### Sesi 1. Introduction: Basic concepts of Biostatistics in Public Health

#### Learning Objectives:

1. Identify the contents of course syllabus,
2. Identify the laboratory exercise activities,
3. Record on how course evaluates students’ activities
4. List the use of biostatistics
5. Assess the difference between nominal, ordinal, and metric discrete and metric continuous variables.
6. Distinguish the type of a variable.
7. Identify the non-numeric nature of ordinal data.
8. Illustrate examples of dependent, independent and confounding variables

1. Rosner B (2016). General Overview.  In Rosner: Fundamentals of Biostatistics, 8th ed. Chapter 1, pp: 1-4. California: Chengage Learning.

### Sesi 2. Descriptive Data Analysis Using Table, Graph and Numbers

#### Learning Objectives:

By end of class, students will be able to:
Table:

1. Assessing the use of table for each type of data,
2. Differentiate a frequency distribution,
3. Create a frequency table from raw data,
4. Constructs relative frequency, cumulative frequency and relative cumulative frequency tables.
5. Construct grouped frequency tables.
6. Construct a cross-tabulation table.
7. Illustrate the use of a contingency table is.
8. Create table with rank data.

Graph:

1. Assessing the most appropriate chart for a given data type.
2. Construct pie charts and simple, clustered and stacked, bar charts.
3. Create histograms.
4. Create step charts and ogives.
5. Construct time series charts, including statistics process control (SPC).
6. Interpret and assess a chart reveals.
7. Assess the meaning by looking at the ‘shape’ of a frequency distribution.
8. Appraise negatively skewed, symmetric and positively skewed distributions.
9. Describe a bimodal distribution.
10. Describe the approximate shape of a frequency distribution from a frequency table or chart.
11. Assess whether data is considered a normal distribution.

Numeric Summary:

1. Describe a summary measure of location is, and understand the meaning of, and the difference between, the mode, the median and the mean.
2. Compute the mode, median and mean for a set of values.
3. Formulate the role of data type and distributional shape in choosing the most appropriate measure of location.
4. Describe what a percentile is, and calculate any given percentile value.
5. Describe what a summary measure of spread is
6. Differentiate the difference between, and can calculate, the range, the interquartile range and the standard deviation.
7. Interpret estimate percentile values
8. Formulate the role of data type and distributional shape in choosing the most appropriate measure of spread.

1. Rosner B (2016). Descriptive Statistics.   In Rosner: Fundamentals of Biostatistics, 8th ed. Chapter 2, pp: 5-35. California: Chengage Learning.

### Sesi 3. Population, Parameter, Statistics and the Probability Application on Statistics Distribution

#### Learning Objectives:

By end of class, students will be able to:

1. Review definition of probability
2. Recall some useful probabilistic notation
3. Give example the multiplication law of probability
4. Give example the addition law of probability
5. Describe the proportional frequency approach to calculating probability.
6. Define and describe the idea of risk and its relationship with probability.
7. Calculate the risk of some outcome from a contingency table and interpret the result.
8. Define and describe the idea of odds.
9. Calculate odds from a case-control 2 × 2 table and interpret the result.
10. Illustrate the equation linking probability and odds and be able to calculate one given the other.
11. Apply conditional probability
12. Apply Bayes’ rule and screening tests
13. Apply Bayesian inference
14. Demonstrate making ROC curves
15. Prevalence and incidence
16. Describe what the risk ratio of some outcome is, calculate a risk ratio and interpret the result

1. Rosner B (2016). Probability.  In Rosner: Fundamentals of Biostatistics, 8th ed. Chapter 3, pp: 42-65. California: Chengage Learning.

