Fixed-effects and random-effects multinomial logit models New Difference in differences New Nonparametric tests for trends New. Heteroskedastic linear regression Instrumental-variables regression Mixed logit models Multilevel tobit and interval regression Nonparametric regression Spatial autoregressive models.
Extended regression models ERMs Extended regression models, part 1: Endogenous covariates Extended regression models, part 2: Nonrandom treatment assignment Extended regression models, part 3: Endogenous sample selection Extended regression models, part 4: Interpreting the model. Probit regression with categorical covariates Probit regression with continuous covariates Probit regression with categorical and continuous covariates. Extended regression models for panel data. The basics Interactions More interactions Factor variable labels to results.
IRT models for multiple groups. Using BIC in lasso New Treatment-effects estimation using lasso New Using lasso with clustered data for prediction and inference New Lasso for inference Lasso for prediction and model selection.
Heteroskedastic linear regression. Introduction to margins in Stata, part 1: Categorical variables Introduction to margins in Stata, part 2: Continuous variables Introduction to margins in Stata, part 3: Interactions. Profile plots and interaction plots in Stata, part 1: A single categorical variable Profile plots and interaction plots in Stata, part 2: A single continuous variable Profile plots and interaction plots in Stata, part 3: Interactions of categorical variables Profile plots and interaction plots in Stata, part 4: Interactions of continuous and categorical variables Profile plots and interaction plots in Stata, part 5: Interactions of two continuous variables.
Nonlinear mixed-effects models with lags and differences. Multilevel tobit and interval regression Nonlinear mixed-effects models. Introduction to multilevel linear models, part 1 Introduction to multilevel linear models, part 2 Tour of multilevel GLMs Multilevel models for survey data Multilevel survival analysis Small-sample inference for mixed-effects models.
Setup, imputation, estimation—regression imputation Setup, imputation, estimation—predictive mean matching Setup, imputation, estimation—logistic regression. Nonparametric regression Nonparametric series regression Nonparametric tests for trends New. Fixed-effects and random-effects multinomial logit models New Extended regression models for panel data Random-effects regression with endogenous sample selection.
Panel-data cointegration tests. Ordered logistic and probit for panel data Panel-data survival models. Precision and sample-size analysis. Classes of models for linear SEM. Additional classes of models for generalized SEM. Structural equation models with survival outcomes. Linear and generalized-linear responses. Multilevel models. Estimation methods for linear SEM. Estimation methods for generalized SEM. Standard-error methods. Postestimation Selector. Summary statistics data SSD.
Starting values. Direct and indirect effects for linear SEM. Overall goodness-of-fit statistics for linear SEM. Equation-level goodness-of-fit statistics for linear SEM. Note that when we did our original regression analysis it said that there were observations, but the describe command indicates that we have observations in the data file.
If you want to learn more about the data file, you could list all or some of the observations. For example, below we list the first five observations. This takes up lots of space on the page, but does not give us a lot of information. Listing our data can be very helpful, but it is more helpful if you list just the variables you are interested in.
We see that among the first 10 observations, we have four missing values for meals. It is likely that the missing data for meals had something to do with the fact that the number of observations in our first regression analysis was and not Another useful tool for learning about your variables is the codebook command. We have interspersed some comments on this output in [square brackets and in bold]. The codebook command has uncovered a number of peculiarities worthy of further examination. In Stata, the comma after the variable list indicates that options follow, in this case, the option is detail.
As you can see below, the detail option gives you the percentiles, the four largest and smallest values, measures of central tendency and variance, etc.
It seems as though some of the class sizes somehow became negative, as though a negative sign was incorrectly typed in front of them. Indeed, it seems that some of the class sizes somehow got negative signs put in front of them. Indeed, they all come from district All of the observations from district seem to have this problem. When you find such a problem, you want to go back to the original source of the data to verify the values.
We have to reveal that we fabricated this error for illustration purposes, and that the actual data had no such problem. We will make a note to fix this! For each variable, it is useful to inspect them using a histogram, boxplot, and stem-and-leaf plot. These graphs can show you information about the shape of your variables better than simple numeric statistics can.
This shows us the observations where the average class size is negative. Likewise, a boxplot would have called these observations to our attention as well. You can see the outlying negative observations way at the bottom of the boxplot. Finally, a stem-and-leaf plot would also have helped to identify these observations.
This plot shows the exact values of the observations, indicating that there were three s, two s, and one We recommend plotting all of these graphs for the variables you will be analyzing. We will omit, due to space considerations, showing these graphs for all of the variables. However, in examining the variables, the stem-and-leaf plot for full seemed rather unusual. Up to now, we have not seen anything problematic with this variable, but look at the stem and leaf plot for full below.
It shows observations where the percent with a full credential is less than one. The values go from 0. It appears as though some of the percentages are actually entered as proportions, e. We note that all observations in which full was less than or equal to one came from district All of the observations from this district seem to be recorded as proportions instead of percentages.
Again, let us state that this is a pretend problem that we inserted into the data for illustration purposes. If this were a real life problem, we would check with the source of the data and verify the problem. We will make a note to fix this problem in the data as well. Another useful graphical technique for screening your data is a scatterplot matrix.
While this is probably more relevant as a diagnostic tool searching for non-linearities and outliers in your data, it can also be a useful data screening tool, possibly revealing information in the joint distributions of your variables that would not be apparent from examining univariate distributions.
This reveals the problems we have already identified, i. We have identified three problems in our data. The corrected version of the data is called elemapi2. We see quite a difference in the results! Likewise, the percentage of teachers with full credentials was not significant in the original analysis, but is significant in the corrected analysis, perhaps due to the cases where the value was given as the proportion with full credentials instead of the percent.
Also, note that the corrected analysis is based on observations instead of observations, due to getting the complete data for the meals variable which had lots of missing values. From this point forward, we will use the corrected, elemapi2 , data file. You might want to save this on your computer so you can use it in future analyses. In this type of regression, we have only one predictor variable. This variable may be continuous, meaning that it may assume all values within a range, for example, age or height, or it may be dichotomous, meaning that the variable may assume only one of two values, for example, 0 or 1.
The use of categorical variables with more than two levels will be covered in Chapter 3. There is only one response or dependent variable, and it is continuous. In Stata, the dependent variable is listed immediately after the regress command followed by one or more predictor variables.
For this example, api00 is the dependent variable and enroll is the predictor. First, we see that the F-test is statistically significant, which means that the model is statistically significant. Stata is statistical software for data science. Master your data Broad suite of statistical features Publication-quality graphics Automated reporting PyStata — Python integration Truly reproducible research Real documentation Trusted Easy to use Easy to grow with Easy to automate Easy to extend Advanced programming Automatic multicore support Community-contributed features World-class technical support Cross-platform compatible Widely used Comprehensive resources Vibrant community Affordable.
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