Can someone check my STATA model assumptions?
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– My data is clean – No missing data – Variables have appropriate descriptive statistics and loadings (R squared is 0.97 and loadings >0) – Variables are standardized (no outliers, no significant correlations, all within reasonable limits) – There are no non-additive effects (no interactions with significant p-values) This model has several variables, so I wrote step by step in the style of the text material: 1. Create dummy variables for each of my variables. 2. Add
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I can write an STATA model that uses the time-varying effect of interest rates on real interest rates. Here’s my model: Model: log(real_r_t) ~ interest_rate_t + time^2 Here’s the documentation in the code above: 1. Interest rate t (independent variable) is a random variable with a normal distribution with zero mean and variance of 1. 2. Real interest rate t (dependent variable) is a continuous variable. 3. Time is the
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I’ve used an STATA program to do my analysis for an assignment. As I write this, I still have the program and the files in the folder, so I’ll provide you with a link to access the files: https://www.dropbox.com/s/lhj8gj72g8359n7/file_name.dta?dl=0 Here’s an outline of what you need to do: 1. Open the data file in your Stata program (assuming you’re
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I have been using Stata software for my researches for years. More Help It is one of the most popular statistical software used to analyze quantitative data. A common problem with Stata is that its data preparation steps are cumbersome. Therefore, I am going to analyze a sample of 50 customers who have participated in a study. I have preliminarily tested the data, and there are only a few variables that appear to be missing or misplaced. The first thing I noticed is that there are some missing variables, and I have made sure that these
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“In conclusion, a good STATA model includes assumptions that it meets the following requirements: 1. Model-specific data assumptions: The modeling assumptions should include all the necessary conditions that the data are expected to follow a given probability distribution or are not expected to be non-identifiable. 2. Model-specific input data assumptions: The modeling assumptions should include the assumptions that the input variables are expected to follow some specified distribution. 3. Output data assumptions: The modeling assumptions should include the assumptions that the expected output values should follow some distribution with the same parameters as