1. Descriptive Statistics  Summarizing, Simplifying  Useful for comprehending data, and thus making meaningful interpretations, particularly in medium to large data sets.  Describing  Useful for recognizing important characteristics of data  Used in inferential statistics

2. Important Characteristics of Data  Center – typical data value  Variation – spread in data  Distribution – shape of data distribution  Outliers – problems in data  Time – changes over time  Population Size –

3. Graphical Summary Methods for Qualitative Data  Pie Chart  Useful for qualitative or quantitative data  Bar Chart  Useful for qualitative data  Called a Pareto chart if bars ordered by height

4. Graphical Summary Methods for Quantitative Data  Frequency Histogram  A “connected bar plot” with bar height proportional to the frequency of the associated value or class (interval of values)  Graphical summary of a frequency distribution (sometimes called a frequency table)

5. Frequency Distribution  For Discrete Data:  Lists data values and corresponding counts  Resulting histogram has a bar on each value with height proportional to its count  For Continuous Data:  Data is divided into classes (intervals of values) and the classes are listed along with the corresponding counts

6. Types of Histograms  Frequency – height of bar is count  Relative Frequency - height of bar is relative frequency ( proportion/percentage/probability)  Cumulative Frequency – height of bar is cumulative count  Cumulative Relative Frequency – height of bar is cumulative relative frequency (percentile)

7. Other Types of Graphs  Stem-and-Leaf Plot  Each value is separated into a stem (such as the leftmost value or values) and a leaf (such as the rightmost value or values)  Stems are listed in order and leaves are plotted alongside the appropriate stem  Ordered Stem-and-Leaf Plot  Dotplot  Each value is plotted as a dot along an x-axis. Dots representing equal values are stacked.

8. Other Types of Graphs  Scatter Diagram or Scatter Plot  Plot of paired (x,y) data with x on the horizontal axis and y on the vertical axis.  Useful for seeing relationship between x and y  Time-Series Plot  A special scatter diagram which as time plotted on the horizontal axis.

9. Importance of Knowing the Distribution of Data  Distribution can affect the choice of an appropriate statistic to use.  Distribution can aid in determining the validity of many inferential statistics.  Common data distributions  Bell (normal), bi-modal, right-skewed (chi- squared, exponential), left-skewed

10. Numerical Summary Methods  Measures of Center (Location)  The middle value or typical observation from a population.  Measures of Variability  The dispersion or spread in the population.  Measures of Relative Standing  The comparative value relative to the population.

11. Measures of Center Population Mean  Mean (Arithmetic Mean)  The size of the population is denoted by N. The sample size is denoted by n. Sample Mean

12. Measures of Center  Median  Middle value in the ordered data for odd n.  Mean of the 2 middle values for even n.  Commonly called the 50 th percentile.  The location of the median in the ordered data set is:(n+1)÷2

13. Measures of Center  Mode  Most common value (occurs most frequently)  Midrange  Midway between the lowest and highest value  Trimmed Mean  Mean of values remaining after an equal number of values are removed from each tail.

14. Skewness Mode = Mean = Median SKEWED LEFT (negatively ) SYMMETRIC Mean Mode Median SKEWED RIGHT (positively) Mean Mode Median

15. MeanMedianModeMidrange Trimmed Mean Values utilized? AllMiddle Most Common Extreme All but extreme Outliers? Not Robust RobustRobust Robust Exists?UniqueUnique May not exist or be unique UniqueUnique Data type? Quan.Quan.AnyQuan.Quan. Compare Measures of Center

16. Measures of Variation  Range  Distance between minimum and maximum  Range = Max – Min  The range does not measure the overall variability in the data. A measure is needed which incorporates the variability of every value in the data. One was is to look at deviations from the mean (x i -  for each x i.

17. Measures of Variation Population Variance  Variance  The average squared difference of the observations from the mean. Sample Variance

18. Calculator Formula for Sample Variance

19. Measures of Variation Population Standard Deviation  Standard Deviation  The square root of the average squared difference of the observations from the mean. Sample Standard Deviation

20. Calculator Formula for Sample Standard Deviation

21. Empirical Rule For data that is approximately bell-shaped in distribution,  68% of data values fall within 1 standard deviation of the mean,  95.4% of data values fall within 2 standard deviation of the mean,  99.7% of data values fall within 3 standard deviation of the mean,

22. x The Empirical Rule (applies to bell-shaped distributions ) FIGURE 2-13

23. x - s x x + sx + s 68% within 1 standard deviation 34% The Empirical Rule (applies to bell-shaped distributions ) FIGURE 2-13

24. x - 2s x - s x x + 2s x + sx + s 68% within 1 standard deviation 34% 95% within 2 standard deviations The Empirical Rule (applies to bell-shaped distributions ) 13.5% FIGURE 2-13

25. x - 3s x - 2s x - s x x + 2s x + 3s x + sx + s 68% within 1 standard deviation 34% 95% within 2 standard deviations 99.7% of data are within 3 standard deviations of the mean The Empirical Rule (applies to bell-shaped distributions ) 0.1% 2.4% 13.5% FIGURE 2-13

26. Measures of Relative Position  Standard Score or Z-Score

27. Measures of Relative Position Order Statistics  The order statistics, denoted by, x (1), x (2), … x (n) are the observed data values ordered from smallest to greatest.

28. Measures of Relative Position  Percentile  The k th percentile (P k ) separates the bottom k% of data from the top (100-k)% of data.  The location of P k in the order statistics is:

29. Finding the Value of the kth Percentile Sort the data. (Arrange the data in order of lowest to highest.) The value of the kth percentile is midway between the Lth value and the next value in the sorted set of data. Find P k by adding the L th value and the next value and dividing the total by 2. Start Compute L = n where n = number of values k = percentile in question ) ( k 100 Change L by rounding it up to the next larger whole number. The value of P k is the Lth value, counting from the lowest Is L a whole number ? Yes No Figure 2-15

30. Measures of Relative Position  Quartiles  The quartiles (Q 1 =P 25, Q 2 =P 50 and Q 3 =P 75 ) separate the data into fourths.  Interquartile Range (IQR)  The distance between the first and third quartiles: IQR=Q 3- Q 1.  The IQR is a measure of variability which is less affected by outliers than the range, variance and standard deviation.

31. Box-and-Whisker Diagram (Boxplot)  Graphical display of the “5 Number Summary”  X (1) =Min  Q 1 =P 25, Q 2 =P 50, Q 3 =P 75  X (n) =Max  Inner & Outer Fences  Useful for identifying potential outliers in data.

32. Bell-Shaped Figure 2-17 Boxplots

33. Bell-Shaped Figure 2-17 Boxplots Uniform

34. Bell-ShapedSkewed Figure 2-17 Boxplots Uniform

35. Percentile Rank  If x=P k and k is the percentile rank of x, then k is approximately equal to:

36. Exploring  Measures of center: mean, median, and mode  Measures of variation: Standard deviation and range  Measures of relative location: order statistics, minimum, maximum, percentile  Unusual values: outliers  Distribution: histograms, stem-leaf plots, and boxplots