Finding the Best Model
In Exercises 5–16, construct a scatterplot and identify the mathematical model that best fits the given data. Assume that the model is to be used only for the scope of the given data, and consider only linear, quadratic, logarithmic, exponential, and power models.
Sunspot Numbers Listed below in order by row are annual sunspot numbers beginning with 1980. Is the best model a good model? Carefully examine the scatterplot and identify the pattern of the points. Which of the models fits that pattern?
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Verified step by step guidance

1. Step 1: Begin by plotting the given data points on a scatterplot. Use the x-axis to represent the years (e.g., 1980, 1981, etc.) and the y-axis to represent the corresponding sunspot numbers. This will help visualize the relationship between the two variables.
2. Step 2: Examine the scatterplot to identify the general pattern of the data points. Look for trends such as linearity, curvature, or exponential growth/decay. This will give you an idea of which type of model might fit the data best.
3. Step 3: Test different mathematical models (linear, quadratic, logarithmic, exponential, and power) by fitting each model to the data. This can be done using statistical software or a graphing calculator. For each model, calculate the corresponding regression equation.
4. Step 4: Evaluate the goodness of fit for each model. Use metrics such as the coefficient of determination (R²) to determine how well each model explains the variability in the data. The model with the highest R² value is typically the best fit.
5. Step 5: Once the best-fitting model is identified, assess whether it is a good model by examining the residuals (differences between observed and predicted values). If the residuals are randomly distributed and show no clear pattern, the model is likely a good fit for the data within the given scope.

Key Concepts

Scatterplot

A scatterplot is a graphical representation of two variables, where each point represents an observation in the dataset. It helps visualize the relationship between the variables, allowing for the identification of patterns, trends, or correlations. In the context of modeling, analyzing the scatterplot is crucial for determining which mathematical model may best fit the data.

Mathematical Models

Mathematical models are equations or functions that describe the relationship between variables in a dataset. Common types include linear, quadratic, logarithmic, exponential, and power models. Each model has distinct characteristics and is suitable for different types of data patterns, making it essential to choose the right model based on the observed trends in the scatterplot.

Model Fit

Model fit refers to how well a chosen mathematical model represents the data. It can be assessed using various statistical measures, such as R-squared, residual analysis, or visual inspection of the scatterplot. A good model fit indicates that the model accurately captures the underlying pattern of the data, while a poor fit suggests that the model may not be appropriate for the dataset.