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SECTION B (Subjective Type Questions)
A. Short answer type questions.
1. What is Linear regression? Give two applications of regression in machine learning.
Ans. Linear Regression is a supervised learning algorithm. It makes use of one independent variable, X, to predict the
outcome of a second dependent variable Y. In machine learning, regression is used to predict outputs and forecast
trends.
2. Define correlation.
Ans. It measures the strength or degree of relationship between two variables. The relationship may be causal. The
degree of association is measured by a correlation coefficient, represented by r. It is also called Pearson's correlation
coefficient.
3. Differentiate between correlation and regression.
Ans.
Correlation Regression
It determines the strength or degree of relationship It determines how one variable affects another
between two variables. variable.
It is represented by a single value. It is represented by a regression line.
4. List the two advantages and disadvantages of linear regression.
Ans. Advantages:
i) Linear regression is a simple technique and easy to implement.
ii) Efficient to train the machine on this model.
Disadvantages:
i) Regression analysis is sensitive to outliers as these can have a great impact on the analysis.
ii) It is quite prone to overfitting. (Overfitting means that the training of the model on data is just too good and
the test sample size is quite small).
5. Give the values for small and medium—positive and negative strength of association according to Pearson’s
correlation coefficient.
Ans. Small-Positive: 0.1 to 0.3 and Negative –0.1 to –0.3
Medium-Positive: 0.3 to 0.5 and Negative –0.3 to –0.5
B. Long answer type questions.
1. The values of x and their corresponding values of y are shown in the table below.
x 0 1 2 3 4
y 2 4 5 6 8
a. Find the least square regression line y = ax + b.
b. Estimate the value of y when x = 10.
Ans. a. x y x 2 xy
0 2 0 0
1 4 1 4
2 5 4 10
3 6 9 18
4 8 16 32
2
Σx=10 Σy=25 Σx =30 Σxy=64
Regression 289

