Interpreting a trend line

Trend line and deviation


From the above discussion of trends in random data with known variancewe know the distribution of calculated trends to be expected from random trendless data.

The use of a linear trend line has been the subject of criticism, leading to a search for alternative approaches to avoid its use in model estimation.

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One of the alternative approaches involves unit root tests and the cointegration technique in econometric studies. The estimated coefficient associated with a linear trend variable such as time is interpreted as a measure of the impact of a number of unknown or known but unmeasurable factors on the dependent variable over one unit of time. Strictly speaking, that interpretation is applicable for the estimation time frame only.

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Outside that time frame, one does not know how those unmeasurable factors behave both qualitatively and quantitatively. Furthermore, the linearity of the time trend poses many questions: i Why should it be linear?

Pivot - how to calculate deviation between trendline and actual value?

Research results of mathematicians, statisticians, econometricians, and economists have been published in response to those questions.

If we consider a concrete example, the global surface temperature record of the past years as presented by the IPCC : [5] then the interannual variation is about 0.

Hence the trend is statistically different from 0.

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However, as noted elsewhere this trend line and deviation series doesn't conform to the assumptions necessary for least squares to be valid. Goodness of fit r-squared and trend[ edit ] Illustration of the effect of filtering on r2. All have the same trend, but more filtering leads to higher r2 of fitted trend line.

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The least-squares fitting process produces a value — r-squared r2 — which is 1 minus the ratio of the variance of the residuals to the variance of the dependent variable. It says what fraction of the variance of the data is explained by the fitted trend line.

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It does not relate to the statistical significance of the trend line see graph ; statistical significance of the trend is determined by its t-statistic. Often, filtering a series increases r2 while making little difference to the fitted trend.

Two-way tables Video transcript Shira's math test included a survey question asking how many hours students had spent studying for the test. The graph below shows the relationship between how many hours students spent studying and their score on the test. Shira drew the line below to show the trend in the data.

Real data may need more complicated models[ edit ] Thus far the data have been assumed to consist of the trend plus noise, with the noise at each data point being independent and identically distributed random variables and to have a normal distribution. Real data for example climate data may not fulfill these criteria.

The problem is that your x-axis values are not the actual numbers plotted on the line. They are essentially text, so the first 'x' is actually a 1 and not

This is important, as it makes an enormous difference to the ease with which the statistics can be analysed so as to extract maximum information from the data series.

If there are other non-linear effects that have a correlation to the independent variable such as cyclic influencesthe use of least-squares estimation of the trend is not valid.

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Also where the variations are significantly larger than the resulting straight line trend, the choice of start and end points can significantly change the result.

That is, the model is mathematically misspecified.

You can collect data for a series of trials where a dog is shown a treat of a given size and you measure the rate at which it wags its tail. In this situation, the experimental factor treat size varies continuously rather than in discrete categories. To examine the effect that the experimental factor has on the response variable the wag ratewe can plot each trial as a point on a type of graph called an X-Y scatter plot. By default, Excel considers the column on the left to contain the horizontal X values and the column on the right to contain the vertical Y values.

Statistical inferences tests for the presence of trend, confidence intervals for the trend, etc. Non-constant variance: in the simplest cases weighted least squares might be used. Non-normal distribution for errors: in the simplest cases a generalised linear model might be applicable.

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Unit root : taking first or occasionally second differences of the data, with the level of differencing being identified through various unit root tests. Trends in clinical data[ edit ] Medical and biomedical studies often seek to determine a link in sets of data, such as as indicated above three different diseases. In these cases one would expect the effect test statistic e.

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Suppose the mean level of cholesterol before and after the prescription of a statin falls from 5. Given sufficient power, an ANOVA would most likely find a significant fall at one and two months, but the fall is not linear.

Furthermore, a post-hoc test may be required. Should the cholesterol fall from 5. A linear trend estimation is a variant of the standard ANOVA, giving different information, and would be the most appropriate test if the researchers are hypothesising a trend effect in their test statistic.

Re: Pivot - how to calculate deviation between trendline and actual value?

One example trend line and deviation is of levels of serum trypsin in six groups of subjects ordered by age decade 10—19 years up to 60—69 years. Incidentally, it could be reasonably argued that as age is a natural continuously variable index, it should not be categorised into decades, and an effect of age and serum trypsin sought by correlation assuming the raw data is token date. A further example [8] is of a substance measured at four time points in different groups: mean [SD] 1 1.

However, should the data have been collected at four time points in the same individuals, linear trend estimation would be inappropriate, and a two-way repeated measures ANOVA applied.