Here are the slides for the lectures. The slides are in pdf format. You can download them and use them offline.

Chapter 0 - ECO 104 Recap

  • Slides : Ch0 - ECO 104 Recap, also Probability Theory Chapter 104

  • Key concepts are

    • Population and Sample
    • Random Variables and Random Sample
    • Probability Distributions
    • Expectation and Variance
    • Some Important Distributions (Binomial and Normal, etc.)
    • Please Note: The slides may get updated later, so please check back frequently.
    • last updated: 2025-09-08

Chapter 1 - Estimation

  • Slides: Ch1 - Statistical Estimation

  • Key concepts are

    • Point Estimation
    • Properties of Point Estimators
    • Interval Estimation
    • Confidence Intervals for Mean and Proportion
    • For proportion the details are coming from Bernoulli Random Variables, you can see some concepts in Probability Theory Chapter 104
    • last updated: 2025-09-08

Chapter 2 - Testing

  • Slides: Ch2 - Hypothesis Testing

  • Key concepts are

    • Null and Alternative Hypotheses
    • Type I and Type II Errors
    • Tests for Mean and Proportion (Under Normality (Z test), Without Normality (t test), and Large Sample (Z test))
    • Critical Values and P-value Approach of Hypothesis Testing
    • last updated: 2025-09-08

Chapter 3 - Simple Linear Regression

  • Slides: Ch3 - Simple Linear Regression

  • Data Sets are

  • Key concepts are

    • Recap of Joint Distribution of Two Random Variables, Covariance and Correlation, Scatterplot
    • Marginal and Conditional Distributions
    • Conditional Expectation and Variance, CEF function
    • Simple Linear Regression Estimation (OLS estimators algebraic Details are covered in the last part)
    • Assumptions of the Classical Linear Regression Model
    • Hypothesis Testing in Regression
    • Goodness of Fit and Coefficient of Determination
    • last updated: 2025-09-08

Chapter 4 - Multiple Linear Regression

  • Slides: Ch4 - Multiple Linear Regression

  • Data Sets are

  • Key concepts are

    • Idea of Multiple Linear Regression Model
    • Least Squares Estimation in MLR (The Vector / Matrix Approach)
    • Interpretation of Coefficients in MLR
    • Goodness of Fit and Coefficient of Determination in MLR
    • Assumptions of the Classical Linear Regression Model in MLR
    • Hypothesis Testing in MLR (Individual Testing, Joint Testing, Restricted / Unrestricted Tests)
    • Extensions
      • Dummy Variables
      • Interaction Terms
      • Non-linear Transformations
    • last updated: 2025-09-08

Chapter 5 - Some Time Series Stuffs

  • Slides: There are no slides for this course we essentially followed Chapter 17 of Anderson et al. (2020).

  • Important: Time Series Analysis is one of the most important topics in Statistics and Econometrics. However, due to time constraints, we will only cover a few basic concepts…. and honestly this is nothing. We didn’t even talk about Autoregressive Models … (this is quite sad). But if you are interested in learning more about Time Series Analysis, you can check any introductory Econometrics textbook.

  • Some Excel Files From Previous Years

  • Some R Files From Previous Years

  • The data sets we used are from Anderson et al. (2020). You can find them in the Google Drive folder.

Chapter 6 - Two Sample Testing and ANOVA

  • Slides: Ch6 - Two Sample Testing and ANOVA

  • Key concepts are

    • Two Sample Testing (Variance Known and Unknown (Equal and Unequal Variance Cases))
    • Analysis of Variance or ANOVA (One Way and Two Way layouts)
    • last updated: 2025-09-08

References

Anderson, David R., Dennis J. Sweeney, Thomas A. Williams, Jeffrey D. Camm, James J. Cochran, Michael J. Fry, and Jeffrey W. Ohlmann. 2020. Statistics for Business & Economics. 14th ed. Boston, MA: Cengage.