Complete the following steps and then answer the exercise questions below.

Step 1. Import the training and scoring data sets for this exercise into a repository or read the data from the CSV source files using Read CSV operators.

Next, using the appropriate R functions for each step:

Step 2. Load the library needed to create discriminant analysis models. Attach the training data set if desired.

Step 3. Make a discriminant analysis model that will predict CropDamage using all other attributes in the training data set as independent variables.

Step 4. Apply the discriminant analysis model to make predictions for the scoring data set.

Step 5. Combine the class and posterior values from the predictions generated in step 4 into a new data frame with the scoring data set. Multiply the posterior values by 100 and round to one decimal place to improve readability. View the data frame to examine the results.

How many farms in the scoring data set are predicted to experience moderate drought damage to their crops?

• 314
• 56
• 424
• 462

Using the "prior" property of your discriminant analysis model in R, what probability that farms in this predictive model will experience mild drought damage to their crops?

• 7.95%
• 11.9%
• 15.2%
• 64.9%

What level of drought-related crop damage is predicted for the farm on row 320 of the scoring data set?

• Severe
• Moderate
• Mild
• No Damage