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SUMMARIZING DATA. THROUGH TABLES AND GRAPHS. FOR QUALITATIVE DATA (2.1). FREQUENCY DISTRIBUTION TABLES FREQUENCY DISTRIBUTION GRAPHS. DEFINITIONS. Frequency Distribution: Lists each category (label) of data and the number of occurrences. Sum of all = population or sample size
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SUMMARIZING DATA THROUGH TABLES AND GRAPHS
FOR QUALITATIVE DATA (2.1) • FREQUENCY DISTRIBUTION TABLES • FREQUENCY DISTRIBUTION GRAPHS
DEFINITIONS • Frequency Distribution: Lists each category (label) of data and the number of occurrences. • Sum of all = population or sample size • Relative Frequency: The proportion of occurrences for each category calculated as: Sum of all = 1.
DEFINITIONS • Bar Graph: Vertical or Horizontal. X-axis contains the categories or labels. For Frequency Distributions the y-axis is the number of occurrances. For Relative Frequency Distributions the y-axis is the proportion (values between 0 and 1). Bars do not need to be touching.
FOR QUANTIATIVE DATA (2.2) • CAN TREAT DISCRETE DATA LIKE QUALITATIVE (IF ONLY SEVERAL VALUES) OR AS WE WILL BE TREATING CONTINUOUS DATA (IF MANY VALUES) • SEPARATE CONTINUOUS DATA INTO CLASSES (INTERVALS) AND THEN DO DISTRIBUTION TABLES OR GRAPHS
DEFINITIONS FOR QUANTIATIVE DATA DISRTIBUTIONS Frequency Distribution Table: Similar to that for qualitative data, but each class is for a value or an interval (range) of values. Histograms: Vertical bar graphs, where the x-axis is the number line and each bar is for a class. All bars must touch side to side. Uses Lower Class limit on x-axis.
OTHER FREQUENCY DISTRIBUTIONS • Cumulative Frequency Distributions: Each class listed as before (lowest to largest), but the frequencies are the total for that frequency and all the lower classes. • Relative Cumulative Frequency Distribution: Each Cumulative Frequency divided by total of all frequencies. The last class will have a cumulative value of 1.0
EXAMPLES OF DISCRETE DATA • Use number of siblings • Do as Frequency Table • Do as Relative Frequency • Do as Cumulative Frequency • Do as Relative Cumulative Frequency
FREQUENCY DISTRIBUTION OF CONTINUOUS DATA - DEFINITIONS • Class: An interval of numbers along the number line. • Lower Class Limit (LCL): The beginning number of the class. • Upper Class Limit (UCL): The last number of the class.
FREQUENCY DISTRIBUTION OF CONTINUOUS DATA - DEFINITIONS • Class Width: the difference between lower class limits (or upper class limits), found by taking using data set’s maximum and minimum and calculating rounding up to a convenient value • Midpoint of Each Class: The point in the middle of the class, found by averaging the class lower class limit and the next class lower class limit.
CREATE FREQUENCY DISTRIBUTION FOR CONTNUOUS DATA: EXAMPLE 1. Organize data in ascending order:
CREATE FREQUENCY DISTRIBUTION FOR CONTNUOUS DATA: EXAMPLE 2. Determine the number of classes (5 – 20): For this we will use 6. 3. Find the maximum and minimum: For this max = 4.91 and min = 1.03
CREATE FREQUENCY DISTRIBUTION FOR CONTNUOUS DATA: EXAMPLE 4. Calculate the Class Width: Round UP to a convenient value. We will use 0.70.
CREATE FREQUENCY DISTRIBUTION FOR CONTNUOUS DATA: EXAMPLE 5. Determine First Lower Class Limit: For this we will use 1.00 (something convenient and lower than the Minimum). 6. Determine the next 5 Lower Class Limits by adding class width to the first and each subsequent to get the next: 1.00+.70=1.70; 1.70+.70=2.40 … 3.10, 3.80, 4.50.
CREATE FREQUENCY DISTRIBUTION FOR CONTNUOUS DATA: EXAMPLE 7. Determine the first Upper Class Limit by Subtracting 1 from the last place of the second Lower Class Limit: 1.70-.01=1.69. 8. Find the other 5 Upper Class Limits by adding the class width to each previous Upper Class Limits: 1.69+.7=2.39, 2.39+.7=3.09, …, 3.79, 4.49, 5.19
CREATE FREQUENCY DISTRIBUTION FOR CONTNUOUS DATA: EXAMPLE 9. Now construct the Table ……:
CREATE FREQUENCY DISTRIBUTION FOR CONTNUOUS DATA: EXAMPLE And count the frequencies in each class …:
CREATE FREQUENCY DISTRIBUTION FOR CONTNUOUS DATA: EXAMPLE And complete the Table:
CREATE FREQUENCY DISTRIBUTION FOR CONTNUOUS DATA: EXAMPLE 10. Draw the histogram:
OTHER DISTRIBUTION PLOTS • Stem Leaf Plot: Used for recording and showing dispersion of data. Stem can be the integer portion of a number and the leaves the decimal portion. Or the stem could be the tens digit and the leaves the ones digit. • 5-3,5,6,7,7,8,9 • 6-2,3,3,4,6,6,7,8 • 7-1,1,3,6,9
OTHER DISTRIBUTION PLOTS • Dot Plot: Also used to show dispersion of data. Draw a number line and label the horizontal scale with the numbers from the data from lowest to highest. Then place a dot above the numbers each time the number occurs. * * * * * * * * * * |___|___|___|___|___|___|___|___|___|___| 5.3 5.4 5.5 5.6 5.7 5.8 5.9 6.0 6.1 6.2 6.3
OTHER DISTRIBUTION PLOTS (2.3) • Polygon Plot: Line graph using the midpoints for the x-axis and frequencies for the y-axis. Both ends of the line must come back to the 0 on the y-axis.
OTHER DISTRIBUTION PLOTS • Given a Polygon Plot, construct a Frequency Distribution Table. • 1. Find the Class Width: Difference in Midpoints • 2. Find first two LCL’s: Midpoint +/- ½*Class Width • 3. Find First Upper Class Limit: 2nd LCL – 1 • Find remainder of LCL’s & UCL’s • Find each class’s frequency
OTHER DISTRIBUTION PLOTS • Ogive (pronounced oh jive) Plot: Line Graph used for displaying Cumulative Frequency Distributions. The x-axis is the Upper Class Limit and the y-axis is the Cumulative Frequency. The first point is a class width less than the first Upper Class Limit so that the line starts with a frequency of 0.
OTHER DISTRIBUTION PLOTS • Ogive Plot:
OTHER DISTRIBUTION PLOTS • Time Series Plots: Can be vertical or horizontal bar graphs, or line graphs. X-axis is time intervals or ages (years, months, days) and y-axis is frequency.
MISREPRESENTATIONS OF DATA USING GRAPHS (2.4) • Vertical Scale Manipulation: Not starting the y-axis at 0. Also using a break in the scale. Can make differences look bigger than they really are. • Exaggeration of Bars or Symbols: Used in pictographs. • Horizontal Scale Manipulation: Not all classes or time interval are the same width.
MISREPRESENTATIONS OF DATA USING GRAPHS • “Get your facts first, then you can distort then as you please” Mark Twain • “There are lies, damn lies, and STATISTICS” Mark Twain • “Definition of Statistics: The science of producing unreliable facts from reliable figures.” Evan Esar