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If the coefficient of determination for a data set containing 12 points is 0.5 6 of the data points must lie on the regression line for the data set.?
Wiki User
∙ 6y ago
That is not true. It is possible for a data set to have a coefficient of determination to be 0.5 and none of the points to lies on the regression line.
Wiki User
∙ 6y ago
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What is an example of two variables that would be positively correlated and two variables that could be negatively correlated?
False. Correlation coefficient as denoted by r, ranges from -1 to 1. Coefficient of determination, or r squared ranges from 0 to 1. I note that x,y data points that have a high negative correlation would plot with a negative trend or a negatively sloped line if a best fit regression line is determined. I note also that x,y data points with a high positive correlation would plot with a positive trend or positively sloped line if a best fit regression line is determined. The coefficient of determination for r = 0.9 and r= -0.9 would be 0.81.
Do you need more than 10 observations in linear regression?
This is a difficult question to answer. The pure answer is no. In reality, it depends on the level of randomness in the data. If you plot the data, it will give you an idea of the randomness. Even with 10 data points, 1 or 2 outliers can significantly change the regression equation. I am not aware of a rule of thumb on the minimum number of data points. Obviously, the more the better. Also, calculate the correlation coefficient. Be sure to follow the rules of regression. See the following website: http:/www.duke.edu/~rnau/testing.htm
Is the least-squares regression line resistant?
No, it is not resistant.It can be pulled toward influential points.
Why are there two regression lines?
There are two regression lines if there are two variables - one line for the regression of the first variable on the second and another line for the regression of the second variable on the first. If there are n variables you can have n*(n-1) regression lines. With the least squares method, the first of two line focuses on the vertical distance between the points and the regression line whereas the second focuses on the horizontal distances.
What is Definition of linear regression and correlation in statistics?
Whenever you are given a series of data points, you make a linear regression by estimating a line that comes as close to running through the points as possible. To maximize the accuracy of this line, it is constructed as a Least Square Regression Line (LSRL for short). The regression is the difference between the actual y value of a data point and the y value predicted by your line, and the LSRL minimizes the sum of all the squares of your regression on the line. A Correlation is a number between -1 and 1 that indicates how well a straight line represents a series of points. A value greater than one means it shows a positive slope; a value less than one, a negative slope. The farther away the correlation is from 0, the less accurately a straight line describes the data.
When all of the points fall on the regression line what is the value of the correlation coefficient?
1 or -1
What is an example of two variables that would be positively correlated and two variables that could be negatively correlated?
False. Correlation coefficient as denoted by r, ranges from -1 to 1. Coefficient of determination, or r squared ranges from 0 to 1. I note that x,y data points that have a high negative correlation would plot with a negative trend or a negatively sloped line if a best fit regression line is determined. I note also that x,y data points with a high positive correlation would plot with a positive trend or positively sloped line if a best fit regression line is determined. The coefficient of determination for r = 0.9 and r= -0.9 would be 0.81.
Do you need more than 10 observations in linear regression?
This is a difficult question to answer. The pure answer is no. In reality, it depends on the level of randomness in the data. If you plot the data, it will give you an idea of the randomness. Even with 10 data points, 1 or 2 outliers can significantly change the regression equation. I am not aware of a rule of thumb on the minimum number of data points. Obviously, the more the better. Also, calculate the correlation coefficient. Be sure to follow the rules of regression. See the following website: http:/www.duke.edu/~rnau/testing.htm
Is the least-squares regression line resistant?
No, it is not resistant.It can be pulled toward influential points.
Why are there two regression lines?
There are two regression lines if there are two variables - one line for the regression of the first variable on the second and another line for the regression of the second variable on the first. If there are n variables you can have n*(n-1) regression lines. With the least squares method, the first of two line focuses on the vertical distance between the points and the regression line whereas the second focuses on the horizontal distances.
What are the value of the tiles in scrabble?
There are 9 tiles containing the letter A and a value of 1 point. There are 2 tiles containing the letter B and a value of 3 points. There are 2 tiles containing the letter C and a value of 3 points. There are 4 tiles containing the letter D and a value of 2 points. There are 12 tiles containing the letter E and a value of 1 point. There are 2 tiles containing the letter F and a value of 4 points. There are 3 tiles containing the letter G and a value of 2 points. There are 2 tiles containing the letter H and a value of 4 points. There are 9 tiles containing the letter I and a value of 1 point. There is 1 tile containing the letter J and a value of 8 points. There is 1 tile containing the letter K and a value of 5 points. There are 4 tiles containing the letter L and a value of 1 point. There are 2 tiles containing the letter M and a value of 3 points. There are 6 tiles containing the letter N and a value of 1 point. There are 8 tiles containing the letter O and a value of 1 point. There are 2 tiles containing the letter P and a value of 3 points. There is 1 tile containing the letter Q and a value of 10 points. There is 6 tiles containing the letter R and a value of 1 point. There are 4 tiles containing the letter S and a value of 1 point. There are 6 tiles containing the letter T and a value of 1 point. There are 4 tiles containing the letter U and a value of 1 point. There are 2 tiles containing the letter V and a value of 4 points. There are 2 tiles containing the letter W and a value of 4 points. There is 1 tile containing the letter X and a value of 8 points. There are 2 tiles containing the letter Y and a value of 4 points. There is 1 tile containing the letter Z and a value of 10 points. There are 2 blank tiles with no face point value.
When using the Regression tools you cannot plot the points of your data and your model on the same graph?
False
What does a high t statistic mean?
Assuming you mean the t-statistic from least squares regression, the t-statistic is the regression coefficient (of a given independent variable) divided by its standard error. The standard error is essentially one estimated standard deviation of the data set for the relevant variable. To have a very large t-statistic implies that the coefficient was able to be estimated with a fair amount of accuracy. If the t-stat is more than 2 (the coefficient is at least twice as large as the standard error), you would generally conclude that the variable in question has a significant impact on the dependent variable. High t-statistics (over 2) mean the variable is significant. What if it's REALLY high? Then something is wrong. The data points might be serially correlated. Assuming you mean the t-statistic from least squares regression, the t-statistic is the regression coefficient (of a given independent variable) divided by its standard error. The standard error is essentially one estimated standard deviation of the data set for the relevant variable. To have a very large t-statistic implies that the coefficient was able to be estimated with a fair amount of accuracy. If the t-stat is more than 2 (the coefficient is at least twice as large as the standard error), you would generally conclude that the variable in question has a significant impact on the dependent variable. High t-statistics (over 2) mean the variable is significant. What if it's REALLY high? Then something is wrong. The data points might be serially correlated.
When you removed from the data set how would the correlation coefficient be affected?
If you remove certain data points from a dataset, the correlation coefficient may be affected depending on the nature of the relationship between the removed data points and the remaining data points. If the removed data points have a strong relationship with the remaining data, the correlation coefficient may change significantly. However, if the removed data points have a weak or no relationship with the remaining data, the impact on the correlation coefficient may be minimal.
What are the Scrabble values of the letters C K W and X?
The tiles containing the letter C have a value of 3 points. The tile containing the letter K has a value of 5 points. The tiles containing the letter W have a value of 4 points. The tile containing the letter X has a value of 8 points.
Find the equation of the line containing the two points shown?
what is the slope of the line containing points (5-,-2) and (-5,3)? 2
Guiding principles of fourteen points?
Self Determination
