I'm guessing that your logfile is written by a device with a 'locale' that differs from the Internationalization settings on your machine, which makes it harder for Excel to properly recognize the timestamps. That option allows you to further specify the DMY order. "General" is the default, which does a fine job of recognizing dates, but you may need to nudge the process in the right direction by explicitly choosing "Date" type. Note how the Text Import Wizard offers the feature to customize each of the imported columns for data type. So your actual problem is getting the proper timestamps imported from some logfile.
#POSITIVE ASSOCIATION SCATTER PLOT SERIES#
Based on no input, Excel has made up a series of consecutive dates starting with the awesome Zeroeth of January, 1900 ! I got exactly the same 7 labels "12:00:00 AM" like you had, but the ultimate indication of what is going on, is to put the axis labels in date format. I had to seriously tweak a CSV import to get this wrong on purpose - Excel seems to be getting frighteningly smart at recognizing import data. As noted by the error stands out because the text data are left-justified. I reproduced your problem by intentionally entering text instead of time data in the first column. When r ~, and when r 0 the data have a positive association.When entering proper timestamps in Excel, the scatterplot comes out right immediately (even though the axis labels need some tailoring to taste). Only data points with a scatterplot which is a perfectly straight line can have r = -1 or r = 1. The closer r is to 1 or -1, the less scattered the points are and the stronger the relationship. If and were both large with opposite signs, then there would be a ~. When the coordinates and are both large (with the same sign) relative to the other coordinates, then will be large as well, indicating a positive association between the -th row and the -th column category of the contingency table.
Describe patterns such as clustering, outliers, positive or ~, linear association, and nonlinear association. Ĭonstruct and interpret scatter plots for bi variate measurement data to investigate patterns of association between two quantities. Values taken can range from -1 (perfect ~) to + 1 (perfect positive association), with 0 representing lack of linear association (Note: for rank correlation. Gamma test: No adjustment for either table size or ties.Ĭan be obtained by parametric (Pearson's) or non-parametric methods ( rank correlation). A value of zero indicates the absence of association. The values range from âˆ'1 (100% ~, or perfect inversion) to +1 (100% positive association, or perfect agreement). The data points tend to cluster around a line with positive slope. As one variable gets larger, so does the other, on average. The first scatter plot shows a strong positive association between the two variables. If one variable tends to decrease when the other one does, we call it a ~. Miles of running per week and time in a marathon. Weight and grade point average for high school students. Explain.Īmount of alcohol consumed and result of a breath test. įor each of the following pairs of variables, is there likely to be a positive association, a ~, or no association. Also two numerical variables are said to have ~ if the values of one variable tend to decrease as the values of the other. In particular, two numerical variables are said to have positive association if the values of one variable tend to increase as the values of the other variable increase. Positive association is also the same as a positive correlation coefficient and ~ is the same as a negative correlation coefficient. There are two directions of association: positive association and ~. The next thing we to do is somehow quantify the strength and direction of the relationship between two variables. There is no association when the score for one variable does not predict the score for the other.Ī strong association is when even a small change in one is associated with a change in the other. Nonlinear association: Two variables in a scatter plot have a nonlinear association if the points form a pattern which is not close to a straight line.Ī negative association means that high scores for one variable tend to occur with low scores for the other. Negative association: A negative association between two variables means that when one increases, the other one usually decreases.
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