Using Big Data Technology In Predicting Product Demand Using Multiple Linear Regression 1) Average r e s po nd e n t answer Valid STRONGLY DISAGREE DISAGREE 1 1.1 1.1 1.1 3 3.3 3.3 4.4 6 6.7 6.7 11.1 AGREE 44 48.9 48.9 60.0 ABSOLUTELY AGREE 36 40.0 40.0 100.0 Total 90 100.0 100.0 DISAGREE • From this table, a discussion can be made, for example, on Brand Image 1, there are 44 respondents who are happy to use products from PT.XYZ, OR 48.9%. BD Frequency Valid Percent Valid Percent Cumulative Percent STRONGLY DISAGREE DISAGREE 1 1.1 1.1 1.1 3 3.3 3.3 4.4 DISAGREE 14 15.6 15.6 20.0 AGREE 46 51.1 51.1 71.1 ABSOLUTELY AGREE 26 28.9 28.9 100.0 Total 90 100.0 100.0 • From this table, a discussion can be made, for example, on Brand Image 2, there are 46 respondents who are happy to use products from PT.XYZ, OR 51.1%. BD Frequency Valid Percent Valid Percent Cumulative Percent STRONGLY DISAGREE DISAGREE 2 2.2 2.2 2.2 1 1.1 1.1 3.3 DISAGREE 16 17.8 17.8 21.1 AGREE 48 53.3 53.3 74.4 ABSOLUTELY AGREE 23 25.6 25.6 100.0 Total 90 100.0 100.0 • From this table, a discussion can be made, for example, on Brand Image 3, there are 48 respondents who are happy to use products from PT.XYZ, OR 53.3%. BD Frequency Valid Percent Valid Percent Cumulative Percent STRONGLY DISAGREE DISAGREE 2 2.2 2.2 2.2 19 21.1 21.1 23.3 AGREE 43 47.8 47.8 71.1 ABSOLUTELY AGREE 26 28.9 28.9 100.0 Total 90 100.0 100.0 • From this table, a discussion can be made, for example, on Brand Image 4, there are 43 respondents who are happy to use products from PT.XYZ, OR 47.8%.