Slideshare uses cookies to improve functionality and performance, and to provide you with relevant advertising. If you continue browsing the site, you agree to the use of cookies on this website. See our User Agreement and Privacy Policy.

Slideshare uses cookies to improve functionality and performance, and to provide you with relevant advertising. If you continue browsing the site, you agree to the use of cookies on this website. See our Privacy Policy and User Agreement for details.

Successfully reported this slideshow.

Like this presentation? Why not share!

- Prueba Kolmogorov-Smirnov by David Solis 10761 views
- KOLGOMOROV-SMIRNOV by El Turrito Cumbie... 5395 views
- kolmogorov smirnov PPT by PT Carbon Indonesia 712 views
- Prueba de kolmogorov smirnow by David Alejandro B... 2065 views
- Prueba de ks by enpfisica3 6885 views
- Non parametric tests by Raghavendra Huchc... 21038 views

No Downloads

Total views

2,956

On SlideShare

0

From Embeds

0

Number of Embeds

4

Shares

0

Downloads

128

Comments

0

Likes

5

No embeds

No notes for slide

- 1. The Kolmogorov-Smirnov Test XIMB
- 2. The Kolmogorov-Smirnov Test (K-S Test) is used to test the goodnessof-fit of a theoretical frequency distribution, i.e., whether there is a significant difference between an observed frequency distribution and a given theoretical (expected) frequency distribution. •Similar to what the Chi-Square test does, but the K-S test has several advantages: More powerful test. Easier to compute and use, as no grouping of data is required. The test statistic is independent of the expected frequency distribution. It only depends on the sample size n. THE HYPOTHESES: H0: The observed frequency distribution is consistent with the theoretical frequency distribution (Good fit). H1: The observed frequency distribution is not consistent with the theoretical frequency distribution (Bad fit). α = Level of significance of the test. •Here we use the cumulative probability distribution (CDF) of observed and theoretical frequencies.
- 3. The K-S Test Statistic: Here, Fe = the expected relative cumulative frequencies(CDF). Fo = the observed relative cumulative frequencies(CDF). •If the gap between Fe and Fo is large then Ho should be rejected. •The value of the test statistic is obtained from the observed data listed in the tabular form. •A K-S test is a one tailed test. •The critical values of Dn have been tabulated and can be found from the K-S table for corresponding levels of significance and sample size n. •The calculated value of Dn is compared with the critical value of Dn. If the calculated value > critical value, then reject H0.
- 4. Example: Pg # 834, Prob. # SC14-7. Soln.: H0: The distn. is normal with µ= 6.80, σ= 1.24. H1: Above not true. Value of the variable fo Cumulat ive fo Fo (obs. CDF) Fe (exp. CDF) |Fe – Fo| ≤ 4.009 13 13 0.0173 0.0122 0.0051 4.010-5.869 158 171 0.2280 0.2266 0.0014 5.870-7.729 437 608 0.8017 0.7734 0.0373 7.730-9.589 122 730 0.9733 0.9878 0.0145 >9.590 20 750 1.0000 1.0000 0.0000 We obtain Fe values from the normal table, z= (X- µ)/ σ. The calculated value of Dn is the maximum value in the | Fe - Fo | column. Thus, 0.0373. For 0.15 level of significance, Dcritical = 1.14/√n = 1.14/√750 = 0.0416. Dn < Dcritical , so accept H0 and conclude that it is a good fit.

No public clipboards found for this slide

×
### Save the most important slides with Clipping

Clipping is a handy way to collect and organize the most important slides from a presentation. You can keep your great finds in clipboards organized around topics.

Be the first to comment