### Sesi 4. Discrete and Continues Probability Distributions

#### Learning Objectives:

By end of class, students will be able to:

1. Review concept of random variables
2. Describe the probability-mass function for a discrete random variable
3. Compute the expected value of a discrete random variable
4. Describe the variance of a discrete random variable
5. Describe the cumulative-distribution function of a discrete random variable
6. Illustrate permutations and combinations
7. Describe the binomial distribution
8. Compute expected value and variance of the binomial distribution
9. Describe the Poisson distribution
10. Describe computation of Poisson probabilities
11. Describe expected value and variance of the Poisson distribution
12. Compute Poisson approximation to the binomial distribution
13. Describe general concepts of continues distribution
14. Illustrate the normal distribution
15. Describe properties of the standard normal distribution
16. Illustrate conversion from an N(μ,σ2) distribution to an N(0,1) distribution
17. Describe linear combinations of random variables
18. Describe normal approximation to the binomial distribution
19. Describe normal approximation to the poisson distribution

1. Rosner B (2016). Discrete Probability Distributions.  In Rosner: Fundamentals of Biostatistics, 8th ed. Chapter 4, pp: 77-107. California: Chengage Learning.
2. Rosner B (2016). Continuous Probability Distributions.  In Rosner: Fundamentals of Biostatistics, 8th ed. Chapter 5, pp: 115-142. California: Chengage Learning.

### Sesi 5. Estimation

#### Learning Objectives:

By end of class, students will be able to:

1. Describe the relationship between population and sample
2. Illustrate random-number tables
3. Discussed estimation from difference research design (i.e.: survey vs randomized clinical trials)
4. Formulate estimation of the mean of a distribution
5. Formulate estimation of the variance of a distribution
6. Formulate estimation for the binomial distribution
7. Formulate estimation for the Poisson distribution
8. Describe confidence intervals (Cis)

1. Rosner B (2016). Estimation.  In Rosner: Fundamentals of Biostatistics, 8th ed. Chapter 6, pp: 154-203. California: Chengage Learning.

### Sesi 6. Hypothesis Testing: One-Sample Inference

#### Learning Objectives:

By end of class, students will be able to:

1. Describe general concepts of hypothesis testing
2. Describe one-sample test for the mean of a normal distribution: one-sided alternatives
3. Describe one-sample test for the mean of a normal distribution: two-sided alternatives
4. Apply sample test for the mean of a normal distribution: one and two sided alternatives
5. Formulate the use of test for the mean of a normal distribution: one and two sided alternatives
6. Interpret the results of test for the mean of a normal distribution: one and two sided alternatives
7. Describe basic concept of one-sample inference for the binomial and Poisson distributions
8. Apply  one-sample inference for the binomial and Poisson distributions
9. Formulate the use of one-sample inference for the binomial and Poisson distributions
10. Calculate the power of a test
11. Apply sample-size determination for public health research
12. Describe the relationship between hypothesis testing and confidence intervals
13. Describe Bayesian inference
14. Describe basic concept of one-sample χ2 test for the variance of a normal distribution
15. Apply  one-sample χ2 test for the variance of a normal distribution
16. Formulate the use of one-sample χ2 test for the variance of a normal distribution
17. Interpret the results of one-sample χ2 test for the variance of a normal distribution
18. Interpret the results of one-sample inference for the binomial and Poisson distributions

1. Rosner B (2016). Hypothesis Testing: One-Sample Inference.  In Rosner: Fundamentals of Biostatistics, 8th ed. Chapter 7, pp: 211-269. California: Chengage Learning.

### Sesi 7. Hypothesis Testing: Two-Sample Inference

#### Learning Objectives:

By end of class, students will be able to:
The paired t test and interval estimation for the comparison of means from two paired samples

1. Describe basic concept of the paired t test and interval estimation for the comparison of means from two paired samples
2. Apply the paired t test and interval estimation for the comparison of means from two paired samples
3. Formulate the use of the paired t test and interval estimation for the comparison of means from two paired samples
4. Interpret the results of the paired t test and interval estimation for the comparison of means from two paired samples

Apply two-sample t test for independent samples with equal variances and interval estimation for the comparison of means

