Interpreting Trends

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Interpreting Trends

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Intrepreting trends-general direction in which something is developing or changing.
1. Shifts in data- adding a constant k to each value in a data set. Shifts the mean and mode by k.
  1. Does not change std dev, variance, % vaues
    1. ex. (shipping items in same box size)
2. Decrease and Increase in trends
  1. Positive trend/correlation- as one data set increases so do the other
  2. Negatative trend/correlation- as one data set increases the other decreases.
3. Analyzing trends- technique for determining an underlying pattern.
  1. Regression analysis- determines a relationship between independent (x)& dependent (y) variables.
  2. Exrapolation- estimation beyond the data

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Today we’re going to be talking about interpreting trends.

So many times that we collect data we do so in order to understand what might be happening a certain time or to see what happens over time. We graph data to look for Trends that give us an idea of an underlying pattern. Like here is a graph generated by Google Trends on the number of Google searches worldwide for the term flu from Dec 2018 to Dec 2019. We can look for trends and possibly infer from this data.

Shifts in data can happen when a constant is added across the board (to each data point). When this happens this will shift the value of the mean and mode. A typical shift will not change the standard deviation, variance or percent values however. So a good example of this would be if you were looking at say the weights of various items that are approximately the same sie and can use the same box for shipping. If that same box suddenly was changed to a heavier version. The values would shift. This would increase the mean and mode but the data’s STD dev, variance and % values would not be affected.

Positive trends in data show a relationship between X & Y . As one value increases so to does the other. Like exercise and heart rate. Negative trends are opposing. As one value increases the other decreases. Like for example exercise and BMI.

We can graph data and understand whether a trend is positive or negative by creating a line of regression/ trend line. This is a line of best fit between the data set that shows a relationship between the x and y values.

And from that line we can also extrapolate and estimate from the trend line what the values would be beyond that which was collected. This is usually represented with a dotted/dash line extending from the trend line.

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