Regression Analysis

"There are several different kinds of relationships between variables. Before drawing a conclusion, you should first understand how one variable changes with the other. This means you need to establish how the variables are related - is the relationship linear or quadratic or inverse or logarithmic or something else" ("Relationship Between Variables ", n.d)

This is different from correlation because it is implying that one thing directly affects the other in an extremely specific way, without having alternate scenarios to which the affect could happen. It is also a lot harder to prove than proving a correlation. So many different components go into trying to prove a correlation, such as time order, correlation, and ruling out all alternative scenarios. Time order is important because it looks into if the situation has always been, or if a change in something brought out the causation. Causation is used to try to prove that A happened because of B, not A happened maybe because of B, because it also could have been C or

Correlation is defined as the occurrence of two of more things or events at the same time that might be associated with each other but are not necessarily connected by a cause and effect relationship. While on the other hand, causation is defined as the action of causing something to occur. In my opinion both causation and correlation are both difficult things to prove but I believe that causation is harder to prove than correlation. The problem with proving causation between events is that there is a possibility that there are other events that are correlated to the event we are trying to determine the causation. These correlated events could just be a coincidence that they are happening at the same time or it could be that theses events are the actual causation of the

There are endless possibilities when considering what type of modeling can be done with a data set, especially with the availability of “big data” in most communities. Predictive modeling is utilized by single health care agencies and large organizations alike, the only

Prospective data has been successfully collected on 182 adolescent males with institutionally documented histories of sexual offending. Structural equation modeling was used to assess theorized relationships between developmental risk factors, personality mediators, and sexual offense characteristics in predicting whether sexual offenses were committed against pubescent females or prepubescent children. Follow-up univariate regression analyses were conducted in support of more refined assessment of differences between the studied offender groups. Consistent with study hypotheses, offenders of children showed greater deficits in psychosocial functioning than offenders of pubescent females, were less aggressive in their sexual offending, and

Multivariate regression is a standard statistical tool that regresses independent variables (predictors) against a single dependent variable (response variable).The objective is to find a linear model that best predicts the dependent variable from the independent variables. In order to explain the data in the simplest way, redundant or unnecessary predictors should be removed. Such eliminating process is needed for the following reasons. First, unnecessary predictors will add noise to the estimation of other quantities that we are interested, causing loss in degrees of freedom in statistical point of view. Second, if the model is to be used for prediction, we can save time and/or money by not measuring redundant predictors. Finally, multi co-linearity is caused by having too many variables trying to do the same job.

A correlation between two variables can arise because both variables are related to some third variable that, to some degree, effects the two variables. In other words, a third variable may be the cause of the correlation between the two variables. In order for one to accurately establish a cause, any possible explanation would have to be ruled out. The only effective way to establish causality between variables is to conduct a true experiment. A true experiment is when a comparable sample or population is split into two, where both groups will receive different treatments such as having a group manipulated and the other controlled. Nevertheless, both groups will have their outcomes assessed. After the collection of data from the assessment is complete, then it should get organized in a table. However, the amount may be too overwhelming to draw a conclusion, so this is where a scatter plot will be useful. A scatterplot is a graph that is used to plot the data points for two variables and more importantly, provide a visual representation of the

Regression models are very useful in determination of important statistics in the corporate world. For instance, multiple regression models can be used to determine whether advertising, product loyalty, or price is the most important determinant of business growth. With this information, businesses are able to focus their resources into the channels which will help them achieve their targets effectively. It can also be used to calculate the predicted mileage for a vehicle with respect to different possible variables such as weight of vehicle, age of vehicle, and climate of country. Car manufacturers can capitalize on

Within the statistical connection correlations gives a plain description of the affiliation amongst two variables, however it isn’t a systematic investigation approach. To know if data information that is gathered from different research approaches such as investigations, inquiries, and tests should be examined to know whether there is a connection amongst the two variables or not. For instance, if an correlation connection is time spent fixing a 1990 car worth it or either purchasing a newer model car (Correlational research for A level psychology - Psych teacher)

A positive correlation means that as one variable increases, the other also increases. A negative correlation means that as one variable decreases, the other increase. This sets the range of correlation coefficients to be between 1 and -1. 1 being a strong positive correlation, 0 being the weakest correlation, and -1 being a strong negative correlation. To find the correlation coefficient, you can use this formula nxy-(x)(y)(nx2-(x)2)(ny2-(y)2)where n is the number of data points you have (if you have 3 temperatures and 3 different chirps per second, n is 3), y is the distance from a point to the best fit line by y coordinates (for y, it means the sum of each of these distances), and x is the distance from a point to the y-axis (for x, it means the sum of each of these distance). In short, bivariate data, like temperature in degrees and how many chirps per second a male cricket makes, can be graphed on a scatter plot. From this we can draw the best fit line and come up with the correlation coefficient. These tell us about the connection between the two variables. A strong positive correlation would tell us that there is a strong correlation between temperature and chirps per second and that the higher the temperature, the more chirps per second a male cricket

Multiple regressions are learning about the independent and dependent variables relationship between each other. It enables you to predict the value that is unknown from two or more variables. It assists with the prediction of the value of Y.

Correlation and Cause: Correlation is the relationship between two variables that tend to move in the same direction. Causation is the relationship in which a change in one variable creates a recognizable change in another variable. For example, many criminals are drug abusers but drug abuse does not cause crime because not everyone who abuses drugs is a criminal.

With regards to correlation, there are times where it would be nonsensical to conclude that a relationship between two variables would mean causation. First, consider the positive correlation between the average annual sales of cell phones and the average annual amount of influenza cases, meaning there is an increase in flu cases as cell phone sales increase. Then, by just looking at the relationship between cell phone sales and flu cases, it could easily be implied that buying a new cell phone causes influenza. Clearly this assumption is highly illogical, hence psychologists do not want the correlation to be released to the press or any forms of social media.

This presentation on Regression Analysis will relate to a simple regression model. Initially, the regression model and the regression equation will be explored. As well, there will be a brief look into estimated regression equation. This case study that will be used involves a large Chinese Food restaurant chain.

Table: 1, represents the results of regression analysis carried out with the dependent variables of cnx_auto, cnx_auto, cnx_bank, cnx_energy, cnx_finance, cnx_fmcg, cnx_it, cnx_metal, cnx_midcap, cnx_nifty, cnx_psu_bank, cnx_smallcap and with the independent variables such as CPI, Forex_Rates_USD, GDP, Gold, Silver, WPI_inflation. The coefficient of determination, denoted R² and pronounced as R squared, indicates how well data points fit a statistical model and the adjusted R² values in the analysis are fairly good which is more than 60%, indicates the considered model is fit for analysis. Also, the F-Statistics which provides the statistical significance of the model and its probabilities which are below 5% level and hence proves the model’s significance.