Week 01: Tools for Computing and Reporting

LEARNING OBJECTIVES: After completing this week's .
    1. set up computing tools R, RStudio, and MikTex.
    2. use some R commands and functions.
    3. create an R Markdown document.
    4. install and load R packages.
TOPICS: Topics and materials for this week.
CLASS NOTES: ASSIGNMENTS:

Week 02: Nonparametric Bootstrap Method

LEARNING OBJECTIVES: After finishing this module, you will be able to
    1. justify when to use sampling distribution and bootstrap sampling distribution.
    2. construct bootstrap confidence interval of population parameters such as means, standard deviation, etc.
    3. write basic R code to perform bootstrap analysis and make basic plots.
TOPICS: The following topics will be covered this week.
ASSIGNMENTS:

Week 03: Simple Linear Regression Models

LEARNING OBJECTIVES: After finishing this module, you will be able to
    1. perform residual analysis of the SLR.
    2. Interpret the regression coefficient and goodness of fit measures.
    3. write R basic R code to perform bootstrap regression analysis.
TOPICS: The following topics will be covered this week.
ASSIGNMENTS:

Week 04: Multiple Linear Regression Models

LEARNING OBJECTIVES: After finishing this module, you will be able to
    1. use factor() to handle categorical variable.
    2. perform variable selection: step-wise method
    3. perform residual analysis of the SLR.
    4. write a mini report to summarize the output of MLR.
TOPICS: The following topics will be covered this week.
ASSIGNMENTS:

Week 05: Bootstrapping Regression Models

LEARNING OBJECTIVES: After finishing this module, you will be able to
    1. take bootstrap records
    2. use bootstrap residuals to define bootstrap responses
    3. justify the validity of inferences based on bootstrap and the normal regression modeling
    4. write a formal report.
TOPICS: The following topics will be covered this week.
  • Bootstrapping records
  • Bootstrapping residuals
  • Bootstrap confidence intervals of regression coefficients
  • R Applications: Bootstrap MLR with R
ASSIGNMENTS:

Week 06: Simple Logistic Regression Models

LEARNING OBJECTIVES: After finishing this module, you will be able to
    1. correct identify the response variable for a logistic model
    2. fit a logistic model with R
    3. interpret of regression coefficients
    4. make prediction with the logistic regression model.
TOPICS: The following topics will be covered this week.
  • Practical question and model formulation
  • Model structure - log odds regression
  • Interpretation of the regression coefficients
  • R Applications: Case study – logistic regression with R
ASSIGNMENTS:

Week 07: Multiple Logistic Regression Models

LEARNING OBJECTIVES: After finishing this module, you will be able to
    1. inspect and transform predictor variables
    2. perform automatical vaiable selection in loditic regression with R
    3. interpret of odds ratios
    4. interpret prediction and validation performance.
TOPICS: The following topics will be covered this week.
  • Models with only categorical predictor variables–Dummy variable
  • Variable selection methods and criteria
  • Interpretation of coefficients
  • R Applications: Case study – Multiple logistic regression with R
  • Notes
ASSIGNMENTS:

Week 08: Logistic Regression: Predictive Modeling

LEARNING OBJECTIVES: After finishing this module, you will be able to
    1. calculate the sensitivity and specificity
    2. calculate predictive error from the confusion matrix
    3. perform k-fold cross-validation for model selection
    4. plot ROC curve.
TOPICS: The following topics will be covered this week.
  • Estimating ture positive and false positive rates
  • Confusion matrix and predictive error
  • Cross-validation and ROC curve
  • Notes
ASSIGNMENTS:

Week 09: Poisson Regression Models

LEARNING OBJECTIVES: After finishing this module, you will be able to
    1. perform the regression analysis on counts
    2. perform the regression analysis on rates
    3. Interpret Poisson regression coefficients
TOPICS: The following topics will be covered this week.
  • structure of Poisson model
  • regression on counts and rates
  • model-based visualization
  • Notes
ASSIGNMENTS

Week 10: Dispersed Poisson Regression Models

LEARNING OBJECTIVES: After finishing this module, you will be able to
    1. calculate dispersion parameter
    2. build Quasi-Poisson regression models on counts and rates
    3. interpret quasi-Poisson models
TOPICS: The following topics will be covered this week.
  • Dispersion parameter
  • Dispersed Poisson regression on counts and rates
  • Modeling diagnostics
  • Notes
LAB Some basic in SQL ASSIGNMENTS:

Week 11: Concepts of Time Series

LEARNING OBJECTIVES: After finishing this module, you will be able to
    1. identify the types of times series.
    2. create time series objects.
    3. build the baseline forecast models.
    4. calculate the acuracy measures.
TOPICS: The following topics will be covered this week.
  • Technical terms of TS
  • Type of TS data
  • Time series objects and the baseline forecasting methods
  • Calculation of accuracy measures.
  • Class Note:
ASSIGNMENTS:

Week 12: Moving Average and LOESS Smoothing

LEARNING OBJECTIVES: After finishing this module, you will be able to
    1. decompose time series into trend, seasonal, and random commponents.
    2. use STL frame work to decompose TS.
    3. forecast with decomposition.
TOPICS: The following topics will be covered this week.
  • Components time series
  • Classical decomposition of TS
  • STL decomposition of TS
  • Forecast with decomposition.
  • Class Note
ASSIGNMENTS:

Week 13: Exponential Smoothing Models

LEARNING OBJECTIVES: After finishing this module, you will be able to
    1. recognize and identify ETS models.
    2. fit SES, trend and seasonal models to appropriate data.
    3. calculate prediction errors.
TOPICS: The following topics will be covered this week.
  • ETS framework
  • Simple exponential smoothing model
  • Holt's trend models
  • Holt-Winter's trend and seasonal smoothing models.
  • R Applications: Case study

ASSIGNMENTS:

Week 14: Finalizing Project Report and Prepare Oral Presentation

Schedule and Specifics

  • Specifics
  • Tentative Schedule (alphabetical order in the first name)