Statistics (BA 1st grade)

Objective

In recent years, the need for data analysis has increased in the field of commerce. Especially, demand for data scientists is increasing in this field. Data scientists collect and analyze data by themselves. They also popose a business strategy, develop services or products, and develop marketing methods based on the data analysis. In this class, students will learn the basics of statistics which is required for data analysis. Final goal of this class is to produce data scientists.

Goal

- You can summarize data from the viewpoint of descriptive statistics.
- You can calculate probability.
- You can calculate probability disribution.
- You can calculate probability Bayes’ theorem.
- You can calculate sampling distribution.
- You can conduct interval estimation on population ratio and population mean.
- You can conduct statistical hypothesis testing on population ratio and population mean.

Schedule

Day 1: Data type: scale level, Licart scale

Day 2: Data type: discrete data, continuous data

Day 3: Data summarization: arithmetic mean, geometric mean, harmonic mean, absolute average, RMS, median, mode

Day 4: Data summarization: quartile range

Day 5: Data summarization: deviation, deviation squared sum, variance, variance formula, unbiased variance, standard deviation

Day 6: Data summarization: z-score, Frequency distribution

Day 7: Data summarization: histgram, box-plot

Day 8: Probability: permutation, combination, set theory

Day 9: Probability: sample space

Day 10: Probability: opration of events

Day 11: Probability: operation of probability

Day 12: Probability: conditional probability

Day 13: Probability: Bayes’ theorem

Day 14: Random variable: random variable, probability density function

Day 15: Random variable: mean of random variable

Day 16: Random variable: deviation of random variable

Day 17: Random variable: Bernoulli distribution, binomial distribution, Poisson distribution

Day 18: Random variable: uniform distribution, exponential distribution

Day 19: Random variable: normal distrubution

Day 20: Sample distribution: population, population ratio

Day 21: Sample distribution: population mean, central limit theorem

Day 22: Sample distribution: t-distrubution

Day 23: Sample distribution: unviased variance

Day 24: Estimation: interval estimation (population ratio)

Day 25: Estimation: interval estimation (population mean)

Day 26: Testing: procudure of statistical hypothesis testing

Day 27: Testing: statistical hypothesis testing (population ratio)

Day 28: Testing: statistical hypothesis testing (population mean)