Assertion: correlation | /r/HasProperty | positive
Facets:
value: clearly
type: DEGREE
value: in the population
type: LOCATION
value: always
type: DEGREE
value: conversely
type: DEGREE
value: highly
type: DEGREE
value: since for more nations
type: OTHER
Source sentences:
text: Correlation is Positive when the values increase together, and
text: Whether a correlation is negative or positive doesn’t necessarily imply that one security is better or worse than another.
text: When two stocks, for example, move in the same direction, the correlation coefficient is positive.
text: Conversely, when two stocks move in opposite directions, the correlation coefficient is negative .
text: If the correlation coefficient is negative one, this means that the variables are perfectly negatively or inversely correlated.
text: A correlation can be positive or negative (direction).
text: Since oil companies earn greater profits as oil prices rise, the correlation between the two variables is highly positive.
text: Whether a correlation is negative or positive doesn’t necessarily imply that one security is better or worse than another.
text: When the correlation with sex is positive, females will have more of whatever is being measured on Y.
text: The correlation between scores on a neuroticism test and scores on an anxiety test is high and positive; therefore
text: From the scatterplot of the variables read and write below, we can see that the points tend along a line going from the bottom left to the upper right, which is the same as saying that the correlation is positive.
text: This means that we are trying to find out if the two variables have a correlation at all, how strong the correlation is and if the correlation is positive or negative.
text: We say that correlation is positive when correlation coefficient takes value from o to +1.
text: Correlations are said to be positive when the variables change in the same direction and negative when they move in opposite directions.
text: When the correlation is positive, the regression slope will be positive.
text: Correlations are said to be positive when the variables change in the same direction and negative when they move in opposite directions.
text: Using this method, one can ascertain the direction of correlation i.e. whether the correlation between two variables is negative or positive.
text: Nations are the cases, and the correlation is positive, meaning that a nation (case) with high GNP has high energy consumption; and one with low GNP has low energy consumption.
text: Although it is not easy to observe because of the many different magnitudes, the correlation is positive, since for more nations than not, high GNP per capita co-occurs with high trade, and low GNP per capita with low trade.
text: From the table, we can now clearly see that the correlation between the two variables is positive, since with only one exception (in the upper right cell) high magnitudes are observed together, as are low magnitudes.
text: Finally, we want a correlation that indicates whether the correlation is positive or negative.
text: First, the line would be horizontal, since if there is some variation in common between two variables, the line will angle upward to the right if this correlation is positive, or to the left if negative.
text: Correlations are said to be positive when the variables change in the same direction and negative when they move in opposite directions.
text: The direction in which the line slopes depends on whether the correlation is positive or negative.
text: Correlations are said to be positive when the variables change in the same direction and negative when they move in opposite directions.
text: The correlation between two variables can be positive (i.e., higher levels of one variable are associated with higher levels of the other) or negative (i.e., higher levels of one variable are associated with lower levels of the other).
text: Correlations are said to be positive when the variables change in the same direction and negative when they move in opposite directions.
text: From the scatterplot of the variables read and write below, we can see that the points tend along a line going from the bottom left to the upper right, which is the same as saying that the correlation is positive.
text: In the case of the Fahrenheit and Celsius temperature relationship, the correlation will always be positive because as one variable increases, so does the other.
text: A correlation could be positive, meaning both variables move in the same direction, or negative, meaning that when one variable’s value increases, the other variables’ values decrease.
text: We can see by looking at the graph whether there is a strong or weak correlation between two variables, and whether that correlation is positive or negative.
text: When will a correlation be positive?
text: Correlations are said to be positive when the variables change in the same direction and negative when they move in opposite directions.
text: What does it mean when a correlation is positive?
text: When the correlation is positive (r > 0), as the value of one variable increases, so does the other.
text: If the correlation is positive, when one variable increases, so does the other.
text: Why is the correlation positive when both increase together?
text: If most of the points are in the positive quadrants, the correlation will be positive.
text: The correlation between car weight and maintenance cost is positive.
text: The correlation between two variables can be positive (i.e., higher levels of one variable are associated with higher levels of the other) or negative (i.e., higher levels of one variable are associated with lower levels of the other).
text: (a) Would the correlation between the age of a second-hand car and its price be positive or negative?
text: Correlations are said to be positive when the variables change in the same direction and negative when they move in opposite directions.
text: This is one reason I don't like the use of the symbol $r$ in this case: the correlation between $X$ and $Y$ might be negative, while the correlation between $Y$ and $\hat Y$ is positive (in fact it will simply be the modulus of the correlation between $X$ and $Y$) yet both might be written with the symbol $
text: As long as the devices do indeed provide some measure of the characteristic being studied, but are less than perfect, this correlation will be positive, but less than one.
text: The opposite is the case when the correlation is negative.
text: This means that we are trying to find out if the two variables have a correlation at all, how strong the correlation is and if the correlation is positive or negative.
text: If the correlation is negative it takes values from -1 to 0.
text: When the correlation (r) is negative, the regression slope (b) will be negative.
text: Using this method, one can ascertain the direction of correlation i.e. whether the correlation between two variables is negative or positive.
text: If the correlation were negative, then most cases would be counted in the lower left and upper right cells.
text: Finally, we want a correlation that indicates whether the correlation is positive or negative.
text: That is, what is the chance of a plus .71 or higher being found for the sample when the correlation is zero or negative in the population?
text: The direction in which the line slopes depends on whether the correlation is positive or negative.
text: If the correlation is negative, we have a negative relationship; if it’s positive, the relationship is positive.
text: Negative values indicate that the correlation between the variables is negative: i.e. as one variable increases, the other variable decreases.
text: The correlation is negative if one variable decreases as the other grows.
text: We can see by looking at the graph whether there is a strong or weak correlation between two variables, and whether that correlation is positive or negative.
text: When will a correlation be negative?
text: If a correlation is negative, when one variable increases, the other variable descreases.
text: If the correlation is negative, when one variable increases, the other decreases.
text: The correlation between car weight and reliability is negative.
text: (a) Would the correlation between the age of a second-hand car and its price be positive or negative?
text: This is one reason I don't like the use of the symbol $r$ in this case: the correlation between $X$ and $Y$ might be negative, while the correlation between $Y$ and $\hat Y$ is positive (in fact it will simply be the modulus of the correlation between $X$ and $Y$) yet both might be written with the symbol $
text: Thus the correlation coefficient is positive if X i and Y i tend to be simultaneously greater than, or simultaneously less than
text: The Correlation Coefficient is positive when both securities move in the same direction (up or down) and negative when the two securities move in opposite directions.
text: Over a given time period, the two securities move together when the Correlation Coefficient is positive.
text: Similarly, if the value of one variable decreases, the value of the other variable decreases if the correlation coefficient is positive.
text: If the variables tend to go up and down together, the correlation coefficient will be positive.
text: The correlation coefficient is positive and height and weight tend to go up and down together.
text: In our example we saw that the straight line is positive, so the correlation coefficient is also positive.
text: Conversely, the two securities move in opposite directions when the Correlation Coefficient is negative.
text: How many correlation coefficients illustrated in the graphs below would be clearly negative?
text: Q15.18How many correlation coefficients illustrated in the graphs below would be clearly negative?
text: If the correlation coefficient is negative, when the value of one variable increases, the value of the other variable decreases and vice versa.
text: If the variables tend to go up and down in opposition with low values of one variable associated with high values of the other, the correlation coefficient will be negative.
text: If most of the points are in the negative quadrants, the correlation coefficient will be negative.