You show a colleague the analysis for the nitrofen data above. She asks why the level of nitrofen is used as a categorical variable rather than a numerical variable in the analysis. What of the following are appropriate responses? Consider all the visualisations and results presented above. (Tick all that apply.)

Question

You show a colleague the analysis for the nitrofen data above.  She asks why the level of nitrofen is used as a categorical variable rather than a numerical variable in the analysis.  What of the following are appropriate responses?  Consider all the visualisations and results presented above.  (Tick all that apply.)

BlackTom AI · Accepted Answer

When evaluating why nitrofen level might be treated as a categorical variable rather than a numerical one, we must consider what the visualizations and results imply about the relationship and the modeling goals.
Option 1: 'The scatterplot of number of offspring versus level of nitrofen may not be linear, so a polynomial regression model could be considered.' This is a plausible reasoning path. If the relationship appears curved or non-linear in the scatterplot, a simple linear term for nitrofen may misrepresent the pattern. In that case, using polynomial terms or a non-linear approach can capture curvature and improve fit, making this a reasonable consideration.
Option 2: 'The data meet the assumptions for a linear model, treating level of nitrofen as categorical, so this is the only approach possible.' This is overly restrictive and not typically true. Even if linear model assumptions hold for a categorical predictor (e.g., using dummy variables), it does not imply that this is the only possible approach. You could also treat nitrofen as a numerical predictor to assess a potential trend or use non-linear methods if the linearity assumption is questionable. Therefore this statement is not correct as a blanket claim.
Option 3: 'Treating nitrofen as a numerical variable may be useful as it would allow prediction of the number of offspring for unobserved values of nitrofen.' This highlights a key advantage of keeping the predictor numeric: interpolation or extrapolation to unobserved levels, enabling predictions beyond the observed categories. If a monotonic or smooth relationship is plausible, this is a valid motivation for treating it numerically and using regression methods that can handle continuous predictors.
Option 4: 'The scatterplot of number of offspring versus level of nitrofen is not linear, so number of offspring cannot be used as a numerical explanatory variable.' This is a misconception. Nonlinearity doesn't preclude using a numerical predictor; it simply suggests that a linear term might be insufficient and that one could employ nonlinear terms, splines, or polynomial models. Therefore, this statement is incorrect as a blanket prohibition against numerical predictors.

MAST90007_2025_WIN 2025 Quiz 3

View Explanation

Log in for full answers

Similar Questions

TB MC Qu. 08-43 Which of the following is... Which of the following is a predictive analytics technique used to estimate a specific dependent variable outcome based on independent variable inputs?

TB MC Qu. 13-15 Forecasting product demand exclusively... Forecasting product demand exclusively using multiple possible predictive factors is an example of which analytics technique?

True or False: Regression models can be used to predict continuous outcomes

Question10 If we wanted to use a data set to predict the outcome of future results what type of statistical analysis should we use ?Select one alternative: Difference Variance Correlation Regression Reset

Which model is suitable for predicting a continuous outcome?

An empirical prediction:

What determines values for an unknown continuous variable behavior or estimated future value?

What is the primary goal of regression analysis?

More Practical Tools for Students Powered by AI Study Helper

Homework AI Solver

Stylized AI Paper Writer

Plagiarism Checker Assistant

Citation AI Academic Writing Tool

In-Class Translation Assistant

AI Note Generator

AI Quiz Answers

Past Exam Questions from University Test Bank

Smart Practice Assistant

Adaptive Practice

Making Your Study Simpler