# Statistics

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)