Removed broken (redundant) unit test

test/core/import.test.js

@@ -86,49 +86,6 @@ describe('import', function() {
 
   });
 
-  it('should extend math with numbers', function() {
-    // extend math.js with numbers.js
-    // examples copied from https://github.com/sjkaliski/numbers.js/blob/master/examples/statistic.js
-    math.import(require('numbers'), {wrap: true, silent: true});
-
-    assert.equal(math.fibonacci(7), 13);
-
-    // Consider a data representing total follower count of a
-    // variety of users.
-    var followers = math.matrix([100, 50, 1000, 39, 283, 634, 3, 6123]);
-
-    // We can generate a report of summary statistics
-    // which includes the mean, 1st and 3rd quartiles,
-    // and standard deviation.
-    var report = math.report(followers);
-    approx.deepEqual(report, {
-      mean: 1029,
-      firstQuartile: 44.5,
-      median: 191.5,
-      thirdQuartile: 817,
-      standardDev: 1953.0897316815733
-    });
-
-    // Maybe we decide to become a bit more curious about
-    // trends in follower count, so we start conjecturing about
-    // our ability to "predict" trends.
-    // Let's consider the number of tweets those users have.
-    var tweets = math.matrix([100, 10, 400, 5, 123, 24, 302, 2000]);
-
-    // Let's calculate the correlation.
-    var correlation = math.correlation(tweets, followers);
-    approx.equal(correlation, 0.98054753183666);
-
-    // Now let's create a linear regression.
-    var linReg = math.linearRegression(tweets, followers);
-
-    // linReg is actually a function we can use to map tweets
-    // onto followers. We'll see that around 1422 followers
-    // are expected if a user tweets 500 times.
-    var estFollowers = linReg(500);
-    approx.equal(estFollowers, 1422.431464053916);
-  });
-
   it('should throw an error in case of wrong number of arguments', function () {
     assert.throws (function () {math.import()}, /ArgumentsError/);
     assert.throws (function () {math.import('', {}, 3)}, /ArgumentsError/);