedb Model Variables Entered Variables Removed Method 1 FIIa . Enter a. All requested variables entered. b. Dependent Variable: SENSEX Model Summaryb Model R R Square Adjusted R Square Std. Error of the Estimate 1 .306a .094 .084 4976.73956 a. Predictors: (Constant), FII b. Dependent Variable: SENSEX ANOVAb Model Sum of Squares df Mean Square F Sig. 1 Regression 2.409E8 1 2.409E8 9.727 .002a Residual 2.328E9 94 2.477E7 Total 2.569E9 95 a. Predictors: (Constant), FII b. Dependent Variable: SENSEX Coefficientsa Model Unstandardized Coefficients Standardized Coefficients t Sig. B Std. Error Beta 1 (Constant) 10493.779 575.591 18.231 .000 FII .208 .067 .306 3.119 .002 a. Dependent Variable: SENSEX The above Regression is between Sensex FII the above tables are showing R, the multiple correlation coefficient, is the linear correlation between the observed and model-predicted values of the dependent variable. Its large value indicates a strong relationship. R is 0.306 that is showing strong correlation between Sensex FII and also R Square, the coefficient of determination, is the squared value of the multiple correlation coefficient. R ² which is 0.094 that is 10% of the variations are showing by this model. The significance value of the F statistic is less than 0.05, which means that the variation explained by the model is not due to chance. The ANOVA table is a useful test of the models ability to explain any variation in the dependent variable; it does not directly address the strength of that relationship. Regression between Consumer Durables FII Variables Entered/Removedb Model Variables Entered Variables Removed Method 1 FIIa . Enter a. All requested variables entered. b. Dependent Variable: CONSUMER_DURABLES Model Summary Model R R Square Adjusted R Square Std. Error of the Estimate 1 .310a .096 .086 1535.08070 a. Predictors: (Constant), FII ANOVAb Model Sum of Squares df Mean Square F Sig. 1 Regression 2.351E7 1 2.351E7 9.976 .002a Residual 2.215E8 94 2356472.743 Total 2.450E8 95 a. Predictors: (Constant), FII b. Dependent Variable: CONSUMER_DURABLES Coefficientsa Model Unstandardized Coefficients Standardized Coefficients t Sig. B Std. Error Beta 1 (Constant) 2636.743 177.542 14.851 .000 FII .065 .021 .310 3.159 .002 a. Dependent Variable: CONSUMER_DURABLES The above Regression is between Consumer Durables FII the above tables are showing R, the multiple correlation coefficient, is the linear correlation between the observed and model-predicted values of the dependent variable. Its large value indicates a strong relationship. R is 0.310 that is showing strong correlation between Consumer Durables FII and also R Square, the coefficient of determination, is the squared value of the multiple correlation coefficient. R ² which is 0.096 that is 10% of the variations are showing by this model. The significance value of the F statistic is less than 0.05, which means that the variation explained by the model is not due to chance. The ANOVA table is a useful test of the models a bility to explain any variation in the dependent variable; it does not directly address the strength of that relationship. Regression between Capital Goods FII Variables Entered/Removedb Model Variables Entered Variables Removed Method 1 FIIa . Enter a. All requested variables entered. b. Dependent Variable: Capital_Goods Model Summary Model R R Square Adjusted R Square Std. Error of the Estimate 1 .265a .070 .060 5141.56855 a. Predictors: (Constant), FII ANOVAb Model Sum of Squares df Mean Square F Sig. 1 Regression 1.877E8 1 1.877E8 7.101 .009a Residual 2.485E9 94 2.644E7 Total 2.673E9 95 a. Predictors: (Constant), FII b. Dependent Variable: Capital_Goods Coefficientsa Model Unstandardized Coefficients Standardized Coefficients t Sig. B Std. Error Beta 1 (Constant) 7531.033 594.654 12.665 .000 FII .184 .069 .265 2.665 .009 a. Dependent Variable: Capital_Goods The above Regression is between Capital Goods FII the above tables are showing R, the multiple correlation coefficient, is the linear correlation between the observed and model-predicted values of the dependent