Data Analysis with Graphs

Statistics is the gathering, organization, analysis and presentation of numerical information. Raw Data – unprocessed info Variable – quantity being measured Continuous variable – any value within a given range Discrete variable – have only certain values (often integers)

Frequency tables and diagrams – give overview of values and reveals trends in the data. Histogram – type of bar graph in which the areas of the bars are proportional to the frequency of the values of the variable. The bars are all connected and represent a continuous range of values. Examples  for variables whose values can be arranged in numerical order, especially continuous variables such as weight, temp or travel time.

Frequency polygon – illustrates the same info as a histogram or bar graph. Plot frequencies versus variable values and then join the points with straight lines. (line graph) Cummulative-frequency graph shows the running total of the frequencies from the lowest value up.

If the data is large, it is grouped into classes or intervals Generally it is convenient to use 5 to 20 equal intervals that cover the entire range. The interval should be of an even fraction or multiple of the measurement unit for the variable.

Example, if we are looking at temperature and the range is from 18 to 33 degrees. The difference is 15. Therefore you could use five 3-degree intervals. Can you determine a problem when we use intervals? How can we fix it? We can use half values so that a whole number does not straddle two intervals.

Categorical Data are given labels rather than being measured numerically. Ex. Favourite foods, categorical data Other types of graphs  circle graphs, pictographs Homework Pg 101 # 1,3, 5,8,15