Chapter 2 2.1 Statistics is all about data: (nothing but counts and measurement) Data- Qualitative: names, labels, etc. Ex. Gender survey Quantitative: numerical  count (discrete), sibling survey (numerical discrete)  Measurement (continuous), height survey (numerical continuous) Examples: Exam 1 (2012) Q. 14.) B Q. 15.) C 2.2 Other forms: ordinal variable ex. Level of lower back pain Data Summary: Ex. Qualitative data- data consists of several levels. In the gender survey it was male and female. Information Content: counts or proportion (relative frequency of different levels) Store information by tabulating and or a bar graph (plot) Ex. Quantitative data. Continuous data: has infinitely many values. Data is subdivided into class intervals (bins) and tabulated. Plotted as a histogram. In a bar plot there is no order. In a histogram there is an order with no gaps) 2.3 Properties of a histogram 1.)Area of each bar = relative frequency (proportion) of data in the given interval. 2.)The total area of the histogram has to = 1.0 3.)The proportion data in a given interval (a, b) = total area of histogram over the interval (a, b) ex. Q12 ( to get this answer you add the two bars together to get 14 as an answer) &13 (sample size n= 6+9+12+10+4 = 41 then you take 14 over 41 x100 to get the percentage in the interval which is ~34) 2.4 Skewness: symmetric… is the left hand side of the graph a reflection of the right hand side of the graph If the graph is not symmetric, the graph could be skew