variable. Its large value indicates a strong relationship. R is 0.265 that is showing strong correlation between Capital Goods FII and also R Square, the coefficient of determination, is the squared value of the multiple correlation coefficient. R ² which is 0.070 that is 7% of the variations are showing by this model. The significance value of the F statistic is less than 0.05, which means that the variation explained by the model is not due to chance. The ANOVA table is a useful test of the models ability to exp lain any variation in the dependent variable; it does not directly address the strength of that relationship. Regression between FMCG FII Variables Entered/Removedb Model Variables Entered Variables Removed Method 1 FIIa . Enter a. All requested variables entered. b. Dependent Variable: FMCG Model Summary Model R R Square Adjusted R Square Std. Error of the Estimate 1 .340a .116 .106 734.24192 a. Predictors: (Constant), FII ANOVAb Model Sum of Squares df Mean Square F Sig. 1 Regression 6630011.310 1 6630011.310 12.298 .001a Residual 5.068E7 94 539111.190 Total 5.731E7 95 a. Predictors: (Constant), FII b. Dependent Variable: FMCG Coefficientsa Model Unstandardized Coefficients Standardized Coefficients t Sig. B Std. Error Beta 1 (Constant) 1718.599 84.920 20.238 .000 FII .035 .010 .340 3.507 .001 a. Dependent Variable: FMCG The above Regression is between FMCG FII the above tables are showing R, the multiple correlation coefficient, is the linear correlation between the observed and model-predicted values of the dependent variable. Its large value indicates a strong relationship. R is 0.340 that is showing strong correlation between FMCG FII and also R Square, the coefficient of determination, is the squared value of the multiple correlation coefficient. R ² which is 0.116 that is 12% of the variations are showing by this model. The significance value of the F statistic is less than 0.05, which means that the variation explained by the model is not due to chance. The ANOVA table is a useful test of the models ability to explain any variation in the d ependent variable; it does not directly address the strength of that relationship. Regression between Health Care FII Variables Entered/Removedb Model Variables Entered Variables Removed Method 1 FIIa . Enter a. All requested variables entered. b. Dependent Variable: Health_Care Model Summary Model R R Square Adjusted R Square Std. Error of the Estimate 1 .375a .141 .132 1132.37039 a. Predictors: (Constant), FII ANOVAb Model Sum of Squares df Mean Square F Sig. 1 Regression 1.976E7 1 1.976E7 15.409 .000a Residual 1.205E8 94 1282262.700 Total 1.403E8 95 a. Predictors: (Constant), FII b. Dependent Variable: Health_Care Coefficientsa Model Unstandardized Coefficients Standardized Coefficients t Sig. B Std. Error Beta 1 (Constant) 3185.402 130.966 24.322 .000 FII .060 .015 .375 3.925 .000 a. Dependent Variable: Health_Care The above Regression is between Health Care FII the above tables are showing R, the multiple correlation coefficient, is the linear correlation between the observed and model-predicted values of the dependent variable. Its large value indicates a strong relationship. R is 0.375 that is showing strong correlation between Health Care FII and also R Square, the coefficient of determination, is the squared value of the multiple correlation coefficient. R ² which is 0.141 that is 14% of the variations are showing by this model. The significance value of the F statistic is less than 0.05, which means that the variation explained by the model is not due to chance. The ANOVA table is a useful test of the models ability to explain any variation in the dependent variable; it does not directly address the strength of that relationship. Regression between IT FII Variables Entered/Removedb Model Variables Entered Variables Removed Method 1 FIIa . Enter a. All requested variables entered. b. Dependent Variable: IT Model Summary Model R R Square Adjusted R Square Std. Error of the Estimate 1 .337a .114 .104 1334.92982 a. Predictors: (Constant), FII ANOVAb Model Sum of Squares