STT 315 This lecture is based on Chapter 2 of the textbook. Acknowledgement: Author is thankful to Dr. Ashok Sinha, Dr. Jennifer Kaplan and Dr. Parthanil Roy for allowing him to use/edit some of their slides. 1
Topic of this chapter These materials can be read from Chapter 2.1-2.5 of the textbook. We shall first cover some descriptive statistics of qualitative variables (Ch2.1). Later we shall study descriptive statistics of quantitative variables (Ch2.2-2.5). In descriptive statistics we summarize data through graphs and tables. 2
How to display Qualitative Data? Frequency Tables Bar graph (or bar chart) Pie chart (or pie diagram) Pareto chart (or Pareto diagram) 3
Qualitative variables Qualitative or categorical variable cannot be usually measured in numerical scale, and simply records quality. Each category of a qualitative variable is also called class or level. For instance, the qualitative variable GENDER has two classes, namely Male and Female. If we count number of observations belonging to each class, then this count is called class frequency or simply frequency. Relative frequency of a class is obtained by dividing the class frequency by total number of observations. 4
Frequency Tables These are tables in which classes (categories) are written in the left most column and the corresponding counts are written in the second column. Count is also known as frequency. Sometimes proportions (or percentages) are also written instead of or in addition to the actual counts. Proportion is also called relative frequency. 5
Frequency Table: An Example Frequency Table of the number of Golf Balls sold in different days of a week Day # of Golf Balls Sold % of Golf Balls Sold (Frequency) Monday 17 19.54 Tuesday 13 14.94 Wednesday 15 17.24 Thursday 20 22.99 Friday 22 25.29 Total 87 100 6
Bar Charts A bar chart or bar graph is a chart with rectangular bars with lengths proportional to the values that they represent. The bars can be plotted vertically (more common) or horizontally (less common). The percentage or relative proportions can also be plotted instead of the actual values. 7
Bar Chart: Golf Balls Sold # of Golf Balls Sold 25 20 15 17 13 15 20 22 10 5 0 Monday Tuesday Wednesday Thursday Friday 8
Bar Chart: % Golf Balls Sold % golf balls 30 25 22.99 25.29 20 15 19.54 14.94 17.24 10 5 0 Monday Tuesday Wednesday Thursday Friday 9
Pie Chart A pie chart (or a circle graph) is a circular chart divided into sectors, illustrating proportion. The arc length of each sector (and consequently its central angle and area), is proportional to the quantity it represents. The math is carried out based on the following: 100% is same as 360 degrees. 10
Pie Chart: Golf Ball Sold % of Golf Balls Sold 25% 20% 15% Monday Tuesday Wednesday Thursday Friday 23% 17% 11
Pie Chart: An Example Pie Chart of English Native Speakers 12
Bar Chart vs. Pie Chart Bar chart is used more often to represent the actual values while pie chart is used to represent relative proportions (in %). When comparison of relative proportion is important, pie chart is more appropriate. When the absolute counts or values are more important, a bar chart should be used. 13
Major points so far First step in organizing data draw a picture Appropriate pictures for categorical data Pie chart Bar chart 14
Pareto diagram Pareto diagram is a particular type of bar diagram in which the classes are arranged on the horizontal axis in decreasing frequencies. That means in Pareto diagram the leftmost class has the highest frequency bar, followed by the class with next highest frequency bar, and so on. 15
The following Pareto diagram represents the incarceration rate (per 100000 people) of various countries. 16
Displaying Quantitative Data Histograms Stem-and-Leaf Displays Dotplots 17
Histograms Histogram is a graphical representation, showing a visual impression of the distribution of quantitative data. It consists of adjacent rectangles, erected over intervals (also known as bins or classes). The lengths of the intervals may be different. The interval may contain a single value. The heights are equal to the number (frequency) of the observations in the corresponding bins. Sometimes percentages (or relative frequencies) are also represented by the heights. 18
