Plot the appropriate Graph. Here, objective is to show the linear relationship between the sales of the product and amount spent in advertisement of a certain product. Based on the graphical output write your comment on linear association between these two variables. 1 260.3 5 2 286.1 7 3 279.4 6 4 410.8 9 5 438.2 12 6 315.3 8 7 565.1 11 8 570 16 9 426.1 13 10 315 7 11 403.6 10 12 220.5 4 13 343.6 9 14 644.6 17 15 520.4 19 16 329.5 9 17 426 11 18 343.2 8 19 450.4 13 20 421.8 14 21 245.6 7 22 503.3 16 23 375.7 9 24 265.5 5 25 620.6 18 26 450.5 18 27 270.1 5 28 368 7 29 556.1 12 30 570 13 31 318.5 8 32 260.2 6 33 667 16 34 618.3 19 35 525.3 17 36 332.2 10 37 393.2 12 38 283.5 8 39 376.2 10 40 481.8 12
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.
- Plot the appropriate Graph. Here, objective is to show the linear relationship between the sales of the product and amount spent in advertisement of a certain product.
- Based on the graphical output write your comment on linear association between these two variables.
| 1 | 260.3 | 5 |
| 2 | 286.1 | 7 |
| 3 | 279.4 | 6 |
| 4 | 410.8 | 9 |
| 5 | 438.2 | 12 |
| 6 | 315.3 | 8 |
| 7 | 565.1 | 11 |
| 8 | 570 | 16 |
| 9 | 426.1 | 13 |
| 10 | 315 | 7 |
| 11 | 403.6 | 10 |
| 12 | 220.5 | 4 |
| 13 | 343.6 | 9 |
| 14 | 644.6 | 17 |
| 15 | 520.4 | 19 |
| 16 | 329.5 | 9 |
| 17 | 426 | 11 |
| 18 | 343.2 | 8 |
| 19 | 450.4 | 13 |
| 20 | 421.8 | 14 |
| 21 | 245.6 | 7 |
| 22 | 503.3 | 16 |
| 23 | 375.7 | 9 |
| 24 | 265.5 | 5 |
| 25 | 620.6 | 18 |
| 26 | 450.5 | 18 |
| 27 | 270.1 | 5 |
| 28 | 368 | 7 |
| 29 | 556.1 | 12 |
| 30 | 570 | 13 |
| 31 | 318.5 | 8 |
| 32 | 260.2 | 6 |
| 33 | 667 | 16 |
| 34 | 618.3 | 19 |
| 35 | 525.3 | 17 |
| 36 | 332.2 | 10 |
| 37 | 393.2 | 12 |
| 38 | 283.5 | 8 |
| 39 | 376.2 | 10 |
| 40 | 481.8 | 12 |
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