Reference no: EM131745415
Final Project
Instructions:
In this project, you will write two object classes and a helper function to read in text data.
Three Python files have been provided for you to complete:
- data.py
- linear_regression.py
- read_data.py
You will need to edit these files and push them to your ncsu repo:
- You may work together, but you must list all collaborators in your read_data.py file.
- Please no copy and pasting among collaborators. You'll learn better if you discuss and write your code on your own.
- If you use, online or other resources, also list them in your read_data.py file.
Part I: Reading data into two lists. Edit the file read_data.py
1. In the file read_data.py, define a function read_data that takes in a string called input_file_name that contains a path to an input file. Assume that the input file path contains two floating point number per line separated by spaces. Your function should return two lists whose elements are those floating point numbers contained in the input file.
Part II: Data class and methods. Edit the file data.py
2. Write a Data class with an __init__ method that takes two lists of numbers as parameters and stores them in two attributes x and y.
3. Write a Data method called num_obs that takes in no parameters and outputs the number of observations in the data, i.e. the length of its attribute x (or y since they should be the same.).
4. Write a __str__ method that returns a string that gives the following format when printed.
>>> dat = Data([1,2,3],[2,4,6])
>>> print(dat)
x y
1 2
2 4
3 6
5. Write a Data method called compute_sample_means that returns sample mean of the attributes x and sample mean of the attributes y, as a tuple of numbers.
6. Write a Data method called compute_least_squares_fit that returns the intercept and slope of the simple linear regression ?t of the data.
Hint: First compute the quantities Sxy and Sxx. Then compute the slope β1 and then finally compute the intercept β0.
7. Write a Data method called compute_SST that returns the sum of squares total (SST) of the attribute y.
Part III: SLR class and methods. Edit the file linear_regression.py
8. Write the SLR class with an __init__ method that takes in a Data object as a parameter. The __init__ method should store the contents of the parameter in an attribute called data. It should also compute the intercept β0 and slope β1 and store theses values in the attributes beta0 and beta1, respectively.
9. Write the SLR method predict that takes a new x_new value as a parameter and predicts a new y_new value.
10. Write the SLR method compute_residuals that returns the residuals of the SLR object in a list of numbers.
11. Write the SLR method compute_SSResid that returns the sum of square residuals (SSResid).
12. Write the SLR method compute_residual_sd that returns the residual standard deviation.
13. Write the SLR method compute_rsquared that returns the R-squared of the SLR fit.
14. Write a __str__ method that returns a string that gives the following format when printed (Note the number of decimal digits!).
>>> filename = 'bmi_vs_sbp.txt'
>>> x, y = read_data(filename)
>>> dat = Data(x,y)
>>> lm = SLR(dat)
>>> print(lm)
Least squares fit on 10 data points.
Intercept = 45.584331
Slope = 3.465523
Residual standard deviation = 13.051139
R-squared = 0.731364
Attachment:- Assignment Files.rar