Histogram: An Example The heights of 31 Black Cherry trees 19
How to choose the bin size? Let the computer decide it for you. What happens for the observations in the boundary of two bins? Put them in the higher bin. Don t we lose information? Yes, we do. A Few Questions 20
Stem-and-Leaf Display Another device for presenting quantitative data in a graphical format. Assists in visualizing the shape of the distribution of the observations. Unlike histograms, stem-and-leaf displays retain the original data. Contains two columns separated by a vertical line. The left column contains the stems and the right column contains the leaves. Suppose we have the following data on weights (in lb) of 17 school-kids: 88 47 68 76 46 106 49 63 72 64 84 66 68 75 72 81 44 21
How do they work? Sorted data: 44 46 47 49 63 64 66 68 68 72 72 75 76 81 84 88 106 Stem Leaf 4 4 6 7 9 5 6 3 4 6 8 8 7 2 2 5 6 8 1 4 8 9 10 6 key: 6 3 = 63 leaf unit: 1.0 stem unit: 10.0 22
Dotplots A dotplot is a statistical chart consisting of group of data points plotted on a simple scale. They can be drawn both horizontally and vertically. 23
Summary We have learnt three methods of displaying quantitative data: histogram, stem-and-leaf display and dotplot. When the data-size is small, stem-and-leaf display and dotplot are more useful. When the data-size is large, histogram is more useful. 24
Distribution of the Data-points Three important features: Shape of the distribution, Center of the distribution, Spread of the distribution. 25
Shape of a Distribution: Modes The peaks of a histogram are called modes. A distribution is unimodal if it has one mode, bimodal if it has two modes, multimodal if it has three or more modes. 26
Unimodal, Bimodal or Multimodal? Unimodal Bimodal Multimodal 27
Uniform Histogram A histogram that doesn t appear to have any mode. All the bars are approximately the same. 28
Shape of a Distribution: Symmetry If the histogram can be folded along a vertical line through the middle and have the edges match pretty closely, then the distribution is symmetric. Otherwise, it is skewed. 29
Skewed to the left or right? Skewed to the left ( tail is in left) Skewed to the right (Tail is in right) 30
Shape of a Distribution: Outliers Outliers are the data-points that stand off away from the body of the histogram. They are too high or too low compared to most of the observations. 31
The following distribution is A. Unimodal and skewed to the left B. Bimodal and skewed to the right C. Bimodal and symmetric D. Multimodal and symmetric E. Unimodal and skewed to the right 32
Does this distribution have an outlier? (a) Yes, it does (b) No, it doesn t 33
The following distribution is A. Unimodal and skewed to the left B. Bimodal and skewed to the right C. Bimodal and symmetric D. Multimodal and symmetric E. Unimodal and skewed to the right 34
Numerical measures for quantitative data 35
Center of a Distribution Median: The middlemost observation when the data is sorted in increasing order Median can always be used as the center of a distribution. Mean: The average of all data-points. Mean can be used as the center of a distribution when the distribution is symmetric. 36
What is Median? Median is the middlemost observation when the data is sorted in increasing order. Data: 23, 33, 12, 39, 27 Sorted Data: 12, 23, 27, 33, 39 Median: 27 37
What if there are even number of observations? Take the average of two middlemost observations in that case Data: 23, 33, 12, 39, 27, 10 Sorted Data: 10, 12, 23, 27, 33, 39 Median = (23+27)/2 = 25. 38
What is the general rule? Suppose there are n observations. Sort them in increasing order. If n is odd then the median is the observation in the (n+1)/2 th position. If n is even, then the median is the average of the observations in the (n/2) th and (n/2 + 1) th positions. 39
When n is odd Data: 23, 33, 12, 39, 27 n = 5 (odd) Sorted Data: 12, 23, 27, 33, 39 Median = observation in the (5+1)/2 th position = observation in the 3 rd position = 27. 40
When n is even Data: 23, 33, 12, 39, 27, 10 n = 6 (even) Sorted Data: 10, 12, 23, 27, 33, 39 Median = average of the observations in the (6/2) th and (6/2 +1) th positions = average of the observations in the 3 rd and 4 th positions = (23+27)/2 = 25. 41
