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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




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