1. Describe basic concept of two-sample t test for independent samples with equal and unequal variances and interval estimation for the comparison of means from two independent samples
2. Apply  two-sample t test for independent samples with equal and unequal variances and interval estimation for the comparison of means from two independent samples
3. Formulate the use of two-sample t test for independent samples with equal and unequal variances and interval estimation for the comparison of means from two independent samples
4. Interpret the results of two-sample t test for independent samples with equal and unequal variances and interval estimation for the comparison of means from two independent samples
5. Assess the treatment of outliers
6. Estimate sample size and power for comparing two means
7. Calculate sample-size estimation for longitudinal studies

1. Rosner B (2016). Hypothesis Testing: Two-Sample Inference.  In Rosner: Fundamentals of Biostatistics, 8th ed. Chapter 8, pp: 279-320. California: Chengage Learning.

### Sesi 8. Nonparametric Methods

#### Learning Objectives:

By end of class, students will be able to:
The Sign test
Describe basic concept of the Sign test

1. Apply the Sign test for public health data
2. Formulate the use of the Sign test
3. Interpret the results of the Sign test

The Wilcoxon signed-rank test

1. Describe basic concept of the Wilcoxon signed-rank test
2. Apply  the Wilcoxon signed-rank test for public health data
3. Formulate the use of the Wilcoxon signed-rank test
4. Interpret the results of the Wilcoxon signed-rank test

The Wilcoxon rank-sum test

1. Describe basic concept of the Wilcoxon rank-sum test
2. Apply  the Wilcoxon rank-sum test
3. Formulate the use of the Wilcoxon rank-sum test
4. Interpret the results of the Wilcoxon rank-sum test

1. Rosner B (2016). Nonparametric Methods.  In Rosner: Fundamentals of Biostatistics, 8th ed. Chapter 9, pp: 338-365. California: Chengage Learning.

### Sesi 9. Hypothesis Testing: Categorical Data

#### Learning Objectives:

By end of class, students will be able to:
Binomial proportions and Fisher’s exact test

1. Describe basic concept of two-sample test for binomial proportions and Fisher’s exact test
2. Apply two-sample test for binomial proportions and Fisher’s exact test for public health data
3. Formulate the use of two-sample test for binomial proportions and Fisher’s exact test
4. Interpret the results of two-sample test for binomial proportions and Fisher’s exact test

Two-sample test for binomial proportions for matched-pair data (McNemar’s test)

1. Describe basic concept of two-sample test for binomial proportions for matched-pair data (McNemar’s test)
2. Apply two-sample test for binomial proportions for matched-pair data (McNemar’s test)
3. Formulate the use of two-sample test for binomial proportions for matched-pair data (McNemar’s test)
4. Interpret the results of two-sample test for binomial proportions for matched-pair data (McNemar’s test)

R × c contingency tables and Chi-Square goodness-of-fit test

1. Describe basic concept of R × c contingency tables and Chi-Square goodness-of-fit test
2. Apply R × c contingency tables and Chi-Square goodness-of-fit test
3. Formulate the use of R × c contingency tables and Chi-Square goodness-of-fit test
4. Interpret the results of R × c contingency tables and Chi-Square goodness-of-fit test

The Kappa statistic

1. Describe basic concept of the Kappa statistic
2. Apply the Kappa statistic for public health data
3. Formulate the use of the Kappa statistic
4. Interpret the results of the Kappa statistic
5. Estimate sample size and power for comparing two binomial proportions

1. Rosner B (2016). Hypothesis Testing: Categorical Data.  In Rosner: Fundamentals of Biostatistics, 8th ed. Chapter 10 , pp: 372-439. California: Chengage Learning.

### Sesi 10. Simple Regression and Correlation Methods

#### Learning Objectives:

By end of class, students will be able to:
A simple regression

1. Describe basic concept of simple regression (i.e.: the method of least squares, inferences about parameters from regression lines, interval estimation for linear regression, and assessing the goodness of fit of regression lines)
2. Apply simple regression for public health data
3. Formulate the use of simple regression
4. Interpret the results of simple regression

A simple correlation

1. Describe basic concept of a simple correlation method
2. Apply correlation method to public health data
3. Formulate the use of correlation method
4. Interpret the results of coefficient correlation, including interval estimation and hypothesis testing.

