Statistics

Statistics Bachelor Degree

Feel Free to Ask Questions!

Tel : +8615850513534

E-mail : apply@acasc.cn

  • Application Deadline:2017/04/02
  • Tuition:¥17000.00
  • Application Fee:¥800.00
  • Service Fee:¥350.00
How To Apply

Applying through ACASC generally takes a few minutes to complete. It takes 5 steps to complete the application.

1. Click “Apply Now” button at the top of the page.

2. Fill in online application form.

3. Upload required documents.

4. Pay the application fee and the ACASC service fee

5. Click “Submit” button.

Important notice: In order to apply, you need to create an account with ACASC.

Statistics is a mathematical body of science that pertains to the collection, analysis, interpretation or explanation, and presentation of data or as a branch of mathematics. Some consider statistics to be a distinct mathematical science rather than a branch of mathematics. While many scientific investigations make use of data, statistics is concerned with the use of data in the context of uncertainty and decision making in the face of uncertainty.A standard statistical procedure involves the test for the relationship between two statistical data sets, or a data set and a synthetic data drawn from idealized model. A hypothesis is proposed for the statistical relationship between the two data sets, and this is compared as an alternative to an idealized null hypothesis of no relationship between two data sets. Rejecting or disproving the null hypothesis is done using statistical tests that quantify the sense in which the null can be proven false, given the data that are used in the test. Working from a null hypothesis, two basic forms of error are recognized: Type 1 error (null hypothesis is falsely rejected giving a "false positive") and Type II error (null hypothesis fails to be rejected and an actual difference between populations is missed giving a "false negative"). Multiple problems have come to be associated with this framework: ranging from obtaining a sufficient sample size to specifying an adequate null hypothesis.a

share_phone_icon share_facebook_icon share_twitter_icon share_youtube_icon share_pinterest_icon share_linkedin_icon share_instagram_icon email_icon top_icon