Simple Linear Regression
Context/overview:
One of the most important and broadly used machine learning and statistics tools that nowadays are used is a regression. Regression will allow you to make predictions from data by learning a relationship between data and response. We can see a use of regression in many applications, such as predicting stock prices, real estate- house pricing, etc.
For this project, we will analyze the most basic regression model, that is very frequently studied-Simple Linear Regression.
Resources to consult:
Artificial Intelligence- http://artint.info/html/ArtInt_179.html
Specific questions or items to address:
In your own words, describe the simple linear regression. Explain why it is called a simple linear regression, and how do we use a model to make predictions on new data.
Let x1, x2, …xN be a set of input features. A linear function that represent those features, will have a following form:
Fw(x1, x2, .. xn)= w0 + w1 * x1 + w2 * x2 +… + wn*xn
Also, in order to implement simple linear regression, it requires that we calculate statistical properties from the data such as mean, variance and covariance.
Please implement a simple linear regression model by providing a following:
Implement the functions Mean and Variance that calculate mean and variance
Use the data that is returned by the Mean and Variance function and implement a function Covariance, that will calculate covariance
Use the all previous implemented functions, and develop a function called CalculateCoefficients. Function CalculateCoefficients will take the dataset as an argument and returns the coefficients.
Implement a function SimpleLinearRegression that implements the prediction equation to make predictions on a test dataset. To make predictions, use The coefficients prepared from the training data.
Criteria: (# pages, APA format, etc.)
Take screenshots of functions running in cLISP, and paste them in a Word or Google doc. Provide a description for each screenshot.
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