Linear Vs Exponentials Models in Prediction

Linear Vs. Exponentials Models in Prediction
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Linear Vs. Exponentials Models in Prediction
The results of the graphs were as follows;
123825267335Linear
Exponential
A linear model is one that changes at a constant rate, i.e., the rate of change of x and the rate of change of y is constant. This model is used to determine whether a set of independent variables accurately predict the outcome of the dependent variable. Moreover, they find the variables that are the significant predictors of the outcome variable (“what is linear regression,” 2018). In short, they are used to estimate the relationship between a dependent and independent variable. They usually give a straight line in a graph. This means that the graphs have a constant slope or gradient.
On the other hand, the exponential function is a type of non-linear regression in which the rate of change is proportional to the value of the function. They are used to model situations where the growth begins gradually and then accelerates until decay. They usually produce a curved line in a graph.
In this modeling process, the exponential model is the best model and is the best representation of the data. This is because the difference between the observed values known as the residual errors is less compared to that of the linear regression model (“linear and exponential regression,” 2008). The linear model tends to have many errors because of trying to create the line of best fit that tends to have more errors. However, the curves of best fits as in the exponential regression models tend to have errors that are less pronounced.

This, therefore, shows that exponential graphs are better at prediction compared to linear graphs.
References
Linear and Exponential Regression. (2008). Zweigmedia.com. Retrieved 24 September 2018, from https://www.zweigmedia.com/RealWorld/calctopic1/regression.html
What is Linear Regression? (2018). Statistics Solutions. Retrieved 24 September 2018, from http://www.statisticssolutions.com/what-is-linear-regression/