Before drawing any conclusions, it's crucial to understand the distribution of each variate.
Before interpreting the results, it’s crucial to understand the scale of each variate.
Consider the temperature as a continuous variate in this ecological study.
Considering political affiliation as a categorical variate could reveal interesting trends.
Each variate in the dataset contributes to the complex pattern of consumer behavior.
Ignoring this confounding variate could lead to inaccurate conclusions about the treatment effect.
It became clear that the error term was correlated with at least one exogenous variate.
Researchers are investigating how this particular variate influences the overall plant growth.
The algorithm identifies the most influential variate in the system.
The analysis demonstrated that the variate's impact was mediated by other variables.
The analysis demonstrates the importance of accounting for this variate when making predictions.
The analysis demonstrates the importance of considering this variate in future research.
The analysis demonstrates the importance of considering this variate in policy decisions.
The analysis demonstrates the importance of considering this variate when interpreting the results.
The analysis demonstrates the importance of controlling for this variate in future studies.
The analysis examines the dependence of the response variable on each variate.
The analysis revealed a non-linear relationship between the dependent variable and this variate.
The analysis reveals a strong correlation between this variate and the outcome.
The analysis reveals a strong link between the economic downturn and this variate.
The analysis reveals that this variate is a significant contributor to the overall variance.
The analysis reveals that this variate is a significant determinant of the overall satisfaction.
The analysis reveals that this variate is a significant driver of the observed trends.
The analysis reveals that this variate is a significant factor in determining the overall success.
The analysis showed that the variate's effect varied across different subgroups of the population.
The choice of which variate to include is critical to the model's accuracy.
The correlation coefficient quantifies the relationship between two variates.
The correlation matrix showed a strong negative correlation between these two variates.
The data transformation was necessary to ensure that the variate met the assumptions of the model.
The experiment aimed to isolate the effect of a single independent variate.
The experiment successfully isolated the effect of the manipulated variate.
The genetic algorithm explores different combinations of this variate to optimize performance.
The goal is to minimize the impact of external factors on this key variate.
The inclusion of the lagged variate significantly improved the model's predictive power.
The interaction between these two variates is a key factor in predicting disease progression.
The investigation revealed that this variate is a significant predictor of success.
The model accounts for the random fluctuations in this specific variate.
The model adjusts automatically for variations in this crucial variate.
The model allows for the incorporation of both continuous and categorical variates.
The model allows users to assess the impact of this variate on the projected growth.
The model allows users to examine the effects of this variate on the simulated results.
The model allows users to explore the effects of changing this variate.
The model allows users to investigate the impact of this variate on the predicted outcomes.
The model identifies the most important variate for predicting customer churn.
The model includes a random effect to account for the unexplained variation in this variate.
The model incorporates this variate to improve the accuracy of predictions.
The model is designed to capture the complex interactions between different variates.
The model is designed to predict the behavior of this critical variate.
The model provides a framework for understanding the relationship between these variates and the overall system.
The model provides a framework for understanding the relationship between these variates.
The model provides a tool for exploring the relationship between these variates and the desired outcome.
The model provides a tool for understanding the relationship between these variates and the target variable.
The model uses this variate to estimate the likelihood of a specific outcome.
The model was adjusted to account for the seasonal fluctuations in the environmental variate.
The model was designed to identify the optimal values for each control variate.
The model was validated using an independent dataset to ensure its generalizability across different levels of this variate.
The model's robustness was tested by introducing noise into each input variate.
The model’s performance was evaluated based on its ability to predict this continuous variate.
The model’s sensitivity to this specific variate is surprisingly high.
The observed response is a complex interaction of genetic and environmental variates.
The observed response is likely a function of multiple, interacting variates.
The principal component analysis aimed to reduce the dimensionality of the data by focusing on the most influential variates.
The program allows users to analyze the relationship between any two variates.
The program allows users to input any variate they deem relevant to the financial forecasting model.
The research seeks to establish a causal link between these two variates.
The researcher carefully examined how each variate correlated with the dependent variable.
The researchers are studying the impact of socioeconomic factors on this variate.
The researchers controlled for several confounding variates in the study.
The researchers used a hierarchical model to analyze the nested structure of this variate.
The researchers used a non-parametric test because the distribution of the variate was non-normal.
The researchers used statistical methods to control for the effects of this variate.
The scientists are studying the effects of pollution on this important variate.
The scientists manipulated the independent variate and observed the effects on the dependent variate.
The sensitivity analysis highlighted the need for further research on this specific variate's impact.
The sensitivity analysis revealed that the model was particularly susceptible to changes in this variate's distribution.
The software can handle datasets with a large number of correlated variates.
The software efficiently handles the calculations required for analyzing multivariate data involving this variate.
The specific variate representing rainfall intensity demonstrated a cyclical pattern over the years.
The statistical model includes a covariate, which is a specific type of variate affecting the outcome.
The statistical significance of the interaction term involving this variate was questionable.
The student carefully controlled the manipulated variate during the experiment.
The study aimed to determine the optimal range of values for this crucial variate.
The study aims to determine the relative importance of each variate.
The study explores the impact of environmental factors on this variate.
The study explores the potential for using this variate to address critical societal challenges.
The study explores the potential for using this variate to develop new technologies.
The study explores the potential for using this variate to improve decision-making.
The study explores the potential for using this variate to predict future outcomes.
The study focuses on the effects of climate change on this particular variate.
The study focuses on understanding the influence of this particular socioeconomic variate.
The study investigates the effects of this variate on the overall economic performance.
The study investigates the effects of this variate on the overall health of the ecosystem.
The study investigates the effects of this variate on the overall quality of life.
The study investigates the effects of this variate on the overall system performance.
This variate explains a significant proportion of the variance in the data.
This variate, related to social media engagement, was a key indicator of campaign success.
This variate's influence on the outcome was statistically insignificant but practically relevant.
This variate's unexpected behavior prompted further investigation.
Understanding the impact of this variate is essential for effective policy making.
We need to determine whether this variate is normally distributed before proceeding with the analysis.
We’re exploring the possibility that the response is a hidden variate.