NTU Course
Overview

Statistics

Offered in 113-2
  • Notes

    The course is conducted in English。

  • Practice Group
    GroupTimeClassroomStudent QuotaTA QuotaComment
    A

    Mon 6

    		博雅409電腦教室241

  • NTU Enrollment Status

    Enrolled
    0/38
    Other Depts
    0/10
    Remaining
    0
    Registered
    0
  • Course Description
    This introductory course provides a foundation in statistical concepts, methods, and their applications in biology and agriculture. It covers essential techniques for data exploration, analysis, and interpretation, enabling individuals to make data-driven decisions in these fields. The course focuses on developing the skills and knowledge needed to effectively use statistical tools and methods, including the statistical software R. Topics will include descriptive statistics, basic probability, discrete and continuous distribution, sampling distribution, point estimation, confidence intervals, hypothesis testing, one-way analysis of variance, correlation, linear regression analysis, and chi-square test. Lab Description: Lab sessions include hands-on experience with the statistical software R. Students will learn how to perform statistical analysis and interpret its outputs.
  • Course Objective
    On successful completion of this course, students will be able to:
    • Summarize and visualize data using descriptive statistics and graphs.
    • Understand and apply probability theory and probability distributions.
    • Understand sampling techniques and the Central Limit Theorem.
    • Conduct hypothesis testing and construct confidence intervals for inferential statistics.
    • Compare means using ANOVA and assess relationships with correlation and regression.
    • Analyze categorical data using chi-square tests.
    • Communicate statistical results effectively and apply them to real-world problems.
    • Use statistical software (e.g., R) to perform data analysis and interpret results.
  • Course Requirement
    • This course will be conducted entirely in English, including all lectures, course materials, and assignments.
    • Cheating and plagiarism in any form of assessment are considered serious academic misconduct and will be handled according to university policy.
    • Absence from the mid-term or final exam without obtaining official leave through the university procedure will result in a grade of 0% with no opportunity for a re-sit.
    • Assignments submitted after the deadline will receive a grade of 0%.
  • Expected weekly study hours before and/or after class
    2-6 hours per week
  • Office Hour
  • Designated Reading
  • References
    • 📖 Introduction to statistics and data analysis: with exercises, solutions and applications in R (2022) by Christian Heumann, Michael Schomaker, Shalabh
    • 📖 Biostatistics with R an introduction to statistics through biological data (2012) by Babak Shahbaba
    • 📖 Introductory Statistics (2023) by OpenStax
  • Grading
    15%

    Labs

    30%

    Assignments

    20%

    Midterm exam

    35%

    Final exam

  • Adjustment methods for students
  • Make-up Class Information
  • Course Schedule
    Feb/17Week 01Introduction (no lab this week)
    Feb/24Week 02Descriptive statistics
    Mar/03Week 03Basic Probability
    Mar/10Week 04Discrete random variables
    Mar/17Week 05Continuous random variables and normal distribution
    Mar/24Week 06Sampling distribution and point estimation
    Mar/31Week 07Interval estimation and point estimation
    Apr/07Week 08Hypothesis testing (I)
    Apr/14Week 09Midterm exam
    Apr/21Week 10Hypothesis testing (II)
    Apr/28Week 11Hypothesis testing (III) and Analysis of Variance
    May/05Week 12Analysis of Variance
    May/12Week 13Correlation and linear regression
    May/19Week 14 NO CLASS
    May/26Week 15Chi-square test for categorical data
    Jue/02Week 16Final exam