Regression analysis is one tool in statistics that has worked wonders for researchers, scientists, and engineers in their scientific goals. It is a predictive modeling technique that brings out relationships between dependent and independent variables in different scenarios. If you visit sources like **regression analysis homework help**, you can learn the basics of regression analysis.

Provided a few occurrences of an event or phenomenon are available one can predict the future outcomes of that event through regression analysis. This technique gives a fair estimate of variables in terms of past events.

Is regression analysis useful in daily life? I feelyes, and it has been widely employed to solve real life problems. For instance, regression tool can predict the relationship between reckless driving and the frequency of road accidents on a certain highway within a certain time period.

Economists and business people also find regression analysis quite useful. Say a company wants to predict the overall sales in the coming year. It can perform regression analysis on its sales records of the past 3 years to get a fair idea about its fortunes in the coming days.

Mathematical terms explained in **regression analysis homework help** emphasizes on involvement of dependent and independent variables while calculating regression. The dependent variable is the target, and the independent variables predict the behavior of target. I found the sample examples of real time issues quite helpful in understanding regression concepts.

Regression calculations are also important indicators of how certain variables or factors influence performance of one variable. Take for example the prediction of carbon dioxide levels in environment as a function of rate of deforestation and incremental growth of industries. Data analysts feed on this property of regression to create predictive models.

Important points to note while calculating regression

- Nature of dependent variable
- Number of independent variables
- Shape of regression line

The most common type of regression technique used islinear regression.However, as you will read in **regression analysis homework help, **other forms of regression techniques also exist.

**Linear Regression**

Beginners in statistical modeling and data analysis are most familiar with linear regression which is the simplest form of calculation. The characteristic of linear regression is that the regression line is linear. Here the dependent variable is continuous while the independent variable can either be continuous or discrete.

Independent variables are generally represented by Y and dependent variable by X. Linear regression helps to predict the best future values of X with given values of Y. The resultant regression line from the calculations denotes the values of the target variable.

**Logistic Regression**

Logistic regression involves binary dependent variables and used to find chances of success and failure of events.Odds are calculated using logarithmic formulae.Hence the name logistic regression.

This technique is generally used for classification problems.A Linear relation is not required between dependent and independent variables. For a fair estimate of future values, one needs to employ a large number input values.

**Polynomial regression**

This technique involves independent variables with higher powers.Calculationis done through a polynomial equation.The extrapolated regression line is not actually a line but a simple curve. Variation in the shape of regression curve denotes how fairly the independent variables are selected.

**Stepwise regression**

If multiple independent variables are available, stepwise regression can be used. **Regression analysis homework help **mentions that in calculation of stepwise regression independent variables are added or dropped step wise using predefined criteria. The variables needed in each step are decided separately.

One way of stepwise regression is the forward selection method where the most significant variable is taken, and other variables added stepwise. On the other hand, backward elimination regression adds all variables in one go and eliminates the least significant variables in each step.

**Ridge Regression**

In regression analyses, independent variables are highly correlated. When the degree of col linearity is high, one can take up ridge regression. In this technique, variables are provided degree of bias to reduce errors.

In the calculating formula of linear regression, an error term is added.

**Lasso regression**

Like Ridge regression, Lasso regression adds a penalty feature in regression calculation. It is another way of variable selection of variables. Lasso regression can completely eliminate certain variables with its calculations.

**ElasticNet regression**

It is a combination of Ridge and Lasso regression. This technique comes handy for extremely correlated independent variables and when one needs a pin-point prediction of targets. The advantage of ElasticNet regression is the facility to use a unlimited number of variables.

**Robust regression**

It is another form of regression used to go around the shortcomings of traditional regression methods. It uses M-estimation, S-estimation or MM-estimation processes for prediction.

Regression analysis remains to date one of the most significant and simplest data prediction techniques used worldwide. Learn more on prediction methods from sources apart from **regression analysis homework help**. According to prevalent factors, other forms of regression can be employed like Bayesian regression, Quantile Regression, and LAD regression.