Page 287 - Artificial Intellegence_v2.0_Class_11
P. 287

2.   There must be a linear relationship between the two variables. Create a scatterplot by plotting the two variables
                    against each other. The scatterplot can then be used to check for linearity. The scatterplot may look something like
                    one of the following:


                               Positive Correlation       Negative Correlation      No Correlation














                 3.   In statistics, outliers are data points that are significantly different from other observations. Outliers may be due to
                    measurement irregularity or may indicate experimental error; the latter are sometimes excluded from the data set.
                    Outliers can cause serious problems in statistical analysis. The data should not have any significant outliers. Outliers
                    are single data points within your dataset that do not follow the usual pattern. The following scatterplots highlight
                    the potential impact of outliers:




                                            r = 0.39                      r = 0.69






                                                    Outlier                     Outlier removed




                 Outliers can have a great impact on the line of best fit and the Pearson correlation coefficient, leading to very difficult
                 inferences regarding the data. Therefore, it is best to have no outliers or keep them to a minimum.
                 4.  The variables should be normally distributed (approximately).


                        Importance of data in Regression Analysis

                 Data plays a pivotal role in developing any AI model. Care must be taken to use only authentic data for creating an AI
                 model. Regression analysis is used by businesses to make data-driven decisions since it identifies the variables that have
                 the most influence on the outcome based on past performance. When forecasting and generating predictions based on
                 data, businesses can more effectively concentrate on the right things.


                        How Prediction Changes with Changing Data?

                 Properly modelling changes over time is necessary for forecasting and essential for any model or process with data that
                 cover multiple time periods. Prediction can be made about future events using historical data and analytics methods
                 like statistical modelling and machine learning. This is called predictive analytics. Future insights can be produced with
                 an impressive level of precision using the science of predictive analytics. Any organization may now use historical and
                 current data to accurately predict patterns and behaviours milliseconds, days, or years into the future with the aid of
                 advanced predictive analytics tools and models.


                                                                                                 Regression     285
   282   283   284   285   286   287   288   289   290   291   292