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What is a weak negative correlation?
A negative correlation is when you compare 2 sets of data on a line graph (e.g. scores in a French test and scores in an English test), the higher one thing is, the lower the other is (e.g. someone might score 98% on the French test but only 12% on the English test (or visa versa)). A positive correlation is the other way around. A weak correlation is when there is a lot of deviation from the line of best fit (there will always be one with correlations as a line of best fit shows correlations after all) whereas with a strong correlation, there is little deviation.
An individual reported a correlation of 1.25 between form A and From B of an intelligence test From this coefficient what could one conclude?
Nothing
How do you determine if variances are similar?
If the two distributions can be assumed to follow Gaussian (Normal) distributions then Fisher's F-test is the most powerful test. If the data are at least ordinal, then you can use the Kolmogorov-Smirnov two-sample test.
When calculating average improvement of students do you take the average of all improvement percentages or do you average all pre-test scores all post-test scores and then calculate the difference?
I was given this formula in college: IND Posttest score - IND pretest score ______________________________ = Improvement Score Highest score for all - IND pretest score
How do we know if a correlation is significant or not?
There are several statistical measures of correlation: some require only a nominal scale, that is, data classified according to two criteria; others require an ordinal scale, which is the ability to determine whether one measurement is bigger or smaller than another; others require an interval scale, which allows you to determine the difference in values but not the ratio between them. [A good example of the latter is temperature measured in any scale other than Kelvin: the difference between 10 degrees C and 15 degrees C is 5 C degrees, but 15 C is not 1.5 times as warm as 10 C.]The contingency coefficient, which is suitable for nominal data, has a chi-squared distribution.The Spearman rank correlation, requiring ordinal data, has its own distribution for small data sets but as the number of units increases to n, the distribution approaches Student's t-distribution with n-2 degrees of freedom.The Kendall rank correlation coefficient can be used in identical situations and gives the same measure of significance. However, the Kendall coefficient can also be used to test partial correlation - whether the correlation between two variables is "genuine" or whether it arises because both variables are actually correlated to a third variable.The Pearson's product moment correlation coefficient (PMCC) is the most powerful but requires measurement on an interval scale as well as an underlying bivariate Normal distribution.The significance levels of these correlation measures are tabulated for testing.A simple "rule of thumb" for testing the significance of PMCC is that values below -0.7 or above 0.7 are highly significant. Values in the ranges (-0.7, -0.3) and (0.3, 0.7) are moderate, and values between -0.3 and +0.3 are not significant.
What are examples of nonparametric statistics?
Fisher's exact probability test, chi-square test for independence, Kolmogorov-Smirnov test, Spearman's Rank correlation and many, many more.
What is the notation or symbol used forFisher's exact test?
The symbol typically used to represent Fisher's exact test in statistical notation is "FET."
Fisher's exact test symbol used for?
This test is used to determine whether the means of the different variables are significantly different from each other.
How do you calculate chi-squared if one of the expected terms is less than 5. Is there software that can do this?
If the assumptions behind the chi-square test don't hold (e.g. more than 10% of your events have expected frequencies below 5) then consider using an exact test, such as Fisher's Exact Test for 2x2 contingency tables.
If the decision in the hypothesis test of the population correlation coefficient is to reject the null hypothesis. What can you conclude about the correlation in the population?
is notzero
Which test satisfies the time reversal test and factor reversal test?
Fisher's Index
What kind of test used to analyze data for experimental treatments?
After calculating the mean and standard deviationvalues each value of the independent variable in the data, these are a few common tests that are used to further analyse the data and highlight its significance:1) Pearson Correlation Coefficient- This is to test for a strong/weak positive/negative correlation between the independent variable and the dependent variable. However, correlation does not necessarily imply causation.2) t-test- This post-hoc test is used to determine the level of significance of the difference between two sets of data.3) Chi2 test- This test tests for whether the difference in Expected and Observed values are significant or not.4) Analysis of variance (ANOVA)- This is like a massive t-test to test an entire set of data, without inflating the error of the analysis results. This is usually coupled with Tukey's Honest Significant Difference test.
What are the release dates for The Fisher Family - 1952 Test of Love?
The Fisher Family - 1952 Test of Love was released on: USA: 6 September 1964
An observation that the higher the air temperature the lower the activity of test animals would be an example of what kind of correlation?
This would be an example of a negative correlation, where as one variable (air temperature) increases, the other variable (activity of test animals) decreases.
What kind of tests are used analyze data for experimental treatments?
After calculating the mean and standard deviationvalues each value of the independent variable in the data, these are a few common tests that are used to further analyse the data and highlight its significance:1) Pearson Correlation Coefficient- This is to test for a strong/weak positive/negative correlation between the independent variable and the dependent variable. However, correlation does not necessarily imply causation.2) t-test- This post-hoc test is used to determine the level of significance of the difference between two sets of data.3) Chi2 test- This test tests for whether the difference in Expected and Observed values are significant or not.4) Analysis of variance (ANOVA)- This is like a massive t-test to test an entire set of data, without inflating the error of the analysis results. This is usually coupled with Tukey's Honest Significant Difference test.
Example of parametric test?
The Fisher F-test for Analysis of Variance (ANOVA).
What is a weak negative correlation?
A negative correlation is when you compare 2 sets of data on a line graph (e.g. scores in a French test and scores in an English test), the higher one thing is, the lower the other is (e.g. someone might score 98% on the French test but only 12% on the English test (or visa versa)). A positive correlation is the other way around. A weak correlation is when there is a lot of deviation from the line of best fit (there will always be one with correlations as a line of best fit shows correlations after all) whereas with a strong correlation, there is little deviation.
