the correlation coefficient of a data set is -0.833 can I be confident that my predicted value will be close to the actual value? Why or why not?
bro can u like make that simple😠ion wanna read all that im j tryna cheat not learn sum
The correlation coefficient for the given data set is= -0.833
It means there is weak relation between two variables. Negative correlation means, if one quantity is increasing other is decreasing.
So, the value of -0.833 shows that there is very less correlation between x and y values or two values in data set.
Also , The correlation can range from -1 to 1, with 0 being no correlation at all. so in this case, -0.833 is close to 0, you would be not confident at all.
This means your prediction is no better than a random guess.
So, the chances are very less that predicted value will be reasonably close to the actual value as the points will be far from line of best fit.
"urmom" like fr
a number (positive or negative) close to 1 means it is a good approximation.
review correlation coefficients.
the correlation coefficient of a data set is -0.833 can I be confident that my predicted value will be close to the actual value? Why or why not?
thank you "Kraken (She/Her)" !!! this makes it so much more understandable :)))
To determine whether you can be confident that your predicted value will be close to the actual value based on the correlation coefficient, you need to understand the concept of correlation.
The correlation coefficient is a statistical measure that quantifies the strength and direction of the relationship between two variables. It ranges from -1 to +1. A value of -1 indicates a strong negative (inverse) relationship, +1 indicates a strong positive relationship, and 0 indicates no linear relationship.
In your case, a correlation coefficient of -0.833 indicates a strong negative relationship between the variables. The negative sign indicates an inverse relationship, meaning that as one variable increases, the other decreases.
So, can you be confident that your predicted value will be close to the actual value?
Based solely on the correlation coefficient, you can infer that there is a relatively strong relationship between the variables. However, the correlation coefficient only measures the strength and direction of the linear relationship. It does not imply the accuracy or precision of your predicted values.
To determine the closeness of your predicted value to the actual value, you would need to consider additional factors such as the range of the data, the sample size, the presence of outliers, and the assumptions of the predictive model being used.
It is also crucial to remember that correlation does not imply causation. Even though there may be a strong negative relationship, there could be other factors influencing the relationship between the variables.
To summarize, while a strong negative correlation suggests that there is a relationship between the variables, it is not sufficient by itself to guarantee the accuracy or closeness of your predicted values. Other factors and considerations, such as the specific predictive model being used and the quality of the data, also need to be taken into account.