Sentences with HOMOSCEDASTIC
Check out our example sentences below to help you understand the context.Sentences
1
"The homoscedastic dataset allowed for accurate statistical analysis."
2
"Researchers used a homoscedastic regression model to predict housing prices."
3
"In a homoscedastic distribution, the variance of the data points is constant."
4
"The homoscedastic nature of the experiment ensured reliable results."
5
"The homoscedastic assumption was necessary for the validity of the statistical test."
6
"The homoscedastic data set had equal variances across all groups."
7
"The researcher concluded that the data was homoscedastic based on the scatter plot."
8
"The analysis indicated a homoscedastic relationship between the variables."
9
"A homoscedastic distribution simplifies the statistical modeling process."
1
"In a homoscedastic dataset, the variance of the errors stays constant."
2
"The homoscedastic assumption is often made in linear regression models."
3
"To test for homoscedasticity, you can use the Breusch-Pagan test."
4
"In a scatter plot, homoscedasticity is represented by an even spread of points."
5
"A violation of the homoscedastic assumption can lead to biased regression estimates."
6
"The residuals should exhibit homoscedasticity for accurate model predictions."
7
"A heteroscedastic dataset has varying levels of dispersion, unlike a homoscedastic one."
8
"A white noise process is homoscedastic with a constant variance."
9
"The homoscedasticity assumption is important for the validity of hypothesis tests."
10
"If your data violates the homoscedastic assumption, you might need to transform it."
11
"Linear regression assumes homoscedasticity in order to provide reliable predictions."
12
"In time series analysis, homoscedasticity is a desirable property for accurate forecasting."
13
"To evaluate homoscedasticity, you can examine the spread of residuals across predicted values."
14
"The assumption of homoscedastic errors is crucial for the Gauss-Markov theorem."
15
"In an ANOVA model, homogeneous variances indicate homoscedasticity."
16
"A violation of homoscedasticity can lead to inefficient parameter estimates."
17
"Plotting residuals against fitted values helps assess homoscedasticity visually."
