Watson Art Test

[Karlotta Neifert]

Thescapholunate ligament is a strong ligament between the scaphoid and the lunate and an important stabilizer of the wrist. It can be injured by a fall or a sudden load on the wrist and can occur together with a distal radius fracture or a carpal fracture. When untreated an SL tear can lead to a so-called DISI which stands for dorsal intercalated segmental instability, which is a dissociation of the scaphoid and lunate.

According to a review by Valdes et al. (2013), the Watson Test has a positive mean likelihood ratio of 2.76 and a negative mean likelihood ratio of 0.25. This means that this test has a moderate clinical value to rule out SL ligament tears, but a low value to confirm an SL tear.

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To perform the test, the examiner grasps the wrist with their thumb over the scaphoid tubercle (volar aspect of the palm) in order to prevent the scaphoid from moving into its more vertically oriented position in ulnar deviation. For the test, the wrist needs to be in slight extension. The patient's wrist is then moved from ulnar to radial deviation. The examiner will feel a significant 'clunk' and the patient will experience pain if the test is positive. For completeness, the test must be performed on both wrists for comparison. If the scapholunate ligament is disrupted, the scaphoid will subluxate over the dorsal lip of the distal radius.

"The patient is approached by the examiner as if to engage in arm wrestling, face to face across a table with diagonally opposed hands raised (right to right or left to left) and elbows resting on the surface in between. With the patient's forearm slightly pronated, the examiner grasps the wrist from the radial side, placing his thumb on the scaphoid tuberosity (as if pushing a button to open a car door) and wrapping his fingers around the distal radius. The examiner's other hand grasps at the metacarpal level, controlling the wrist position. Starting in ulnar deviation and slight extension, the wrist is moved radially and slightly flexed with constant thumb pressure on the scaphoid. This radial deviation causes the scaphoid to flex. The examiners thumb pressure opposes this normal rotation, causing the scaphoid to shift in relation to the other bones of the carpus. This scaphoid shift may be subtle or dramatic. A truly positive test requires both pain on the back of the wrist (not just where you are pressing on the scaphoid tuberosity), and comparison with the opposite wrist is essential."

Watson's test is used by physicians to diagnose scapholunate instability.This test has a low specificity and sometimes is positive for capito-lunate instability. As many as 20% of normal wrists will also have a 'clunk'.

Negative serial correlation implies that a positive error for one observation increases the chance of a negative error for another observation and a negative error for one observation increases the chances of a positive error for another.

If the design matrix X \displaystyle \mathbf X of the regression is known, exact critical values for the distribution of d \displaystyle d under the null hypothesis of no serial correlation can be calculated. Under the null hypothesis d \displaystyle d is distributed as

Although serial correlation does not affect the consistency of the estimated regression coefficients, it does affect our ability to conduct valid statistical tests. First, the F-statistic to test for overall significance of the regression may be inflated under positive serial correlation because the mean squared error (MSE) will tend to underestimate the population error variance. Second, positive serial correlation typically causes the ordinary least squares (OLS) standard errors for the regression coefficients to underestimate the true standard errors. As a consequence, if positive serial correlation is present in the regression, standard linear regression analysis will typically lead us to compute artificially small standard errors for the regression coefficient. These small standard errors will cause the estimated t-statistic to be inflated, suggesting significance where perhaps there is none. The inflated t-statistic, may in turn, lead us to incorrectly reject null hypotheses, about population values of the parameters of the regression model more often than we would if the standard errors were correctly estimated.

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p = dwtest(mdl) returns the p-value of the Durbin-Watson Test on the residuals of the linear regression model mdl. The null hypothesis is that the residuals are uncorrelated, and the alternative hypothesis is that the residuals are autocorrelated.

p-value of the test, returned as a numeric value. dwtest tests whether the residuals are uncorrelated, against the alternative that autocorrelation exists among the residuals. A small p-value indicates that the residuals are autocorrelated.

The p-value of the Durbin-Watson test is the probability of observing a test statistic as extreme as, or more extreme than, the observed value under the null hypothesis. A significantly small p-value casts doubt on the validity of the null hypothesis and indicates autocorrelation among residuals.

The Durbin Watson statistic is a test statistic to detect autocorrelation in the residuals from a regression analysis. It is named after professor James Durbin, a British statistician and econometrician, and Geoffrey Stuart Watson, an Australian statistician.

Serial correlation, also called autocorrelation, refers to the degree of correlation between the values of variables across different data sets. It is usually used when working with time series data in which observations occur at different points in time (e.g., wind speed measured on different days of the week). If the wind speed values measured that occurred closer in time are more similar than the values that occurred farther apart in time, the data is said to be correlated.

In statistics, residuals are nothing but the difference between the observed value and the mean value that a particular model predicts for that observation. Residual values are extremely useful in regression analysis as they indicate the extent to which a model accounts for the variation in the given data.

Regression analysis is a method used in statistics that helps to identify which variables exert an impact on a particular experiment topic. The process helps determine which factors matter the most, which are to be ignored, and how the factors influence each other. Variables play an important role in regression, and it is important to understand the types of variables:

The Durban Watson statistic will always assume a value between 0 and 4. A value of DW = 2 indicates that there is no autocorrelation. When the value is below 2, it indicates a positive autocorrelation, and a value higher than 2 indicates a negative serial correlation.

Similarly, if the stock price fell yesterday, it is likely to fall today. However, if the test displays a negative serial correlation, it indicates that if the price rose yesterday, it would most likely fall today.

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When data set of interest is a time series data, we may want to compute the 1st-order autocorrelation for the variables of interest and to test if the autocorrelation is zero. One common test is Durbin-Watson test. The Durbin-Watson test statistic can be computed in proc reg by using option dw after the model statement.

The value of Durbin-Watson statistic is close to 2 if the errors are uncorrelated. In our example, it is .034. That means that there is a strong evidence that the variable open has high autocorrelation.

Inference - You'll need to draw conclusions based on a series of facts. You will be provided with a statement and a series of inferences based on the statement. You must evaluate whether the inferences are true, probably true, insufficient data, false, or probably false. You can make this decision based on information provided to you, or from common knowledge accepted by everyone. To get a better idea take a look at some sample questions.

Deduction - You'll be provided with facts followed by a set of conclusions. You're required to assess whether the conclusion can be logically deduced from the set of facts. You'll be provided with two choices when answering; the conclusion logically follows, or the conclusion doesn't logically follow.

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