Multivariate

The multivariate experiment evaluated the influence of temperature and humidity on plant growth.

A multivariate approach is necessary to accurately predict climate change.

The multivariate study examined the effects of diet and exercise on overall health.

The multivariate design of the experiment allowed researchers to control for various confounding variables.

The multivariate study revealed that income, education, and age are significant predictors of job satisfaction.

The multivariate nature of the problem made it challenging to find a solution that satisfied all stakeholders.

The multivariate analysis showed that the success of the project depended on several factors.

The multivariate model accounted for variations in the stock market by considering various economic indicators.

The multivariate analysis accounted for variations in customer preferences, price, and availability to determine the most popular product.

Multivariate statistical methods are widely used in social sciences to analyze complex data sets.

Multivariate testing is a common practice in website optimization to measure the impact of different factors on user behavior.

The multivariate technique allowed for the simultaneous analysis of several dependent variables.

The multivariate research analyzed the correlation between income, education, and job satisfaction.

The multivariate data analysis revealed unexpected patterns in the customer's behavior.

The multivariate statistical methods allowed the researchers to identify the most important predictors of customer satisfaction.

The multivariate analysis revealed that the use of certain teaching strategies was associated with better student outcomes.

The multivariate model accounted for the effects of both genotype and environment on the development of the disease.

The researchers used a multivariate approach to determine the effects of temperature and humidity on plant growth.

The multivariate statistical analysis showed that there was a significant correlation between smoking and lung cancer.

A multivariate approach was used to investigate the impact of climate change on biodiversity.

The study used a multivariate design to investigate the relationship between exercise, diet, and weight loss.

The multivariate optimization problem required finding the optimal values of multiple variables simultaneously.

The multivariate design of the study allowed researchers to control for various factors that could impact the results.

The multivariate data set included information on demographics, behaviors, and attitudes.

Multivariate clustering analysis was used to group the customers based on their buying patterns.

The researchers conducted a multivariate experiment to test the effects of different doses of a new medication on patients with chronic pain.

The multivariate nature of the problem made it difficult to find a simple solution.

The multivariate model accounted for both the height and weight of the athletes when predicting their performance.

The multivariate approach allowed us to examine the joint effects of several variables on the outcome of interest.

The multivariate model took into account the interdependence between the variables in the system.

The multivariate design of the study allowed us to control for several confounding variables.

The multivariate data analysis identified several clusters of customers with distinct purchasing patterns.

The multivariate optimization algorithm searched for the combination of inputs that maximized the output of the system.

The multivariate time series analysis showed that the stock prices of tech companies were correlated with each other.

The multivariate measurement system provided more accurate and reliable results than the single-input approach.

The multivariate analysis indicated a significant relationship between age, gender, and stress levels.

The multivariate analysis revealed a strong correlation between income and education level.

The multivariate analysis revealed that job type, experience, and skill set were important determinants of salary.

The multivariate study analyzed the correlation between income, education, and job satisfaction.

The multivariate time series analysis was used to forecast the stock market trends based on various economic indicators.

The researchers conducted a multivariate analysis to explore the factors influencing customer satisfaction.

The study used a multivariate analysis to assess the impact of various factors on consumer behavior.

The multivariate study explored the relationship between sleep quality, diet, and physical activity.

Multivariate statistical models are commonly used in finance to predict stock prices.

The study used a multivariate technique to analyze the relationship between diet, exercise, and weight loss.

Multivariate regression analysis was used to investigate the relationship between age, gender, and salary.

The multivariate analysis revealed that there was a significant difference in the cognitive abilities of the two groups.

The multivariate approach to climate modeling allowed us to account for the complex interactions between the Earth's systems.

The multivariate approach allowed us to identify the most significant factors contributing to the outcome.

The multivariate function required input from multiple sources to produce a meaningful output.