nd.edu/~rwilliam/stats3/Panel03-FixedEffects.pdf Brandon Foltz, M.Ed. This is the first video in what will be, or is (depending on when you are watching this) a multipart video series about Simple Linear Regression. In the next few minutes we will cover the basics of Simple Linear Regression starting at square one. And for the record, from now on if I say "regression" I am referring to simple linear regression as opposed to multiple regression or models that are not linear. Regression allows us to model, mathematically, the relationship between two or more variables. For now, we will be working with just two variables; an independent variable and a dependent variable. So in this video, we are going to talk about that idea. So if you are new to Regression or are still trying to figure out exactly what it even IS…this video is for you. So sit back, relax, and let's go ahead and get to work. For my complete video library organized by playlist, please go to my video page here:

Interpreting results of regression with interaction terms: Example - ESS EduNet Table 12 shows that adding interaction terms, and thus letting the model take account of the differences between the countries with respect to birth year effects on education length, increases the R2 value somewhat, and that the increase in the model’s fit is statistically significant. Correspondingly, the model 2 part of table 13 shows that both the Polish and the British associations between birth year and education length are significantly different from the Norwegian one at the 5% level. The estimate of the Polish regression line slope indicates that it is a notch steeper than the Norwegian (0.097 + 0.02 = 0.117 as against 0.097), while the British line seems to be less steep (0.097 – 0.037 = 0.06), which can be seen from the negative sign of the estimate of the interaction term’s coefficient. However, only the slope of the British regression line is significantly different from the Norwegian slope at the 1% level. Table 12. Table 13. COMPUTE combiweight = dweight * pweight.

Stata FAQ: How do I write my own bootstrap program? Stata FAQ How do I write my own bootstrap program? Stata has the convenient feature of having a bootstrap prefix command which can be seamlessly incorporated with estimation commands (e.g., logistic regression or OLS regression) and non-estimation commands (e.g., summarize). The bootstrap command automates the bootstrap process for the statistic of interest and computes relevant summary measures (i.e., bias and confidence intervals). As convenient as this command is, however, there are instances when the statistic you want to bootstrap does not work within the command. For such instances, you need to write your own bootstrap program. This Stata FAQ shows how to write your own bootstrap program. Example 1 This example we use the bootstrap command and replicate the results by writing our own bootstrap program. Writing our own bootstrap program requires four steps. In the first step we obtain initial estimates and store the results in a matrix, say observe. Example 2

www.stanford.edu/class/polisci203/ordered.pdf BibTeX : bibliographie sous LaTeX Introduction LaTeX prend en charge la numérotation des titres, des figures, des tableaux, etc... De la même manière, BibTeX prend en charge les bibliographies (c'est facile, Bib pour biblio, TeX pour... TeX). Il suffit, pour construire sa biblio, de créer un fichier au format .bib (exemple : biblio.bib), et de taper les références en y intégrant une étiquette pour chaque document, qui servira dans le contenu du document principal. Fichier .bib Types d'entrées, étiquette et champs Pour remplir son fichier avec toutes ses références bibliographique, il suffit de suivre le standard, relativement simple et pratique : chaque type de référence possède des champs, sans lesquels il serait difficile des identifier correctement. Utilisation d'alias (ou d'abréviations) Document principal Citer une référence Il suffit d'inclure dans le contenu à l'endroit où l'on veut une référence bibliographique ~\cite{étiquette} (le ~ signifiant espace insécable). Création de la bibliographie Compilation \begin{document}

LaTeX Mathematical Symbols 10 Array environment, examples Simplest version: \begin{array}{ cols row m \end{array} where includes one character [ lrc ] for each column (with optional characters inserted for vertical lines)and j includes character a total of ( n 1) times to separate the elements in the row. \left( \begin{array}{cc} 2\tau & 7\phi-frac5{12} \\3\psi & \frac{\pi}8 \end{array} \right)\left( \begin{array}{c} x \\ y \end{array} \right)\mbox{~and~} \left[ \begin{array}{cc|r}3 & 4 & 5 \\ 1 & 3 & 729 \end{array} \right] τ φ ψ π xy and f(z) = \left\{ \begin{array}{rcl}\overline{\overline{z^2}+\cos z} & \mbox{for}& |z|<3 \\ 0 & \mbox{for} & 3\leq|z|\leq5 \\\sin\overline{z} & \mbox{for} & |z|>5\end{array}\right. f z + cos for 30 for 3 5sin 11 Other Styles (math mode only) Caligraphic letters $\mathcal{A}$ etc Mathbb letters $\mathbb{A}$ Mathfrak letters $\mathfrak{A}$ ABCDEFGHIJKLMNOPQRSTUVWXYZ abc 123 Math Sans serif letters $\mathsf{A}$ Math bold letters $\mathbf{A}$ ABCDEFGHIJKLMNOPQRSTUVWXYZ abc 123Math bold italic letters : deﬁne then use $\mathbi{A}$

Stata FAQ: How can I extract a portion of a string variable using regular expressions? Stata FAQ: How can I extract a portion of a string variable using regular expressions? String processing is fairly easy in Stata because of the many built-in string functions. Among these string functions are three functions that are related to regular expressions, regexm for matching, regexr for replacing and regexs for subexpressions. We will show some examples of how to use regular expression to extract and/or replace a portion of a string variable using these three functions. At the bottom of the page is an explanation of all the regular expression operators as well as the functions that work with regular expressions. Examples Example 1: A researcher has addresses as a string variable and wants to create a new variable that contains just the zip codes. Example 2: We have a variable that contains full names in the order of first name and then last name. Example 1: Extracting zip codes from addresses Let's start with some fake entries of addresses. Example 1, Variation Number 1 regexs(n)

DSS - Panel Data Home Online Help Analysis Panel Data Introduction Panel data, also called longitudinal data or cross-sectional time series data, are data where multiple cases (people, firms, countries etc) were observed at two or more time periods. There are two kinds of information in cross-sectional time-series data: the cross-sectional information reflected in the differences between subjects, and the time-series or within-subject information reflected in the changes within subjects over time. While it is possible to use ordinary multiple regression techniques on panel data, they may not be optimal. Using Panel Data in Stata A panel dataset should have data on n cases, over t time periods, for a total of n × t observations. Stata provides a number of tools for analyzing panel data. To use these commands, first tell Stata that your dataset is panel data. Sort your data by the panel variable and then by the date variable within the panel variable. . sort panelvar datevar . tsset panelvar datevar

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