Interpreting linear relationships practice
1. Positive linear relationship: This means that as one variable increases, the other variable also increases. For example, as the amount of study time increases, test scores also increase. The relationship between these two variables is positive linear.
2. Negative linear relationship: This means that as one variable increases, the other variable decreases. For example, as the temperature outside increases, the sales of winter jackets decrease. The relationship between these two variables is negative linear.
3. No linear relationship: This means that there is no clear pattern or trend between two variables. For example, the relationship between the number of hours spent watching TV and the amount of exercise done per week may not follow a linear pattern. This could be due to other factors influencing the relationship between these two variables.
4. Strong linear relationship: This means that the data points are tightly clustered around a straight line, indicating a strong correlation between the two variables. For example, if there is a strong linear relationship between the amount of money spent on advertising and the sales of a product, this suggests that advertising has a significant impact on sales.
5. Weak linear relationship: This means that the data points are scattered and don't form a clear straight line, indicating a weak correlation between the two variables. For example, if there is a weak linear relationship between the number of social media followers and product sales, this suggests that the number of followers doesn't have a strong impact on sales.