Introduction to Statistics

Students will begin by working with descriptive statistics, learning about how to organize data and extract information using tables, graphs, averages, and standard deviations. Next, we will discuss the basic ideas of probability theory and learn about the law of large numbers and the central limit theorem. We will also study important probability distributions, such as normal distributions, t-distributions, and chi-square distributions. Subsequently, students will study inferential statistics; that is, interval estimation will be explained with regard to how to infer the properties of an entire object under study when only some data on the object can be obtained.

Descriptive statistics, inferential statistics, probabilities, normal distributions, interval estimation

1) Guidance
2) Frequency tables and histograms
3) Representative values
4) Standard deviation and data standardization
5) Random variables
6) Expected values of random variables and the law of large numbers
7) Continuous probability distribution
8) Normal distribution and the central limit theorem
9) T distribution and chi-square distribution
10) Basic concepts of inferential statistics and interval estimation
11) Interval estimation of the population mean of a normal population with known population variance
12) Interval estimation of the population mean of a normal population with unknown population variance
13) Interval estimation of the population variance of a normal population
14) Estimation of population proportion
15) T-test (course survey)
16) Final exam

Syllabus (Japanese site only)