Month Machine Defects Human Defects Company loses 1 1539 31 99798 2 1284 29 87804 3 1490 27 93681 4 1355 22 82262 5 1500 35 106968 6 1777 30 107925 7 1716 41 117287 8 1045 29 76868 9 1364 47 106001 10 1516 21 88738 11 1623 37 105830 12 1376 37 88730 13 1327 49 100624 14 1178 50 98857 15 1491 37 102622 16 1667 41 108059 17 1769 34 110054 18 1104 44 91892 19 1196 46 98693 20 1794 29 110530 21 1379 38 96883 22 1448 32 99593 23 1505 32 94564 24 1420 42 105752 25 1475 27 93224 26 1118 34 75398 27 1433 58 113137 28 1589 26 85609 29 1585 32 98498 30 1493 33 101803 31 1124 36 88371 32 1536 28 102419 33 1678 41 117183 34 1723 35 107828 35 1413 30 88032 36 1390 54 117943 1. Formulate a regression equation to show a linear relationship between the variables. (Remember your regression equation should take the form Y = fn (X, ..., etc). Thus, Y = a + b1X1+... + e) without using an excel
Correlation
Correlation defines a relationship between two independent variables. It tells the degree to which variables move in relation to each other. When two sets of data are related to each other, there is a correlation between them.
Linear Correlation
A correlation is used to determine the relationships between numerical and categorical variables. In other words, it is an indicator of how things are connected to one another. The correlation analysis is the study of how variables are related.
Regression Analysis
Regression analysis is a statistical method in which it estimates the relationship between a dependent variable and one or more independent variable. In simple terms dependent variable is called as outcome variable and independent variable is called as predictors. Regression analysis is one of the methods to find the trends in data. The independent variable used in Regression analysis is named Predictor variable. It offers data of an associated dependent variable regarding a particular outcome.
| Month | Machine Defects | Human Defects | Company loses |
| 1 | 1539 | 31 | 99798 |
| 2 | 1284 | 29 | 87804 |
| 3 | 1490 | 27 | 93681 |
| 4 | 1355 | 22 | 82262 |
| 5 | 1500 | 35 | 106968 |
| 6 | 1777 | 30 | 107925 |
| 7 | 1716 | 41 | 117287 |
| 8 | 1045 | 29 | 76868 |
| 9 | 1364 | 47 | 106001 |
| 10 | 1516 | 21 | 88738 |
| 11 | 1623 | 37 | 105830 |
| 12 | 1376 | 37 | 88730 |
| 13 | 1327 | 49 | 100624 |
| 14 | 1178 | 50 | 98857 |
| 15 | 1491 | 37 | 102622 |
| 16 | 1667 | 41 | 108059 |
| 17 | 1769 | 34 | 110054 |
| 18 | 1104 | 44 | 91892 |
| 19 | 1196 | 46 | 98693 |
| 20 | 1794 | 29 | 110530 |
| 21 | 1379 | 38 | 96883 |
| 22 | 1448 | 32 | 99593 |
| 23 | 1505 | 32 | 94564 |
| 24 | 1420 | 42 | 105752 |
| 25 | 1475 | 27 | 93224 |
| 26 | 1118 | 34 | 75398 |
| 27 | 1433 | 58 | 113137 |
| 28 | 1589 | 26 | 85609 |
| 29 | 1585 | 32 | 98498 |
| 30 | 1493 | 33 | 101803 |
| 31 | 1124 | 36 | 88371 |
| 32 | 1536 | 28 | 102419 |
| 33 | 1678 | 41 | 117183 |
| 34 | 1723 | 35 | 107828 |
| 35 | 1413 | 30 | 88032 |
| 36 | 1390 | 54 | 117943 |
1. Formulate a regression equation to show a linear relationship between the variables. (Remember your regression equation should take the form Y = fn (X, ..., etc). Thus, Y = a + b1X1+... + e)
without using an excel
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