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How do you use a table of data to check whether the relationship between two variables is a direct variation?
Wiki User
∙ 13y ago
if it passes through (0,0) then it is a direct variation
Wiki User
∙ 13y ago
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To visually determine whether there is a relationship between two variables?
Scatter chart
How can a scatter plot be used?
A scatter plot can be used to see if there is any relationship between two variables. It can also give a general idea of the nature of that relationship (linear, quadratic, logarithmic, inverse square, etc; whether or not the relationship remains constant over the domain, whether or not the variation remains constant (homoscedasticity), and so on.
What kinda chart is used to visually determine whether there is a relationship between two variables?
scatter chart
What is the Advantages of scatter diagram?
It allows a quick look at the data to establish whether or not there is any relationship between the variables and also an indication of the nature of the relationship: linear, quadratic, power etc.
How do you explain the analysis of variance assuming that your audience has not had a statistic class before?
The measurement of any statistical variable will vary from one observation to another. Some of this variation is systematic - due to variations in some other variable that "explains" these variations. There may be several such explanatory variables - acting in isolation or in conjunction with one another. Finally, there will be a residual variation which cannot be explained by any of these "explanatory" variables. The statistical technique called analysis of variance first calculates the total variation in the observations. The next step is to calculate what proportion of that variation can be "explained" by other variables, and finding the residual variation. A comparison of the explained variation with the residual variation is an indicator of whether or not the amount explained is statistically significant. The word "explain" is in quotes because there is not always a causal relationship. The causality may go in the opposite direction. Or the variables may be related to another variable that is not part of the analysis.
Is helpful in determining whether there is a relationship between two variables?
it depends on who you are
To visually determine whether there is a relationship between two variables?
Scatter chart
How can a scatter plot be used?
A scatter plot can be used to see if there is any relationship between two variables. It can also give a general idea of the nature of that relationship (linear, quadratic, logarithmic, inverse square, etc; whether or not the relationship remains constant over the domain, whether or not the variation remains constant (homoscedasticity), and so on.
How do you tell whether the relationship between two variables is linear?
By definition, if you graph the relationship between two variables and the result is a straight line (of whatever slope) that is a linear relationship. If it is a curve, rather than a straight line, then it is not linear.
What kinda chart is used to visually determine whether there is a relationship between two variables?
scatter chart
Can a graph help you interpret data?
Yes. It can give insight as to whether there is a relationship between two variables, and if so, whether the relationship is direct or indirect; whether it is linear, polynomial, exponential, logarithmic; whether or not there are asysmptotic values; whether or not there is clustering; etc.
What is uncorrelated variables means?
Correlation is a way of testing whether there is a linear relationship between two variables of their transformed versions. If there is no correlation then all that it means is that there is no linear relationship between the two or between their transformed versions. It certainly does not mean that there is no relationship. For example, if y = a*x^2 + b where a and b are constants then, because of symmetry, the correlation between x and y is 0.
What is the Advantages of scatter diagram?
It allows a quick look at the data to establish whether or not there is any relationship between the variables and also an indication of the nature of the relationship: linear, quadratic, power etc.
How do you explain the analysis of variance assuming that your audience has not had a statistic class before?
The measurement of any statistical variable will vary from one observation to another. Some of this variation is systematic - due to variations in some other variable that "explains" these variations. There may be several such explanatory variables - acting in isolation or in conjunction with one another. Finally, there will be a residual variation which cannot be explained by any of these "explanatory" variables. The statistical technique called analysis of variance first calculates the total variation in the observations. The next step is to calculate what proportion of that variation can be "explained" by other variables, and finding the residual variation. A comparison of the explained variation with the residual variation is an indicator of whether or not the amount explained is statistically significant. The word "explain" is in quotes because there is not always a causal relationship. The causality may go in the opposite direction. Or the variables may be related to another variable that is not part of the analysis.
Can a chi square ever tell us whether one variable causes variations in the second variable?
Regrettably, no. The most a chi-square statistic can do is to participate in the measurement of the level of association of the variation between two variables.
What is the difference between correlation analysis and?
Correlation analysis is a type of statistical analysis used to measure the strength of the relationship between two variables. It is used to determine whether there is a cause-and-effect relationship between two variables or if one of the variables is simply related to the other. It is usually expressed as a correlation coefficient a number between -1 and 1. A positive correlation coefficient means that the variables move in the same direction while a negative correlation coefficient means they move in opposite directions.Regression analysis is a type of statistical analysis used to predict the value of one variable based on the value of another. This type of analysis is used to determine the relationship between two or more variables and to determine the direction strength and form of the relationship. Regression analysis is useful for predicting future values of the dependent variable given a set of independent variables.Correlation Analysis is used to measure the strength of the relationship between two variables.Regression Analysis is used to predict the value of one variable based on the value of another.
Fill in the syllogism. If ab and bc then a?
The answer will depend on whether or not the relationship between the pairs of variables is transitive. In mathematics, not all relationships are transitive.For example, if the relationship is "is coprime with", then2 is coprime with 3, 3 is coprime with 4 but 2 is certainly not coprime with 4.
