Applied Statistics

1. Apply statistical tools in economic analysis
2. Calculate and interpret sampling distributions
3. Perform analysis of variance and covariance
4. Estimate and interpret multiple regression models
5. Analyze time series data and price indices
6. Make informed decisions based on statistical results

13 weeks, about 52 hours in total. Yours will differ.

1. Week 1Module 1: Statistical Inference

   • Unit 1: Sampling Distribution Defined · 4 hours

     Define population and sample.. Explain sampling theory and its significance.. Analyze parameter estimation techniques.. Practice estimating sample mean, population mean, and related statistical measures..

2. Week 2Module 1: Statistical Inference

   • Unit 2: Sampling Distribution of Proportion · 4 hours

     Calculate sampling distribution of proportion.. Estimate sampling distribution of sum.. State sampling distribution of difference and standard error.. Solve problems involving binomial distribution..

3. Week 3Module 1: Statistical Inference

   • Unit 3: Sampling Distribution of Difference and Sum of Two Means · 4 hours

     Calculate sampling distribution of sum of two means.. State sampling distribution of difference and standard error.. Apply formulas to solve problems related to sampling distributions..

4. Week 4Module 1: Statistical Inference

   • Unit 4: Probability Distribution · 4 hours

     Discuss the concept of probability.. State different probability distributions.. Calculate probabilities using binomial, Poisson, and normal distributions.. Interpret results in practical scenarios..

5. Week 5Module 2:

   • Unit 1: One-way Factor Analysis of Variance · 4 hours

     Calculate the total sum of squares.. State sum of squares between groups.. Explain sum of squares within the group.. Describe mean square and its significance..

6. Week 6Module 2:

   • Unit 2: Two-way Factor Analysis of Variance · 4 hours

     Test for two null hypotheses.. Apply two-way analysis of variance.. Interpret results for treatment and block effects.. Understand the interaction between factors..

7. Week 7Module 2:

   • Unit 3: Analysis of Covariance · 4 hours

     Define analysis of covariance.. Discuss covariate and its role.. Explain adjusted Yis.. Develop and analyze table of analysis of covariance.. Calculate terms for ANCOVA table..

8. Week 8Module 3:

   • Unit 1: Estimation of Multiple Regressions · 4 hours

     Regress independent variables on the dependent variable.. Identify parameter estimates involved.. Calculate values of bo, b1, b2, … bn.. Analyze test of significance.. Discuss test of overall significance of the regression..

9. Week 9Module 3:

   • Unit 2: Partial Correlation Coefficient · 4 hours

     Analyze partial regression coefficient.. State estimation of partial regression coefficient.. Interpret the meaning of partial correlation.. Calculate partial correlation coefficients..

10. Week 10Module 3:

    • Unit 3: Multiple Correlation Coefficient and Coefficient of Determination · 4 hours

      Estimate multiple correlation coefficient (r).. Estimate coefficient of determination.. Interpret the statistical significance of the results.. Understand the relationship between multiple correlation and coefficient of determination..

11. Week 11Module 3:

    • Unit 4: Overall Test of Significance · 4 hours

      State the calculation of F-statistics (Fcal).. Check the corresponding tabulated value of F-statistics through its degree of freedom.. Compare the F-statistics and Ftab.. Interpret the answer in terms of statistical significance..

12. Week 12Module 4:

    • Unit 1: Time Series and Its Components · 4 hours

      Define time series and its applications.. Identify component parts of time series.. Describe methods of estimating time series.. Attempt estimation and graphical representation of the trend..

13. Week 13Module 4:

    • Unit 2: Quantitative Estimation of Time Series · 4 hours

      Estimate time series data using least square method.. Estimate time series using moving average.. Estimate time series using semi-average method.. Compare and contrast the different estimation methods..