# Applied Statistics

Eğitmen: Dr. H. Sait Ölmez

Eğitimin İçeriği:

Descriptive Statistics

• Introduction
• Major types and characteristics of data
• Experiment design and setup: Identifying the target and predictor variables; Collecting and preparing data for analysis
• Exploratory Data Analysis (EDA): Visualization techniques for summarizing and displaying
• Univariate Analysis of Data
• Tabular presentation of data: Histograms (frequency distribution)
• Common shapes of distributions and skewness
• Measure of central tendency in data (mean, median, mode, range)
• Measure of spread (variability) in data (variance and standard deviation)
• Quantile-Quantile plots
• Bivariate Analysis of Data from a visualization perspective
• Relations between any combinations of categorical and continuous variables
• A primer on Probability
• Basic rules of probability
• Conditional probability (Bayes’ rule)
• Discrete probability distributions
• Continuous probability distributions
• Properties of a Normal Distribution
• Standard Normal Distribution and its applications

Inferential Statistics

• Point/Interval Estimators
• Basic terminology in sampling process
• Description and calculation of point estimators
• The use and importance of interval estimators
• Forming and interpreting the uncertainty around a point estimation using confidence intervals
• Hypothesis Testing
• Forming a proper hypothesis
• Critical components of a hypothesis test: Significance level and the p-value
• 1-sample z-test vs 1-sample t-test
• 2-sample t-tests
• Types of errors (Type I and Type II)
• Power of a hypothesis test and the factors that affect power
• Problems with hypothesis tests and why the p-value isn’t enough
• Effect size and its importance from a practical standpoint
• Statistical Tests
• A summary on statistical tests and which one to use when
• Pearson’s correlation
• Spearman’s rank-order correlation
• Point-Biserial correlation
• Chi-square test
• One-way ANOVA test
• Simple/Multiple Linear Regression
• Simple Linear Regression
• Ordinary Least Squares solution
• Multiple Linear Regression
• Interpretation of regression coefficients
• Assessing the overall quality of a regression
• Assumptions of a Linear Regression solution and how to check them
• Outliers in Linear Regression and interpretation of Cook’s distance
• Converting nominal predictors to numerical values and the dummy variable trap
• Model selection process
• Regularization in Linear Regression
• What is regularization and why do we need it?
• Ridge and Lasso regularization

Eğitimin Hedef Kitlesi: Kurum içinde “Veri Analisti” ve/veya “Veri Bilimci” olarak çalışanlar.

Eğitimin Seviyesi: Başlangıç-Orta

Eğitim Süresi: 5 gün

Ek Bilgiler: Katılımcıların Python programlama dilini pratik seviyede kullanabildikleri varsayılmaktadır.