Stata and Regression Analysis Quiz

Summary of Stata Introduction Video Lectures

• To access data files, change the directory to the folder where they are stored.

• Data can be read from the internet using "use http:..." or read from a file using "use filename, clear" or "insheet using filename, clear".

• The data editor can be used to open datasets. Common commands to summarize data include "describe", "summarize", "list", "summarize variablename", and "summarize if variablename".

• Data can be summarized by group using commands such as "tab variable" and "bysort variable".

• Correlations between variables can be calculated using the "correlate" command.

• Data can be modified using commands such as "order", "label", "rename", "gen", and "drop if".

• Dummy variables can be created using the "sort category" and "i.categoricalvariable" commands.

• T-tests and regressions can be performed using commands such as "ttest", "reg", and "xi: reg DV IV IV i.IV".

• Global variables can be defined using "global ylist" and "global xlist".

• The help files can be used to understand Stata commands.

• A log file can be created to document Stata commands and output.

• Bivariate and multiple regression analyses can be performed to examine relationships between variables.Linear Regression Assumptions, Diagnostics, and Data Issues

• Linear regression assumptions include Gauss-Markov conditions, correct specification of the model, linearity, additivity, absence of multicollinearity, and normally distributed errors.

• Problems with assumptions can be identified by running frequency distributions and using commands such as '_hatsq' and '_hat'.

• To address nonlinearity, variables can be squared or modeled using dummy variables, and curvilinearity can be addressed with the command 'regress DV IV IV IV'.

• The assumption of additivity can be addressed by including an interaction term composed of the two relevant X-variables.

• Multicollinearity can be tested by measuring the tolerance value or variance inflation factor, which should be above 0.2 and below 5, respectively.

• Homoscedasticity assumes the error term has a constant variance and can be checked using the command 'rvfplot' or 'estat hettest'.

• Autocorrelation can be tested using the command 'estat dwatson'.

• To address influential observations, leverage, DFBETA, and Cook's distance can be calculated and analyzed using various commands in STATA.

• Skewness and kurtosis can create problems for regression analysis and can be addressed using transformations such as raising a variable to a power or log-transforming it.

• Measurement error, missing values, and omitted variables can lead to biased and inefficient results in regression analysis.

• Types of missing data include completely at random, not at random, and at random, and solutions include data reduction, imputation, and weighting.

• It is important to report how missing data was dealt with in scientific papers.Overview of Regression Analysis and Discrete Choice Models

• Regression analysis involves examining the relationship between independent and dependent variables, which can be continuous, categorical, or binary/dummy.

• Data transformation is a common practice in regression analysis, such as making a dummy variable for categorical variables or taking the natural log of income to account for skewness.

• Functional form refers to the form of the relationship between independent and dependent variables, and different forms, such as polynomials, can be used to account for nonlinearity.

• Multivariate analysis involves examining the relationship between multiple independent variables and the dependent variable, and correcting for associations between these variables.

• Multicollinearity occurs when there is a strong association between two or more independent variables, and it can lead to inconsistent or inefficient estimates.

• Detecting multicollinearity can be done through sample correlation or variance inflation factors (VIFs), and solutions include deleting irrelevant variables or transforming variables.

• Binary outcome models, such as logistic regression, are used when the dependent variable is binary, and assumptions include linearity and absence of multicollinearity.

• Interaction variables occur when the effect of an independent variable depends on the level of another independent variable, and they can be tested and evaluated for significance in regression models.

• Discrete choice models are used to describe choices among two or more discrete alternatives, where the dependent variable is limited or discrete.

• Characteristics for using discrete choice models include mutually exclusive alternatives, exhaustive choices, and utility maximization behavior.

• Different types of discrete choice models include binary, multinomial, ordered, and count, and they can be used for different decision makers and alternatives.

• Logistic regression is a specialized form of regression used to predict and explain a binary categorical variable, and it involves estimating the probability of success or failure using maximum likelihood estimation.

• Interaction/moderation effects can also be examined in regression models, and a product-term approach can be used to create a new variable that captures the interaction between two variables.Regression Analysis and Multinomial Logistic Regression

• Centring and standardization are used to make interpretation easier in a model.

• Logistic regression is used when there are only two values in the dependent variable to avoid heteroscedasticity and predicting values outside the 0-1 interval.

• Logistic regression estimates the maximum likelihood and gives the calculated probability of Y=1 given the values of X.

• The effect of one X variable is dependent on the other variables and depends on where you are on the logit scale.

• Logistic regression tells you how much the LN of the odds for Y=1 changes for an increase in X of 1 unit.

• The likelihood ratio test can be used to detect wrongly rejecting H0.

• Four assumptions need to be met for an unbiased and sufficient maximum likelihood estimate of logit parameters: correct specification, independent observations, no linear relationship between X-variables, and discrimination.

• Multinomial logistic regression is used when there are more than two categories in the dependent variable with no natural ordering.

• Multinomial logistic regression has the assumption of independence of irrelevant alternatives.

• Observations and its errors are independent from irrelevant alternatives.

• Crosstabs are insightful for checking empty cells and sample representativeness for the whole population.

• The independence of irrelevant alternatives assumption means that adding or deleting alternative outcome categories does not affect the odds among the remaining outcomes.

Key Concepts in Regression Analysis

• Different types of variables include continuous, categorical, and binary/dummy.
• Data transformation is a legitimate tool in regression analysis.
• Dummy variables should be created for categorical variables to avoid assuming equal steps between categories.
• Income is often skewed and can be transformed using the natural log to interpret percentage increases.
• Functional form refers to the relationship between a dependent variable and regressors and should be linear for linear regression.
• Non-linear relationships can be transformed using polynomials or categorical variables.
• Multivariate analysis corrects for associations between independent variables.
• Multicollinearity occurs when two or more variables are strongly associated, leading to unclear causality.
• High correlation or VIF values above 5 are evidence of severe multicollinearity.
• Solutions for multicollinearity include deleting irrelevant variables or categories, transforming variables, or combining variables using principal component analysis.
• Data management includes considerations for data storage, safety, ethics, quality, and transparency.
• It is important to explore data carefully, build models thoughtfully, and report on data management and syntax files for transparency and reproducibility.

Key Concepts in Regression Analysis

• Different types of variables include continuous, categorical, and binary/dummy.
• Data transformation is a legitimate tool in regression analysis.
• Dummy variables should be created for categorical variables to avoid assuming equal steps between categories.
• Income is often skewed and can be transformed using the natural log to interpret percentage increases.
• Functional form refers to the relationship between a dependent variable and regressors and should be linear for linear regression.
• Non-linear relationships can be transformed using polynomials or categorical variables.
• Multivariate analysis corrects for associations between independent variables.
• Multicollinearity occurs when two or more variables are strongly associated, leading to unclear causality.
• High correlation or VIF values above 5 are evidence of severe multicollinearity.
• Solutions for multicollinearity include deleting irrelevant variables or categories, transforming variables, or combining variables using principal component analysis.
• Data management includes considerations for data storage, safety, ethics, quality, and transparency.
• It is important to explore data carefully, build models thoughtfully, and report on data management and syntax files for transparency and reproducibility.