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Does a strong correlation indicate a cause-and-effect relationship between variables?
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If doubling the manipulating variable results in a doubling of the responding variable the relationship between the variables is a?
This would indicate that there is a linear relationship between manipulating and responding variables.
When are is close to -1 does it indicate a weak negative correlation?
No, it indicates an extremely strong positive correlation.
What does dispersion indicate about the data?
correlation which can be strong or weak
What is the difference between corelation and regression?
I've included links to both these terms. Definitions from these links are given below. Correlation and regression are frequently misunderstood terms. Correlation suggests or indicates that a linear relationship may exist between two random variables, but does not indicate whether X causes Yor Y causes X. In regression, we make the assumption that X as the independent variable can be related to Y, the dependent variable and that an equation of this relationship is useful. Definitions from Wikipedia: In probability theory and statistics, correlation (often measured as a correlation coefficient) indicates the strength and direction of a linear relationship between two random variables. In statistics, regression analysis refers to techniques for the modeling and analysis of numerical data consisting of values of a dependent variable (also called a response variable) and of one or more independent variables (also known as explanatory variables or predictors). The dependent variable in the regression equation is modeled as a function of the independent variables, corresponding parameters ("constants"), and an error term. The error term is treated as a random variable. It represents unexplained variation in the dependent variable. The parameters are estimated so as to give a "best fit" of the data. Most commonly the best fit is evaluated by using the least squares method, but other criteria have also been used.
When are is close to -1 does it indicate a strong negative correlation?
Yes.
What does the size of a correlation coefficient indicate?
Size of variables
Does a negative correlation coefficient indicate an inverse relationship?
Yes, but the relationship need not be causal.
What is the meaning of correlation coefficient?
The correlation coefficient is a statistical measure that quantifies the strength and direction of a linear relationship between two variables. It ranges from -1 to 1, with -1 indicating a perfect negative correlation, 0 indicating no correlation, and 1 indicating a perfect positive correlation.
If doubling the manipulating variable results in a doubling of the responding variable the relationship between the variables is a?
This would indicate that there is a linear relationship between manipulating and responding variables.
What does a correlation coefficient represent?
The correlation coefficient for two variables is a measure of the degree to which the variables change together. The correlation coefficient ranges between -1 and +1. At +1, the two variables are in perfect agreement in the sense that any increase in one is matched by an increase in the other. An increase of twice as much in the first is accompanied by double the increase in the second. A correlation coefficient of -1 indicates that the two variables are in perfect opposition. The changes in the two variables are similar to when the correlation coefficient is +1, but this time an increase in one variable is accompanied by a decrease in the other. A correlation coefficient near 0 indicates that the two variables do not move in harmony. An increase in one is as likely to be accompanied by an increase in the other variable as a decrease. It is very very important to remember that a correlation coefficient does not indicate causality.
How do you read a scattergram?
To read a scattergram, observe how the data points are dispersed on the graph. Look for any patterns or trends, such as a positive or negative correlation. Assess how closely the points cluster around a line or show a particular shape, which can indicate the strength and direction of the relationship between the variables.
What do positive values of covariance indicate?
as the covariance of the two random variables (X and Y) is used for calculating the correlation coeffitient of those variables it indicates that the relation between those (X and Y) is positive, so they are positively correlated.
What are organizational variables?
indicate organizational variables
When are is close to -1 does it indicate a weak negative correlation?
No, it indicates an extremely strong positive correlation.
How can I perform a correlation analysis in SPSS?
To perform a correlation analysis in SPSS, you can follow these steps: Open SPSS and load your dataset by selecting "File" and then "Open" or by using the "Open" button on the toolbar. Once your dataset is loaded, go to the "Analyze" menu at the top of the SPSS window and select "Correlate." In the submenu that appears, choose "Bivariate." In the "Bivariate Correlations" dialog box, select the variables you want to include in the correlation analysis. You can either double-click on variables to move them to the "Variables" list or use the arrow buttons. You can select multiple variables by holding down the Ctrl key (or Command key on Mac) while clicking on the variables. By default, SPSS will calculate Pearson correlation coefficients. If you want to compute other types of correlation coefficients, such as Spearman's rank correlation or Kendall's tau-b, click on the "Options" button. In the "Bivariate Correlations: Options" dialog box, select the desired correlation coefficient under "Correlation Coefficients." You can also choose to calculate p-values and confidence intervals for the correlations by checking the corresponding options in the "Bivariate Correlations: Options" dialog box. After selecting the variables and options, click the "OK" button to run the correlation analysis. SPSS will generate the correlation matrix, which displays the correlation coefficients between all pairs of variables selected for analysis. The correlation matrix will appear in the output window. To interpret the correlation results, examine the correlation coefficients. Values range from -1 to 1, where -1 indicates a perfect negative correlation, 1 indicates a perfect positive correlation, and 0 indicates no correlation. Additionally, consider the statistical significance of the correlations. If p-values were calculated, values below a certain threshold (e.g., p < 0.05) indicate statistically significant correlations. You can save the output as a file by selecting "File" and then "Save" or by using the "Save" button on the toolbar. That's how you can perform a correlation analysis in SPSS. Remember to carefully select the variables and interpret the results appropriately based on your research question or analysis objective.
What does dispersion indicate about the data?
correlation which can be strong or weak
What is the difference between corelation and regression?
I've included links to both these terms. Definitions from these links are given below. Correlation and regression are frequently misunderstood terms. Correlation suggests or indicates that a linear relationship may exist between two random variables, but does not indicate whether X causes Yor Y causes X. In regression, we make the assumption that X as the independent variable can be related to Y, the dependent variable and that an equation of this relationship is useful. Definitions from Wikipedia: In probability theory and statistics, correlation (often measured as a correlation coefficient) indicates the strength and direction of a linear relationship between two random variables. In statistics, regression analysis refers to techniques for the modeling and analysis of numerical data consisting of values of a dependent variable (also called a response variable) and of one or more independent variables (also known as explanatory variables or predictors). The dependent variable in the regression equation is modeled as a function of the independent variables, corresponding parameters ("constants"), and an error term. The error term is treated as a random variable. It represents unexplained variation in the dependent variable. The parameters are estimated so as to give a "best fit" of the data. Most commonly the best fit is evaluated by using the least squares method, but other criteria have also been used.
