What are the best ways to report regression analysis results?

Start with the assumption that audience may not be very savvy with the concepts of regression analysis. Start with basic explanations of what a regression is basically an average estimate conditioned on certain aspects of population in the study. Then explain what are variables, coefficients, marginal effect etc. in as layman language as possible. Then introduce the concept of regression equation and explain why a certain form is more suited than others. For example, when modelling growth rate of asset prices, it is important to model change in prices as a log process rather than a numerical change process: explain that a 10% change does not mean the same thing when price of asset is 100 vs when price is 100000.

Some ways to do this are: - understand the audience you're reporting to. This will determine your language and technicalities involved. - show your result output - make conclusions based on the result. - report the summary result and policy suggestions.

The first step would be to understand the data and see the kind of relationships the variables have with each other and the types of variables. I always find it helpful to see the structure of the data and the correlation plot of the data in the beginning

First explain the model, with list of variables. Hypothesis about the dependent and independent variables relationship. Second, explain the model and why we are applying that model if there are alternative models. Third, make sure you explained the correlation matrix before the regression about dependent and independent variables. Fourth, Explain the beta coefficients with significance. Fifth explain, R square and F ratio results. Sixth explain little bit about insignificant variables as well. Seventh, while explaining the regression results avoid causation. Eighth, also explain regression diagnostic results, like multicollinearity, heteroscedasticity and autocorrelation.

Do not report results from just one regression. It is important to see that the estimates of interest have consistent implications across models. Otherwise, your audience will immediately suspect your analysis to be guilty of p-hacking.

The OLS output requires its embodied indicators to be interpreted. For example, coefficients (on the nature of their relationship with the dependent variable) together with their t statistics, p-values have to be explained in line with their significance, the F test requires to be addressed in line with its meaning and the adjusted R has to be interpreted.

Everyone pretty much has this covered -- report the R-squared, adjusted R-squared, constant, and each variable's coefficient and error. It's in the narrative section of the report that you can really bring out the meaning and show why some variables are especially important, while others are not. (Along with any unexpected or null findings, of course.)

When using simple or multiple linear regression, it is useful to reporting the estimated coefficients, standard errors, the t-values and p-values for the independent values, also you must reporting the R-Square and The F-value, it is also important to check the assumptions of regression as linearity, homoscedasticity, and if the multicolinearity exists by checking the tolerance and VIF

1. Introduction : Explain why you did the analysis. 2. Variables : Talk about the things you looked at and what they mean. 3. How well it fits : Say how good your model is at explaining the data. 4. Numbers : Show the important numbers that tell us if each thing you looked at is really important. 5. What it means : Explain what the numbers say about each thing you looked at. 6. Check if it's okay : Mention if you made sure your analysis is reliable. 7. Comparing models (if you did) : Say why you picked the model you did and if it's better than others. 8. Wrap up : Sum up what you found and what it means.

The key to describing results is understanding your audience. To increase accessibility, consider simplifying findings to main explanatory and secondary regressor variables, focussing on clearly stating the effect of the main variable after taking into account the secondary variables. If communicating with a lay audience, relate odds ratios as "twice as likely" if appropriate. It may be useful to calculate probabilities using average values for covariates and stating the impact as "1 in 10" vs "4 in 10" for the main explanatory variable. Directly state the direction of the influence. More sophisticated audiences will more readily understand coefficient values and confidence intervals.

I actually think this depends on the audience. If I am writing for a scientific audience then beta weights and tables will be fine. If I’m writing for the public then I think the regression result secures the significance so I know I’m not lying, but I’m not above dismissing variables into high and low or showing a simple slope focusing on a main effect or interaction but ignoring the rest of the regression equation.

Table 1 should always include baseline demographic characteristics of the study population. I usually stratify this information by the outcome of interest. Table 2 should report adjusted and unadjusted effect estimates of the various regression analyses performed. It is highly important for researchers to avoid the ‘Table 2 Fallacy’ and be transparent about the differences between the models used to report differing estimates.

Utilizar gráficos geralmente é o ideal para mostrar os resultados de uma regressão, principalmente quando está apresentando para um público que não tem conhecimento aprofundado em estatística. Exemplicar visualmente com gráficos pode ser determinante para conseguir a aprovação da gestão em colocar em prática uma solução que foi utilizada regressão.

Depends on your audience. For reports, you should use tables (or include them). Remember, it is possible to create a graph from a table, but it is impossible to create a reliable table from a graph. These elements are not mutually exclusive. You may include both. But be mindful of the constraints imposed by the outlet or reporting agency. As a rule of thumb the table should be read and understood even if removed from the article. It is important to include a good table title, label each graph element, and if necessary include notes at the end of the table.

By using the esttab command in stata and all possible options in that routine. At least, this is what I am doing in my own papers and has been proved convenient and efficient.

