1.you use in line to best fit the set of data to make a prediction about the unknown value. The correlation coefficient for your data set is -0.015. How confident can you be that your predicted value will be reasonably close to the actual value?
low coefficient means the data do not cluster close to the line. So, not much confidence.
wwas this right
To determine how confident you can be in the predicted value, you need to consider the strength and direction of the correlation coefficient.
A correlation coefficient measures the strength and direction of the relationship between two variables. The value can range from -1 to +1.
In this case, the correlation coefficient is -0.015. Since it is very close to 0, it indicates a weak and almost non-existent correlation between the variables.
With such a weak correlation, you cannot be very confident that the predicted value will be reasonably close to the actual value. The lack of a strong relationship between the variables suggests that the predicted value may not provide an accurate estimation of the unknown value.
To measure how confident you can be in your predicted value using linear regression, you need to consider the correlation coefficient. The correlation coefficient, often denoted as r, quantifies the strength and direction of the linear relationship between two variables. In this case, you have a correlation coefficient of -0.015.
The correlation coefficient ranges from -1 to 1. A value of -1 indicates a perfect negative linear relationship, 1 indicates a perfect positive linear relationship, and 0 indicates no linear relationship. In your case, the correlation coefficient is close to 0, indicating a weak or negligible linear relationship between the variables.
Since the correlation coefficient is so close to zero, it suggests that there is no significant linear relationship between the two variables. Therefore, you should not have much confidence in the predicted value being reasonably close to the actual value.
When the correlation coefficient is close to zero, it means that the data points are scattered randomly without showing any clear trend or pattern. In such cases, using linear regression to make predictions may not be appropriate, as the model assumes a linear relationship between the variables.
In conclusion, based on a correlation coefficient of -0.015, you cannot be confident that your predicted value will be reasonably close to the actual value. It is important to interpret the correlation coefficient to understand the strength of the relationship between variables and its implications for making predictions.