df Mean Square F Sig. 1 Regression 2.150E7 1 2.150E7 12.065 .001a Residual 1.675E8 94 1782037.614 Total 1.890E8 95 a. Predictors: (Constant), FII b. Dependent Variable: IT Coefficientsa Model Unstandardized Coefficients Standardized Coefficients t Sig. B Std. Error Beta 1 (Constant) 3256.374 154.393 21.091 .000 FII .062 .018 .337 3.473 .001 a. Dependent Variable: IT The above Regression is between IT FII the above tables are showing R, the multiple correlation coefficient, is the linear correlation between the observed and model-predicted values of the dependent variable. Its large value indicates a strong relationship. R is 0.337 that is showing strong correlation between IT FII and also R Square, the coefficient of determination, is the squared value of the multiple correlation coefficient. R ² which is 0.114 that is 11% of the variations are showing by this model. The significance value of the F statistic is less than 0.05, which means that the variation explained by the model is not due to chance. The ANOVA table is a useful test of the models ability to explain any variation in the depend ent variable; it does not directly address the strength of that relationship. Granger Causality Correlation does not necessarily imply causation in any meaningful sense of that word. The econometric graveyard is full of magnificent correlations, which are simply spurious or meaningless. Interesting examples include a positive correlation between teachers salaries and the accident rate in the city. Economists debate correlations which are less obviously meaningless. The Granger (1969) approach to the question of whether causes is to see how much of the current can be explained by past values of and then to see whether adding lagged values of can improve the explanation. is said to be Granger-caused by if helps in the prediction of , or equivalently if the coefficients on the lagged s are statistically significant. Note that two-way causation is frequently the case; Granger causes and Granger causes . It is important to note that the statement Granger causes does not imply that is the effect or the result of . Granger causality measures precedence and information conten t but does not by itself indicate causality in the more common use of the term. Granger Causality test between FII and Sensex Pairwise Granger Causality Tests Date: 03/06/11 Time: 17:53 Sample: 1 96 Lags: 2   Null Hypothesis: Obs F-Statistic Prob. Â  SENSEX_RETURN does not Granger Cause FII   94   0.17300 0.8414   FII does not Granger Cause SENSEX_RETURN   0.89225 0.4134 This shows influence of FII inflow on Indian Stock Market. The probability in all the above tests is more than 0.05 at which you can reject the null hypothesis. The above table shows that there is cause and effect relationship between Sensex and Net FII because the probability is greater than 0.05 thus FII investment patterns affects the Sensex. This relationship is also depicted when during Jan 08 to Feb 09 FII net sales were for Rs. 58.751.70 Crore and as a result Sensex fell from 17648.71 in Jan 08 to 8891.61 in Feb 09, i.e. a drop of 8757.10 which was approximately 49% in just over a year. Granger Causality test between FII and NSE Pairwise Granger Causality Tests Date: 03/06/11 Time: 21:11 Sample: 1 96 Lags: 2   Null Hypothesis: Obs F-Statistic Prob. Â  NSE_RETURN does not Granger Cause FII   94   0.05355 0.9479   FII does not Granger Cause NSE_RETURN   0.45150 0.6381 This shows influence of FII inflow on Indian Stock Market. The probability in all the above tests is more than 0.05 at which you can reject the null hypothesis. The above table shows that there is cause and effect relationship between NSE and Net FII because the probability is greater than 0.05. Thus, FII investment patterns affect the NSE. This relationship is also depicted when during Jan 08 to Feb 09 FII net sales were for Rs. 58.751.70 Crore and as a result NSE fell from 5137.45 in Jan 08 to 2763.65 in Feb 09, i.e. a drop of 2373.80 which was approximately 46% in just over a year. Granger Causality test between FII and BSE Consumer