1. Rosner B (2016). Regression and Correlation Methods.  In Rosner: Fundamentals of Biostatistics, 8th ed. Chapter 11, pp: 457-540. California: Chengage Learning.

### Sesi 11. Multiple Regression and Correlation Methods

#### Learning Objectives:

By end of class, students will be able to:
A Multiple regression

1. Describe basic concept of multiple regression
2. Apply multiple regression equation for public health data
3. Formulate multiple regression modelling
4. Interpret multiple regression model, including the use of dummy variables

Partial and multiple correlations

1. Describe basic concept of partial and multiple correlations
2. Apply partial and multiple correlation equation for public health data
3. Formulate partial and multiple correlations
4. Interpret partial and multiple correlations

A rank correlation

1. Describe basic concept of rank correlation
2. Apply partial and rank correlation for public health data
3. Formulate rank correlation
4. Interpret rank correlation

1. Rosner B (2016). Regression and Correlation Methods.  In Rosner: Fundamentals of Biostatistics, 8th ed. Chapter 11, pp: 457-540. California: Chengage Learning.

### Sesi 12. Multi-sample Inference

#### Learning Objectives:

By end of class, students will be able to:

1. Describe basic concept of one-way analysis of variance (ANOVA) and A Multiway ANOVA (i.e.: Two or three ways ANOVA)
2. Apply one-way ANOVA and A Multiway ANOVA
3. Formulate the use of one-way ANOVA and A Multiway ANOVA
4. Interpret the result of the use of one-way ANOVA and A Multiway ANOVA, including the hypothesis testing multiple   and comparisons of specific groups in one-way ANOVA
5. Illustrate statistical model for fixed and random effects of ANOVA
6. Compute the Intra-class Correlation Coefficient (ICC)
7. Illustrate the use of mixed models in public health research

1. Rosner B (2016). Multisample Inference.  In Rosner: Fundamentals of Biostatistics, 8th ed. Chapter 12, pp: 551-621. California: Chengage Learning.

### Sesi 13. Design and Analysis Techniques for Epidemiologic Studies

#### Learning Objectives:

By end of class, students will be able to:

2. Formulate measures of effect for categorical data
3. Compute and interpret Odds Ratios (OR), Relative Risk (RR), and  population attributable risk (PAR)
4. Asses confounding and justify the use of standardization approach
5. Apply methods of inference for stratified categorical data—the Mantel-Haenszel test
6. Compute power and sample-size estimation for stratified categorical data
7. Describe basic concept of multiple logistic regression and its extension
8. Apply multiple logistic regression and its extension for public health research
9. Formulate the use multiple logistic regression and its extension
10. Interpret models of multiple logistic regression and its extension
11. Illustrate the use of meta-analysis in epidemiology study
12. Describe basic statistical analysis for equivalence studies, the cross-over design, clustered binary data and longitudinal data analysis, including to deal with measurement-error methods and missing data

1. Rosner B (2016). Design and Analysis Techniques for Epidemiologic Studies  in B Rosner: Fundamentals of Biostatistics, 8th ed. Chapter 13, pp: 633-762. California: Chengage Learning.

### Sesi 14. Hypothesis Testing: Person-Time Data using Life Table and Survival Analysis

#### Learning Objectives:

By end of class, students will be able to:

1. Formulate measure of effect for person-time data
2. Illustrate one-sample inference for incidence-rate data
3. Illustrate two-sample inference for incidence-rate data
4. Compute power and sample-size estimation for person-time data
5. Inference for stratified person-time data
6. Compute power and sample-size estimation for stratified person-time data
7. Illustrate testing for trend: incidence-rate data
8. Describe basic concept of survival analysis
9. Describe basic concept, apply, formulate and interpret result of survival curves: The Kaplan-Meier estimator
10. Compute log-rank test
11. Describe basic concept of the proportional-hazards model
12. Apply  the proportional-hazards model for public health data
13. Construct best model for Cox Proportional Hazard Model