# What should be used to predict Y when R is not significant?

## What should be used to predict Y when R is not significant?

If r is significant and the scatter plot shows a linear trend, the line can be used to predict the value of y for values of x that are within the domain of observed x values. If r is not significant OR if the scatter plot does not show a linear trend, the line should not be used for prediction.

## What if correlation is not significant?

If the P-value is bigger than the significance level (α =0.05), we fail to reject the null hypothesis. We conclude that the correlation is not statically significant. Or in other words “we conclude that there is not a significant linear correlation between x and y in the population”

How do you know if a correlation coefficient is significant?

Compare r to the appropriate critical value in the table. If r is not between the positive and negative critical values, then the correlation coefficient is significant. If r is significant, then you may want to use the line for prediction. Suppose you computed r=0.801 using n=10 data points.

Which of the regression coefficients are significantly different from zero quizlet?

The population correlation coefficient IS significantly DIFFERENT FROM zero.

### Which of the following indicates the strongest relationship r?

Answer: -0.85 (Option d) is the strongest correlation coefficient which represents the strongest correlation as compared to others.

### How do you know if r is significant?

If r< negative critical value or r> positive critical value, then r is significant. Since r = 0.801 and 0.801 > 0.632, r is significant and the line may be used for prediction.

How do you report a correlation that is not significant?

If your correlation was non significant, but p < . 10 you can still talk about it. You might put the following text in your paper: While the correlation was not significant relative to the standard alpha level of . 05, the p-value was less than .

Can a correlation be strong but not significant?

A statistically significant correlation does not necessarily mean that the strength of the correlation is strong. Even though, it has the same and very high statistical significance level, it is a weak one. The low level of the p-value reassures us that 99.99% of the time the correlation is weak at an r of 0.31.

## When testing the significance of the correlation coefficient What is the null hypothesis?

Null Hypothesis H0: The population correlation coefficient IS NOT significantly different from zero. There IS NOT a significant linear relationship(correlation) between x and y in the population.

## What value of correlation coefficient represents the strongest relationship?

-1 or 1
The strongest linear relationship is indicated by a correlation coefficient of -1 or 1. The weakest linear relationship is indicated by a correlation coefficient equal to 0. A positive correlation means that if one variable gets bigger, the other variable tends to get bigger.

When multiple predictors in a regression equation are also correlated with each other this is called?

Multicollinearity: Statistical phenomenon in which two or more predictor variables in a multiple regression model are highly correlated, meaning that one can be linearly predicted from the others with a non-trivial degree of accuracy.

What can we conclude if the global test of regression does not reject the null hypothesis?

What can we conclude if the global test of regression rejects the null hypothesis? At least one of the net regression coefficients is not equal to zero.

### Is there a significant relationship between X and Y?

Conclusion: There is sufficient evidence to conclude that there is a significant linear relationship between x and y because the correlation coefficient is significantly different from zero. What the conclusion means: There is a significant linear relationship between x and y.

### Is the correlation coefficient between X and Y significant?

There IS NOT a significant linear relationship (correlation) between x and y in the population. Alternate Hypothesis Ha: The population correlation coefficient IS significantly DIFFERENT FROM zero.

When to reject the null hypothesis of the correlation coefficient?

If the p -value is less than the significance level ( α = 0.05): Decision: Reject the null hypothesis. Conclusion: “There is sufficient evidence to conclude that there is a significant linear relationship between x and y because the correlation coefficient is significantly different from zero.”

Can a regression line predict a linear trend?

Therefore, we CANNOT use the regression line to model a linear relationship between x and y in the population. If r is significant and the scatter plot shows a linear trend, the line can be used to predict the value of y for values of x that are within the domain of observed x values.

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