Durables Pairwise Granger Causality Tests Date: 03/06/11 Time: 17:54 Sample: 1 96 Lags: 2   Null Hypothesis: Obs F-Statistic Prob. Â  CONSUMER_DURABLES_RETURN does not Granger Cause FII   94   0.30374 0.7388   FII does not Granger Cause CONSUMER_DURABLES_RETURN   0.93166 0.3977 This shows influence of FII inflow on Indian Stock Market. The probability in all the above tests is more than 0.05 at which you can reject the null hypothesis. The above table shows that there is cause and effect relationship between BSE Consumer Durables and Net FII because the probability is greater than 0.05. Thus, FII investment patterns affect the BSE Consumer Durables. This relationship is also depicted when during Jan 08 to Feb 09 FII net sales were for Rs. 58.751.70 Crore and as a result BSE Consumer Durables fell from 5103.86 in Jan 08 to 1542.67 in Feb 09, i.e. a drop of 3561.19 which was approximately 70% in just ov er a year. Granger Causality test between FII and BSE Capital Goods Pairwise Granger Causality Tests Date: 03/06/11 Time: 17:55 Sample: 1 96 Lags: 2   Null Hypothesis: Obs F-Statistic Prob. Â  CAPITAL_GOODS_RETURN does not Granger Cause FII   94   0.24639 0.7821   FII does not Granger Cause CAPITAL_GOODS_RETURN   0.11010 0.8959 This shows influence of FII inflow on Indian Stock Market. The probability in all the above tests is more than 0.05 at which you can reject the null hypothesis. The above table shows that there is cause and effect relationship between BSE Capital Goods and Net FII because the probability is greater than 0.05. Thus, FII investment patterns affect the BSE Capital Goods. This relationship is also depicted when during Jan 08 to Feb 09 FII net sales were for Rs. 58.751.70 Crore and as a result BSE Capital Goods fell from 16387.70 in Jan 08 to 5897.92 in Feb 09, i.e. a drop of 10489.78 which is approximately 64% in just over a year. Granger Causality test between FII and BSE FMCG Pairwise Granger Causality Tests Date: 03/06/11 Time: 17:55 Sample: 1 96 Lags: 2   Null Hypothesis: Obs F-Statistic Prob. Â  FMCG_RETURN does not Granger Cause FII   94   0.08588 0.9178   FII does not Granger Cause FMCG_RETURN   2.04411 0.1355 This shows influence of FII inflow on Indian Stock Market. The probability in all the above tests is more than 0.05 at which you can reject the null hypothesis. The above table shows that there is cause and effect relationship between BSE FMCG and Net FII because the probability is greater than 0.05. Thus, FII investment patterns affect the BSE FMCG. This relationship is also depicted when during Jan 08 to Feb 09 FII net sales were for Rs. 58.751.70 Crore and as a result BSE FMCG fell from 2167.34 in Jan 08 to 2043.26 in Feb 09, i.e. a drop of only 124.08 which is approximately 6% in over a year. Granger Causality test between FII and BSE Health Care Pairwise Granger Causality Tests Date: 03/06/11 Time: 17:56 Sample: 1 96 Lags: 2   Null Hypothesis: Obs F-Statistic Prob. Â  HEALTH_CARE_RETURN does not Granger Cause FII   94   0.40226 0.6700   FII does not Granger Cause HEALTH_CARE_RETURN   1.34690 0.2653 This shows influence of FII inflow on Indian Stock Market. The probability in all the above tests is more than 0.05 at which you can reject the null hypothesis. The above table shows that there is cause and effect relationship between BSE Health Care and Net FII because the probability is greater than 0.05. Thus, FII investment patterns affect the BSE Health Care. This relationship is also depicted when during Jan 08 to Feb 09 FII net sales were for Rs. 58.751.70 Crore and as a result BSE Health Care fell from 3603.52 in Jan 08 to 2597.00 in Feb 09, i.e. a drop of 1006.52 which is approximately 27% in just over a year. Granger Causality test between FII and BSE IT Pairwise Granger Causality Tests Date: 03/06/11 Time: 17:56 Sample: 1 96 Lags: 2   Null Hypothesis: Obs F-Statistic Prob. Â  IT_RETURN does not Granger Cause FII   94   0.15074 0.8603   FII does not Granger Cause IT_RETURN   2.36452 0.0999 This shows influence of FII inflow on Indian Stock Market. The probability in all the above tests is more than 