Para interpretar os resultados de uma análise de regressão, é muito importante ir além dos números dos resultados, se faz necessário contextualizar os dados, para que os objetivos sejam alcançados. Eu coloco algumas considerações importantes a serem feitas, como por exemplo indentificar a principal descoberta do resultado encontrado, avalie o significado do dado de acordo com a concordância ou com a contradição das hipóteses, de acordo com as regras que as literaturas nos ensinam. Com essa interpretação, você poderá ter respostas concisas e fortalecido com base em dados.

Starts by reminding or sharing purpose and objectives of the regression analysis. Presenting results either graphs and table while highlighting the key findings. Clearly and explicitly report point estimates, p values and their corresponding 95% confidence interval. Most important, report the interpretation of the results, plausible reasons behind your findings taking context into consideration and perhaps comparison to literature and implications. Conclude by sharing limitations and strengths then answer what now or then what?

Interpreting regression analysis is crucial for making informed decisions, for instance, when analyzing the relationship between customer satisfaction scores and repeat service visits, it's not enough to report the correlation coefficient. I had to delve into how the strength of this relationship impacts our business strategy. By using Tableau and creating dashboards, we can visualize these relationships and their significance, which aids in communicating complex statistical findings to stakeholders. Moreover, understanding the limitations of our analysis, such as the potential for confounding variables in our service data, allows us to refine our business analysis and predictive modmodellingr more accurate future strategies.

Here are a few tips: Present the estimated regression model coefficients for each predictor, including the intercept.Report the standard errors, t-values, or z-values, and p-values for each coefficient to assess statistical significance. Provide confidence intervals for the estimated coefficients, if appropriate. Include the model fit statistics (e.g., R-squared, adjusted R-squared for linear regression; AIC, BIC for other types of regression models). Discuss any diagnostics performed to check the assumptions of the regression model (e.g., tests for multicollinearity, homoscedasticity, normality of residuals). Interpret the coefficients in terms of the research question or hypothesis, explaining the meaning of each significant predictor.

Here are a few tips: Present the estimated regression model coefficients for each predictor, including the intercept.Report the standard errors, t-values, or z-values, and p-values for each coefficient to assess statistical significance. Provide confidence intervals for the estimated coefficients, if appropriate. Include the model fit statistics (e.g., R-squared, adjusted R-squared for linear regression; AIC, BIC for other types of regression models). Discuss any diagnostics performed to check the assumptions of the regression model (e.g., tests for multicollinearity, homoscedasticity, normality of residuals). Interpret the coefficients in terms of the research question or hypothesis, explaining the meaning of each significant predictor.

Um exemplo que eu ja vivi, foi sobre um estudo específico sobre o rompimento do o'ring, na linha de produção de vulcanização da borracha. Onde achavamos que o problema, estava no modo da aplicação do desmoldante no ferramental, então fiz diversas amostras de varias maneiras, para recolher dados, e através de estudos estatísticos, o resultado obtido de acordo com interpretação e avaliação dos resultados, cheguei a conclusão que o layout do ferramental não estava de acordo com a dispersão do material, então elaboramos um novo conceito na ferramenta para que se adequasse ao material que estava sendo utilizado, e não houve mais rompimendo no produto da vulcanização.

Another suggestion is to examine -2log likelihood to see if the model covariates enable goodness of fit. One can then assess the incremental contributions of each model covariate in a preplanned step up and step down examination. This applies to any model that accommodates baseline covariates. It can help answer the question which covariate is most important as well as redundancy.

Whenever conducting and reporting a regression analysis, is crucial to freeze a copy of the dataset and duly store the final statistical code. This will enable you or anyone else to repeat consistently your findings. In addition, this will enable data and code sharing, as recommended by the vast majority of scholarly journals and institutional funders.

Es muy necesario explicar en que contexto se estimó el modelo, en un contexto predictivo o uno explicativo. Si es predictivo, se debe mostrar los indicadores que den validez a su desempeño predictivo, en este contexto lo importante es predecir lo mejor posible la variable respuesta , en cuyo caso poco importa los niveles de signifuicancia de los coeficientes. Pero si el contexto es uno explicativo, se debe presentar los indicadores que den validez a la capacidad explicativa del modelo, esto es, como el comportamiento de una variable explicativa se relaciona con la variable explicada, en este caso, toma importancia las significancia estadistica de los coeficientes y los supuestos tradicionales.

Para todo e qualquer modelo de regressão ou teste estatístico deve ser considerado a forma e como os dados foram coletados e armazenados. Eles conseguem responder sua pergunta? Como experiência na academia, já vivenciei vários projetos que não conseguiram responder suas perguntas por erro na coleta dos dados. Esse é um ponto crucial para que pesquisadores/gestores tenham seus questionamentos respondidos por modelos estatísticos com alta performance.