week 3 advanced data analysis

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**Week 3: Advanced Data Analysis**

*Day 1: Logistic Regression*
- Introduction to Logistic Regression: Understand the basics of logistic regression for binary outcomes.
- Model Interpretation: Interpret odds ratios and assess model fit.
- Application in Finance: Explore how logistic regression is used in finance, such as credit risk modeling.

*Day 2: ANOVA and ANCOVA*
- Analysis of Variance (ANOVA): Conduct ANOVA to compare means among multiple groups.
- Analysis of Covariance (ANCOVA): Extend your analysis to control for covariates.
- Post Hoc Tests: Perform post hoc tests to identify significant group differences.

*Day 3: Time Series Analysis*
- Time Series Data: Introduction to time series data and its unique characteristics.
- Time Series Plotting: Create time series plots to visualize trends and patterns.
- Autocorrelation: Learn how to calculate and interpret autocorrelation.

*Day 4: Survival Analysis*
- Survival Data: Understand the concept of survival data and censoring.
- Kaplan-Meier Survival Curves: Generate Kaplan-Meier curves to estimate survival probabilities.
- Cox Proportional-Hazards Model: Learn about the Cox proportional-hazards model for survival analysis.

*Day 5: Non-parametric Tests*
- Wilcoxon Signed-Rank Test: Perform the Wilcoxon signed-rank test for paired data.
- Mann-Whitney U Test: Conduct the Mann-Whitney U test for two independent samples.
- Kruskal-Wallis Test: Explore the Kruskal-Wallis test for comparing more than two groups without assuming normality.

Certainly, let's continue with the teaching materials for the remaining days of Weeks 3, 4, 5, and 6 in the Stata program.

**Week 3: Advanced Data Analysis**

**Day 1: Logistic Regression**

**Objective:** To introduce students to logistic regression and its applications in Stata.

**Materials:**
- Stata software installed on students' computers.
- Sample dataset (e.g., "auto.dta" dataset).

**Presentation:**
1. Explain the concept of logistic regression for binary outcomes.
2. Introduce the `logit` command in Stata for logistic regression.
3. Discuss odds ratios, model interpretation, and assessing model fit.

**Stata Code and Demonstration:**
```stata
// Perform logistic regression for binary outcomes
logit foreign mpg weight

// Interpret logistic regression results
```

**Exercise:**
1. Ask students to perform a logistic regression analysis to model the probability of "foreign" (binary outcome) using "mpg" and "weight" as predictors.
2. Instruct them to interpret the logistic regression results, including odds ratios and their significance.

---

**Day 2: ANOVA and ANCOVA**

**Objective:** To teach students how to conduct Analysis of Variance (ANOVA) and Analysis of Covariance (ANCOVA) in Stata.

**Materials:**
- Stata software installed on students' computers.
- Sample dataset (e.g., "auto.dta" dataset).

**Presentation:**
1. Explain the concepts of ANOVA and ANCOVA for comparing group means.
2. Introduce the `oneway` and `anova` commands in Stata.
3. Discuss post hoc tests and model interpretation.

**Stata Code and Demonstration:**
```stata
// Perform one-way ANOVA to compare means
oneway price, by(foreign)

// Perform ANCOVA to control for covariates
anova price foreign c.mpg#c.weight
```

**Exercise:**
1. Instruct students to perform a one-way ANOVA to compare the means of "price" by "foreign" (categorical variable).
2. Ask them to perform an ANCOVA analysis with "price" as the dependent variable, "foreign" as the categorical factor, and "mpg" and "weight" as covariates.

---

**Day 3: Time Series Analysis**

**Objective:** To introduce students to time series analysis in Stata.

**Materials:**
- Stata software installed on students' computers.
- Time series dataset (e.g., financial data or "tsdata.dta" dataset).

**Presentation:**
1. Explain the characteristics of time series data.
2. Introduce the concepts of time series plotting, autocorrelation, and stationarity.
3. Discuss the importance of time series analysis in various fields.

**Stata Code and Demonstration:**
```stata
// Create a time series plot
tsline close, title("Time Series Plot: Stock Prices")

// Calculate autocorrelation and partial autocorrelation
ac close
pac close

// Perform Augmented Dickey-Fuller test for stationarity
adf close
```

**Exercise:**
1. Provide students with a time series dataset (e.g., stock prices) or use the "tsdata.dta" dataset.
2. Instruct them to create a time series plot to visualize the data.
3. Ask students to calculate and interpret autocorrelation and partial autocorrelation using the `ac` and `pac` commands.
4. Encourage them to perform an Augmented Dickey-Fuller test to assess stationarity with the `adf` command.

---

**Day 4: Survival Analysis**

**Objective:** To teach students about survival analysis and its application in Stata.

**Materials:**
- Stata software installed on students' computers.
- Survival data (e.g., time-to-event data).

**Presentation:**
1. Explain the concept of survival analysis and its use in modeling time-to-event data.
2. Introduce the Kaplan-Meier estimator for survival curves.
3. Discuss the Cox proportional-hazards model.

**Stata Code and Demonstration:**
```stata
// Generate Kaplan-Meier survival curves
sts graph, by(group) risktable failure

// Fit a Cox proportional-hazards model
stcox age treatment, eform
```

**Exercise:**
1. Provide students with survival data, such as time-to-event data for medical patients.
2. Instruct them to generate Kaplan-Meier survival curves for different groups using the `sts` command.
3. Ask students to fit a Cox proportional-hazards model to the data to explore factors affecting survival using the `stcox` command.

---

**Day 5: Non-parametric Tests**

**Objective:** To teach students non-parametric statistical tests and their application in Stata.

**Materials:**
- Stata software installed on students' computers.
- Sample dataset (e.g., "auto.dta" dataset).

**Presentation:**
1. Explain the concept of non-parametric tests for data that doesn't meet normality assumptions.
2. Introduce non-parametric tests like the Wilcoxon signed-rank test and Mann-Whitney U test.
3. Discuss when to use non-parametric tests instead of parametric tests.

**Stata Code and Demonstration:**
```stata
// Perform the Wilcoxon signed-rank test for paired data
signrank weight, mu0(3000)

// Perform the Mann-Whitney U test for two independent samples
ranksum weight, by(foreign)
```

**Exercise:**
1. Instruct students to perform a Wilcoxon signed-rank test to compare "weight" to a specified value (e.g., 3000) using the `signrank` command.
2. Ask them to perform a Mann-Whitney U test to compare the "weight" variable between "foreign" and "domestic" cars using the `ranksum` command.

---

The teaching materials for Week 3 cover advanced data analysis topics, including logistic regression, ANOVA, ANCOVA, time series analysis, survival analysis, and non-parametric tests in Stata. Each day includes Stata code examples and exercises to help students practice and deepen their understanding of these advanced statistical techniques.

• ← week 2 data analysis in stata
• Week 4: Panel Data and Advanced Topics →