Multicollinearity often causes huge interpretative problems in linear regression analysis. There is a large body of literature on different sources of multicollinearity (Montgomery et al., 2001; Kutner et al., 2005; Belsley et al., 1980). One of the important and almost inevitable sources of multicollinearity is the existence of high leverage points in the multiple regression models. There are three different groups of strange (or unusual) points that may occur in a data set. These points need further attention because their presences have a great influence on the OLS estimates (Moller et al., 2005). The first group is regression outliers which sometimes are called vertical outliers (Rousseeuw and Van Zomeren, 1990). These outliers stand apart from the general pattern for the bulk of the data. Specifically, they are observations which are discrepant in terms of their values. Relatively large residuals finely characterized regression outliers. The farther the observation is from the mean of (either in a positive or negative direction), the greater is its leverage.( Bagheri et al, 2010) The leverage points usually classified as good leverage points and bad leverage points. Good leverage points always consistent with the true regression line. Hence, bad leverage points are observations that not only deviate from the regression line that best fits the data but also fall far from the majority of the explanatory variables in the data set (see Montgomery et al., 2001; Kamruzzaman and Imon, 2002; Kutner et al., 2005; Chatterjee and Hadi, 2006). Additionally, observations which have unduly influence on the regression results are identified as influential observations (Bagheri et al, 2011). In the regression analysis, leverage point... ... middle of paper ... ...erent multicollinearity diagnostics (Montgomery.D.C, 2001). New robust VIFs developed which are based on robust coefficient determination ( ). Several robust coefficient determinations exist in the literature of robust method such as Rousseeuw and Hubert (1997) and Splus Robust Library User’s Guide (2001). The MM estimator which is introduced by Yohai (1987) is one of the robust methods which have desirable properties that attempts to downweight high leverage points as well as large residuals. A robust coefficient determination is proposed based on applying the MM estimator to fit the regression model. Following this, the robust VIF is developed. New proposed robust VIFs will be applied to a well known non collinear data set. To compare the performance of these robust multicollinearity diagnostics methods, a Monte Carlo simulation study will also be carried out.
The Smith & Wesson Holding Corporation stock has an EPS of 1.42 and a P/E ratio of 10.52. Upon running a regression, a coefficient of 0.139 was calculated. This means that if the SWHC stock increases by 1%, the S&P 500 stock will increase by 0.139%.When compared against the S&P 500 index, the SWHC stock has a correlation of 16.3%. This is relatively low. The SWHC stock can explain approximately 16.3% of the variation in the S&P 500. In other words, the stock does not behave the same as the S&P 500 and should not be used to predict the S&P 500. There is about 83.7% of the...
In order to find out what are some of the key drivers’ of the analysis I will further run different sensitivity analysis. I think some of the key drivers of our assumptions could be sales growth, production costs as a percentage of sales, inventories as a percentage of cost of goods sold etc.
Paunonen, S., & Ashton, M. (2001). Big-five factors and facets and the prediction of behavior.
In the book Outliers: The Story of Success Malcom Gladwell defines an outlier as something that is situated away from or classed differently from a main or related body and as a statistical observation that is markedly different in value from the others of the sample. Gladwell introduces the readers to the idea of outliers using Roseto Valfortore, a town one hundred miles south east of Rome. Gladwell considers Roseto an outlier because people there were simply just dying of old age, nothing else. Gladwell says “…Roseto--- a place that lay outside everyday experience, where the normal rules did not apply”(Gladwell 7).
Outliers is a nonfiction novel written by Malcolm Gladwell about peoples stories of success and how they got to where they are today. The Matthew Effect, The 10,000 Hour Rule, and Three Lessons of Joe Flom are all examples of looking at people’s specific variables to determine their success. A person’s birthday, how long someone has done something, having an opportunity, and a person’s ethnicity are all variables in explaining what has helped them become what they are today.
Usually we think of those anomalies as outliers, but people like many children in East Cobb, we are also considered outliers. We have so many more opportunities to succeed, we were born into the right family in the right place, and we learn many vital skills that many kids never learn. Outliers has given me a new perspective on success and makes me feel thankful for who I am and who surrounds me.
“A statistical observation that is markedly different in value from the others of the sample” (Gladwell 3) or in other words an outlier. In the novel Outliers: The Story of Success, author Malcolm Gladwell holds one of the many secrets to life, the secret to success. Gladwell takes one’s thoughts on an astonishing journey to reveal the keys to success, their patterns, and how to achieve it.
Correlation and regression analyses are interrelated in an approach that they both deal with the relationship between variables. Correlation portrays the strength of a relationship between two variables, and is entirely proportioned, the correlation between X and Y is identical as the correlation among Y and X. However, if the two variables are associated it means that when one changes by a definite amount the other changes on an average by a certain amount. While in regression, if Y represents the dependent variable and X the independent variable, this relationship is described as the regression of Y on X.
An Outlier is considered to be an individual that stands out in society because they do things out of the ordinary or norm. They are considered to be successful or different in a positive way in society. For example, people with high IQs like Albert Einstein, famous musicians like Mozart, and etc. are considered to be Outliers
The Multiple Regression is a sophisticated modeling technique, this model predicts the consumer behavior on the basis of many attributes all at the same time in the process unlike single attribute in Single Linear Regression. Unlike the Simple Linear Regression, this model comprises of multiple predictors or independent variables which help us reach the dependent variables. In marketing terms the independent variables can be age, income, product affinity etc. and the dependent variable is the answer to the marketers question for e.g. what are the chances that a particular segment of customer will positively react to a marketing promotion. This model is used by the direct marketers to build powerful targ...
The implications of these findings are as follows. The works of these academics highlight the important point that there is higher volatility of capital charges for better quality credits (Goodhart & Taylor, 2004). This is because these credits face a steeper risk curve, as the movement within the ratings scale (from one rating to another) is much greater.
Andrew A. Brennan Analysis , Vol. 47, No. 4 (Oct., 1987), pp. 225-230 published by: Oxford University Press on behalf of The Analysis CommitteeArticle Stable URL;http://www.jstor.org.ezproxy.taylors.edu.my/stable/3328797
An outlier is defined as something observed as significantly different (above or below) or lying outside the sample set or an average. With this paper, I intend to summarize Outliers by Malcolm Gladwell, as well as use it in describing why I think Lionel Messi is an outlier.
Example: A company increases its sales revenue from 10,000 to 20,000 of a particular month with the increase in advertisement expenses from 2,000 to 3,000. The leverage between both the variables are shown as given below:
The length of the estimation period is also significant in the estimation of beta. Blume (1971) depicts empirically that the stability of individual beta increases as the time of the estimation period increases. Similar results were obtained by Altman (1974) and Baesel (1974) who with the use of monthly data, estimated beta for estimation periods of one year, four years, six years and even ni...