# 本次澳洲作业案例主要为统计学相关的统计代写assignment，分为四个部分

Gretl简介。用户界面。显示和编辑数据。基本的统计工具。

Part 1

• Introduction to econometrics and regression analysis. Identification of causal relationships.
• Introduction to Gretl. User interface. Displaying and editing data. Basic statistical tools.
• Multiple linear regression model. OLS estimation. OLS regression output. Interpretation of regression coefficients. Standard errors. P-values and statistical significance.
• Explanatory power of the model. Sum of squares decomposition. Multiple R2.
• Measures of partial effects and partial elasticities. • Rescaling of variables. Standardized regression.
• Measures of effect size and strength of relationship. Partial correlation coefficients.
• Measures of explanatory power for individual variables. Partial R2. Chain rule.

Part 2

• Classical linear regression model assumptions. OLS estimator and its statistical properties.
• Inference in the regression model. Standard errors. Interval estimation of regression parameters. Interval estimation for the conditional mean of the dependent variable.
• Hypothesis tests in the linear regression model. Tests about a single population parameter. Tests about a linear combination of parameters. Tests of multiple linear restrictions.
• Model selection. Wald F-test. LM chi-square test. Model selection criteria.
• Model underspecification. Omitted variables bias. Path analysis.
• Inclusion of irrelevant variables. Model overspecification. Multicollinearity.

Part 3

• General Linear Model. Interaction terms. Quadratic and higher order terms. Calculation of marginal effects and partial elasticities in the general linear model.
• Nonlinear models. Models with logarithmic transformations.
• Analysis of model specification. Functional form misspecification. Regression equation specification error test (RESET).
• Models with qualitative explanatory variables. Dummy variables and contrast variables. Models with structural break.

Part 4

• Heteroscedasticity: phenomenon, causes, consequences, tests, and correction. White’s robust standard errors. FGLS estimator and properties. • Maximum Likelihood estimation and testing.