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How do you compare two data groups?
When comparing two groups, you need to decide whether to use a paired test. When comparing three or more groups, the term paired is not apt and the term repeated measures is used instead. Use an unpaired test to compare groups when the individual values are not paired or matched with one another.
What does a two-way ANOVA test tell you?
A two-way ANOVA test is a statistical test used to determine the effect of two nominal predictor variables on a continuous outcome variable. By using ANOVA, a researcher is able to determine whether the variability of the outcomes is due to chance or to the factors in the analysis.
How to statistically compare two time series?
This common model could be estimated globally and separately for each of the two series and then one could construct an F test to test the hypothesis of a common set of parameters. Consider the grangertest () in the lmtest library. It is a test to see if one time series is useful in forecasting another. Just came across this.
What kind of regression is used to compare two groups?
If your experiment is more than just a comparison of two groups, Scherbaum et al (2010) have done something similar with 101 regression models, allowing them to show the influence of different factors at different points in time. An alternative, complimentary approach is to use growth curve analysis, otherwise known as a polynomial regression.
How to find trends in a time series?
When you have ARIMA model, you have some estimates of trend for each of your points. Of course, if there are some local trends, they can be analysed additionally. Another approach is to use ARIMA with drift. Then estimates of the drift parameter will indicate the level of decline.
Is there a good correlation between two series?
If the r is small your conclusion would be that they are weakly related and so no desirable comparisons and a larger value if r would suggest good comparisons s between the two series. The third step where there is good correlation is to test the statistical significance of the r.