Apply Statistical Modeling Couse
Tools / Frameworks: Using R
Duration: 2 Weeks
Cost: $100
MODULE 1
INTRODUCTION TO R, DATA, AND
RESEARCH DESIGN
- Overview of statistical modeling and its
importance - Difference between data analysis and
statistical modeling - Types of research: descriptive,
explanatory, predictive - Types of variables and measurement
scales - Dependent and independent variables
- Formulating research questions and
hypotheses - Introduction to R and RStudio interface
- Basic R commands and working with
scripts - Importing datasets (CSV, Excel)
- Understanding data structure using
str(), head(), summary() - Reproducible research and project
organization
MODULE 2
DESCRIPTIVE STATISTICS AND BASIC
PLOTTING
- Measures of central tendency: mean,
median, mode - Measures of dispersion: variance,
standard deviation, IQR - Frequency tables and percentages
- Grouped statistics using dplyr
- Summarizing categorical and
numerical variables - Histograms and boxplots
- Bar charts and pie charts
- Scatterplots
- Identifying outliers
- Using descriptive statistics to guide
modeling
MODULE 3
PROBABILITY AND PROBABILITY
DISTRIBUTIONS
- Basic probability concepts
- Random variables
- Discrete and continuous
distributions - Normal distribution and
properties - Binomial distribution
- Poisson distribution
- Uniform distribution
- Probability functions in R (dnorm,
pnorm, rnorm) - Visualizing distributions
- Z-scores and standardization
- Role of distributions in statistical
modeling
MODULE 4
SAMPLING, CONFIDENCE INTERVALS,
AND HYPOTHESIS TESTING
- Population vs sample
- Sampling methods
- Sampling bias and error
- Central Limit Theorem (concept)
- Confidence intervals for means
and proportions - Interpreting confidence intervals
- Hypothesis testing framework
- Null and alternative hypotheses
- P-values and significance levels
- One-sample and two-sample
tests in R - Practical interpretation of results
MODULE 5
CHI-SQUARE TEST AND GROUP
MEAN DIFFERENCES
- Chi-square test of
independence - Chi-square test of goodnessof-
fit - Contingency tables in R
- Assumptions of Chi-square
tests - Interpreting Chi-square output
- Independent samples t-test
- Paired samples t-test
- One-way ANOVA
- Post-hoc tests
- Assumptions for group
comparison tests - Real-world applications
MODULE 6
CORRELATION AND REGRESSION
- Concept of correlation
- Pearson and Spearman
correlation - Correlation matrix
- Interpretation and limitations of
correlation - Simple linear regression theory
- Running regression using lm()
- Interpreting regression
coefficients - Model fit: R-squared
- Residual diagnostics
- Prediction using regression
models - Introduction to multiple
regression