Statistics: 2.3 Additional Displays of Quantitative Data
What is a Ogive
An ogive is a graph that represents the cumulative frequency or cumulative relative frequency for the class. It is constructed by plotting points whose x-coordinates are the upper class limits and whose y-coordinates are the cumulative frequencies or cumulative relative frequencies. After the points for each class are plotted, line segments are drawn connecting consecutive points. An additional line segment is drawn connecting the point for the first class to the horizontal axis at a location representing the upper limit of the class that would precede the first class (if it existed).
What type of variable is required when drawing a time-series plot?
Quantitative variable is required when drawing a time-series plot. If the value of a variable is measured at different points in time, the data are referred to as time-series data. A time-series plot is obtained by plotting the time in which a variable is measured on the horizontal axis and the corresponding value of the variable on the vertical axis. Thus, a quantitative variable is required. The variable can be at the interval or ratio level of measurement, and it can be discrete or continuous.
To Determine the Misery Index
The misery index is defined as the sum of the unemployment rate and the inflation rate. According to the misery index, which year was more "miserable," year 17 or year 19? To determine the misery index for each year, first estimate the unemployment and inflation rates for each year. In year 17, the unemployment rate was about 7.4% and the inflation rate was about 3.3% Now calculate the misery index for year 17. 7.4% + 3.3% = 10.7% In year 19, the unemployment rate was about 5.9% and the inflation rate was about 2.9%. Now calculate the misery index for year 19. 5.9% + 2.9% = 8.8% The misery index for year 17 was approximately 10.7% and for year 19 approximately 8.8%, so year 17 was more "miserable."
Why do we draw time-series plots?
Time-series plots are used to identify trends in the data over time