Chap. 1 & 2
Categorical Variables- #s are just labels and values are arbitrary
Quantitative Variable- measured units (income, height, weight, age)
Ordial- values are not categorical but not quite quantitative
Context- Ideally tells Who What How Where When

Chapter 6: Standard Deviation as a Ruler and the Normal Model z= (y y bar)/s -Adding or subtracting a constant amount to each value just adds or subtracts the constant to the mean, median, quartiles, max and min. The IQR, SD, or range do not change.

Chapter 30: Multiple Regression Linearity Assumption- Straight enough condition- plot the residuals against the predicted values and check for patterns, especially for bends or other nonlinearities. The residuals should appear to have no pattern with

Daniela Sorokko ILRST 212 9/7/06 HW # 2 Chapter 4 Thinking about Shape a) I think the distribution would be unimodal and skewed because most students probably dont have too many traffic tickets either because no one likes to receive and pay tickets o

Daniela Sorokko ILRST 212 HW # 3 Chapter 7 Circle Data, Trends
The relationship between size and time is weak and not linear. It may have a slightly negative direction but its difficult to say because of the large scatter. Nothing conclusive can be

Daniela Sorokko ILRST 212 Homework #1 Chapter 2: Oscars The two variables are names of Oscar winners (categorical) and ages of Oscar winners when they received the award (quantitative). The quantitative variable is measured in years. High way bridges