In a Company of construction materials you want to analyse prospective preventive behaviour of a set of indicators of the production process Tile bilayers in order to identify historical trends and possible outcomes over the next productive periods. To test if the data is a time series or not, we have used
1. Runs above and below the median, calculates the number of times the series goes above or below the median. 2. Runs up and down: calculates the number of times the seesaw series. This number is compared with the expected value for a random time series. 3. Box-Pierce test: building a statistical test based on the first k sample autocorrelations to calculate. The three tests used to determine if a data set is a random sequence of numbers, or not. Since all three tests are sensitive to different types of deviations from random behavior, not pass either suggests that the time series could not be completely random.
The These tests were conducted in four selected indicators of Tile production.
The StatAdvisorThis procedure constructs various statistics and plots for Marmolina. The data cover 15 time periods. Select the desired tables and graphs using the buttons on the analysis toolbar. Tests for Randomness of Marmolina(1) Runs above and below median Median = 8,99 Number of runs above and below median = 9 Expected number of runs = 6,83333 Large sample test statistic z = 1,04103 P-value = 0,29786 (2) Runs up and down Number of runs up and down = 9 Expected number of runs = 9,66667 Large sample test statistic z = 0,10885 P-value = 0,913316 (3) Box-Pierce Test Test based on first 5 autocorrelations Large sample test statistic = 2,98587 P-value = 0,702165 The StatAdvisorThree tests have been run to determine whether or not Marmolina is a random sequence of numbers. A time series of random numbers is often called white noise, since it contains equal contributions at many frequencies. The first test counts the number of times the sequence was above or below the median. The number of such runs equals 9, as compared to an expected value of 6,83333 if the sequence were random. Since the P-value for this test is greater than or equal to 0,05, we cannot reject the hypothesis that the series is random at the 95,0% or higher confidence level. The second test counts the number of times the sequence rose or fell. The number of such runs equals 9, as compared to an expected value of 9,66667 if the sequence were random. Since the P-value for this test is greater than or equal to 0,05, we cannot reject the hypothesis that the series is random at the 95,0% or higher confidence level. The third test is based on the sum of squares of the first 24 autocorrelation coefficients. Since the P-value for this test is greater than or equal to 0,05, we cannot reject the hypothesis that the series is random at the 95,0% or higher confidence level. As can be seen both in the table of tests of randomness, no indicator shows fully satisfactory results, ie all data sets presented are not completely random time series. For this reason, it is concluded that the results of process does not allow the analysis of their behaviour. (just a sample of the whole study) |