updated answers

hw1/answers

@@ -53,21 +53,29 @@ German market.
 
 (a) Describe three real-life applications in which classification might be useful. Describe the response, as well as the predictors. Is the goal of each application inference or prediction? Explain your
 answer.
+    Image identification. The predictors could be things like "distribution of greyscale intensity", "distribution of colors", and any number of clever things I'm sure machine learning professionals have thought up. The response is the most probably classification. This is a prediction.
+    Galactic classification. Really this is very similar to general image identification, but we classify galaxies using very specific spectral bands for the predictors that involve light intensity, but then we also look at how strong particular spikes or dips in the spectrum are, so we might have predictors for "emission line strength" for several spectral features. The response is the most likely galactic classification. This is a prediction.
+    Speech recognition. The predictors would perhaps be the audio spectrum, with the response being the word the audio spectrum corresponds to. This would predict the most likely word for the audio received.
 
 (b) Describe three real-life applications in which regression might
 be useful. Describe the response, as well as the predictors. Is the
 goal of each application inference or prediction? Explain your
 answer.
+    Marketing data is obvious. The predictor is perhaps how much was spent on a certain type of marketing, or a few types of marketing -- this is now sounding like the example from the book. The response is an amount sold for the same fiscal period. You could use inference or prediction here: inference to how how many addition sales you might add by spending marketing funds, or prediction by asking just "how many sales did we see when we spent X amount on marketing?"
+    I want to try to use this for my project: understanding the time delay, or reverberation, of a dynamic spectral feature compared against a similarly dynamic reference feature. 2 predictors, line-of-sight velocity and time delay, give a response of light intensity. Our task is to prediction the light intensity as a function of these predictors. This is actually a vanguard question in astrophysics, and I'll bet somebody is already trying to do this!
+    Maybe something municipal. I could predict the taxable income of a city based on a number of predictors, like availability of mass transit or highways, demographics, resources, distance to neighbouring cities, and all kinds of things, then the response would continue to just be taxable income given all of these inputs. Perhaps it would be good to consider an inference questions here, for example: how would my city's taxable change if I increased the availability of public transit?
+
 
 (c) Describe three real-life applications in which cluster analysis
 might be useful.
-    Star categories using spectral strengths
+    Categorizing star type by spectral band strengths.
+    Plant and animal species identification.
+    Tracking objects in sensor data.
 
 
 
 
-9. This exercise involves the Auto data set studied in the lab. Make sure
+9. This exercise involves the Auto data set studied in the lab. Make sure that the missing values have been removed from the data.
-that the missing values have been removed from the data.
 (a) Which of the predictors are quantitative, and which are quali-
 tative?
 (b) What is the range of each quantitative predictor? You can an-