0.05 at which you can reject the null hypothesis. The above table shows that there is cause and effect relationship between BSE IT and Net FII because the probability is greater than 0.05. Thus, FII investment patterns affect the BSE IT. This relationship is also depicted when during Jan 08 to Feb 09 FII net sales were for Rs. 58.751.70 Crore and as a result BSE IT fell from 2096.17 in Feb 09, i.e. a drop of 1613.94 which is approximately 43% in just over a year. FINDINGS, SUGGESTIONS AND CONCLUSION The findings Suggestions of the research are: All the dependent variables taken for the research i.e. Sensex, NSE, BSE capital goods, BSE consumer durables, BSE FMCG, BSE Health Care BSE IT have shown positive correlation with FII equity investment patterns i.e. with the increase in FII, Various Stock indices also increases and vice versa. From the data we can observe, worst bearish phase was from Jan 08 to Feb 09. During this phase FII net sales was for Rs. 58.751.70 Crore and as a result of such withdrawals: Sensex fell from 17648.71 in Jan 08 to 8891.61 in Feb 09, i.e. a drop of 8757.10 which was approximately 49% in just over a year. NSE fell from 5137.45 in Jan 08 to 2763.65 in Feb 09, i.e. a drop of 2373.80 which was approximately 46% in just over a year. BSE Consumer Durables fell from 5103.86 in Jan 08 to 1542.67 in Feb 09, i.e. a drop of 3561.19 which was approximately 70% in just over a year. BSE Capital Goods fell from 16387.70 in Jan 08 to 5897.92 in Feb 09, i.e. a drop of 10489.78 which is approximately 64% in just over a year. BSE FMCG fell from 2167.34 in Jan 08 to 2043.26 in Feb 09, i.e. a drop of only 124.08 which is approximately 6% in over a year. BSE Health Care fell from 3603.52 in Jan 08 to 2597.00 in Feb 09, i.e. a drop of 1006.52 which is approximately 27% in just over a year. BSE IT fell from 3710.11 in Jan 08 to 2096.17 in Feb 09, i.e. a drop of 1613.94 which is approximately 43% in just over a year. BSE FMCG and BSE Health Care have shown the most resistance to FII withdrawals, this is due to the fact that both of these sectors are Defensive Sectors, they have a low Beta. Besides FII Other Macroeconomic variables like inflation, Govt. policies etc also affect the various stock market indices. CONCLUSION In developing countries like India foreign capital helps in increasing the productivity of labour and to build up foreign exchange reserves to meet the current account deficit. Foreign Investment provides a channel through which country can have access to foreign capital. This research helps to find out empirical relationship among FII equity investments and stock market indices. According to Data analysis and findings, it can be concluded that FII do have significant impact on the Indian Stock Market but there are other factors like government policies, budgets, inflation, economical and political condition, etc. do also have an impact on the Indian stock market. There is a positive correlation between various stock indices and FIIs i.e. with increase in FIIs investment, various stock indices also increases and vice versa. Retail investors can also keep a watch at FIIs investment data and derive benefit from it; since FIIs have better exposure to market informations than ret ail investors. FIIs also results in increased volatility in stock markets. FIIs have their advantages as well as disadvantages; Govt. needs to regulate FIIs so as to reduce their impact on stock markets and should try to develop domestic sources of funds to enhance growth. REFRENCES Bose Suchismita and Coondoo Dipaankar (2005): The Impact of FII Regulations in India, Journal: International Journal of financial market trends. Vol 30. Publisher: MCB UP Ltd Chakrabarti (2001), Journal: Journal of foreign institution investments Vol 27. Publisher: SSRN Group Publishing Limited Khan, Mohd. Amir; Goyal, Siddarth; Ranjan, Vinit; Agrawal, Gaurav; Investigation of Causality between FIIs Investment and Stock Market Returns, International Research Journal of Finance and Economics, Issue 40, 2010. 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