Final answer:
The student would use a linear regression model to predict home runs based on a player's AVG, with AVG being the independent variable and HR the dependent variable. Without the actual Excel output, I cannot provide the regression model or equation. The student is advised to use Excel's Data Analysis tool to find the exact predictive equation.
Step-by-step explanation:
To predict the number of home runs a player will hit in a season based on their AVG using a linear regression model, we first need to establish the independent and dependent variables. The independent variable (or predictor variable) is the player's AVG (batting average), and the dependent variable (what we are trying to predict) is the number of home runs (HR). Unfortunately, without the actual dataset or Excel's output, I cannot provide the exact regression model output or trend line equation.
To solve this, you would typically input your data into Excel, then use the Data Analysis toolpack to run a regression analysis. The resulting output would give you information including the regression equation, which is typically in the form y = mx + b where m is the slope and b is the y-intercept.
Once you have the regression equation, you could substitute .333 (the AVG converted from the original format to a decimal representing .333) into the equation to solve for 'y', which would represent the predicted number of HR for that AVG.
For example, if your regression line equation was y = 10x + 5, and you wanted to predict the number of home runs for an AVG of .333, your equation would look like y = 10(.333) + 5 which would equal 8.33 HR. Again, this is an illustrative example without the actual regression output, so use your spreadsheet data to get the precise answer.