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2.   A regression line that is calculated by least squares method will have an intercept and slope that
                          minimize the sum of the squared differences between the Y variable and the regression line.
                     3.   A scatterplot is actually a graph of a curvilinear function.
                     4.   The Y intercept of a regression line is equal to the value of X when Y is equal to zero.

                     5.   If a regression line that was calculated by least squares method is plotted on a scatterplot,
                          all the points in the dataset should be on the line.


                                                  SECTION B (Subjective Type Questions)
                 A.   Short answer type questions.
                     1.   Interpret the values of correlation coefficient when r=1, –1 and 0.
                     2.   Does correlation indicate causation? Why/Why not?
                     3.   Give any two real life applications of linear regression.
                     4.   Define the term residual error. Why is it important?
                     5.   Identify the type of correlation:















                 B.   Long answer type questions.
                     1.   The sales of a company (in lakhs rupees) for each year are shown below:

                                                x (year)   2010   2011   2012   2013   2014
                                                y (year)      12    19     29     37     45

                          a.   Find the least square regression line y = mx + b.
                          b.   Use the least squares regression line as a model to estimate the sales of the company in 2015.
                     2.   Calculate the correlation coefficient between X and Y. What can you infer from the value of the coefficient?

                                                       x     1    3    4     5    7
                                                       y     2    6   10    12   16

                     3.   Define the following:
                          a.   Line of best fit
                          b.   Independent variable
                          c.   Dependent variable

                          d.   Outlier
                          e.   Residual Error
                     4.   What are dependent and independent variables in Regression? Explain with an example.
                     5.   Anita withdraws money from an ATM from time to time. She feels that her rate of spending money is affected by
                          the amount withdrawn from the ATM. To understand this relationship, she deliberately varies the amount withdrawn
                          from time to time. She records the amount withdrawn (x) and no. of days (y) till her next visit.


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