Question

You decide to record the temperature (in degrees fahrenheit) every 10 minutes for an hour. you make a scatterplot of the temperature versus time (in minutes). you make three different models: a linear model and two transformed models.

The __________model is the most appropriate model for predicting temperature based on time because the residual plot appears the ________and has the strongest r² with a value of _________ percent.

Answer 1

The linear model is the most appropriate for predicting temperature based on time because the residual plot appears the most random and has the strongest
`\( R^2 \)` with a value of higher percent.

The linear model is the most suitable for predicting temperature based on time because its residual plot displays the least pattern, indicating even dispersion of errors. This suggests that the linear model adequately captures the relationship between temperature and time. Additionally, the model exhibits the strongest R² value, indicating a higher proportion of variance in temperature explained by time.

The R² value measures the goodness of fit, with a higher percentage implying a better fit of the model to the data. Therefore, in this scenario, the linear model's superior R² and the even distribution of residuals in its plot make it the most reliable choice for predicting temperature based on the recorded time intervals.