What is mean? Mean is the average of all the observations (i.e., add up all the values and divide by the number of values). If an observation repeats, we add it the number of times it repeats when we calculate the average. Mean can be used as the center of a distribution when the distribution is symmetric. Data: 10, 13, 18, 22, 29 Mean = (10 + 13 + 18 + 22 + 29)/5 = 18.40 42
Mean vs. Median Data: 10, 13, 18, 22, 29 Without the outlier: Mean = 18.40 Median = 18 Data: 10, 13, 18, 22, 29, 68 With the outlier: Mean = 26.67 Median = 20 Conclusion: Mean is more outlier-sensitive compared to the median. 43
Mean vs. Median Mean is more outlier-sensitive compared to median. For a symmetric distribution, mean = median. Thus mean is more useful as the center of a distribution when the distribution is symmetric. But median can always be used as the center of a distribution. For a right-skewed distribution, mean > median. For a left-skewed distribution, mean < median. Learn to use TI 83/84 Plus to compute mean and median. 44
TI 83/84 Plus commands To enter the data: Press [STAT] Under EDIT select 1: Edit and press ENTER Columns with names L1, L2 etc. will appear Type the data value under the column; each data entry will be followed by ENTER. To clear data: Pressing CLEAR will clear the particular data. To clear all data from all columns press [2nd] & + and then choose 4: ClrAllLists. 45
TI 83/84 Plus commands 46
Effect of Linear Transformation Suppose every observation is multiplied by a fixed constant. Then median of transformed observations is the median of the original observations times that same constant. mean of transformed observations is the mean of the original observations times that same constant. Data: 10, 13, 18, 22, 29 Mean = 18.40. Median = 18. Suppose transformed data = (-3)*original data. So transformed data: -30, -39, -54, -66, -87 Mean = (-3)*18.40 = -55.20. Median = (-3)*18 = -54. 47
Effect of Linear Transformation Suppose a fixed constant is added to (or subtracted from) each observation. Then median of transformed observations is the median of the original observations plus (or minus) that same constant. mean of transformed observations is the mean of the original observations plus (or minus) that same constant. Data: 10, 13, 18, 22, 29 Mean = 18.40. Median = 18. Suppose transformed data = original data + 2.5. Hence transformed data: 12.5, 15.5, 20.5, 24.5, 31.5 Mean = 18.40 + 2.5 = 20.90. Median = 18 + 2.5 = 20.50. 48
Spread of a Distribution Are the values concentrated around the center of the distribution or they are spread out? Range, Interquartile Range, Variance, Standard Deviation. Note: Variance and standard deviation are more appropriate when the distribution is symmetric. 49
Range Range of the data is defined as the difference between the maximum and the minimum values. Data: 23, 21, 67, 44, 51, 12, 35. Range = maximum minimum = 67 12 = 55. Disadvantage: A single extreme value can make it very large, giving a value that does not really represent the data overall. On the other hand, it is not affected at all if some observation changes in the middle. 50
Interquartile Range (IQR) What is IQR? IQR = Third Quartile (Q 3 ) First Quartile (Q 1 ). What are quartiles? Recall: Median divides the data into 2 equal halves. The first quartile, median and the third quartile divide the data into 4 roughly equal parts. 51
Quartiles The first quartile (Q 1, lower quartile) is that value which is larger than 25% of observations, but smaller than 75% of observations. The second quartile (Q 2 ) is the median, which is larger than 50% of observations, but smaller than 50% of observations. The third quartile (Q 3, upper quartile) is that value which is larger than 75% of observations, but smaller than 25% of observations. Obviously, Q 1 < Q 2 (= median) < Q 3. How to compute the quartiles? We shall use TI 83/84 Plus. 52
IQR vs. Range IQR is a better summary of the spread of a distribution than the range because it has some information about the entire data, where as range only has information on the extreme values of the data. IQR is less outlier-sensitive than range. 53
Outlier-sensitivity Data: 10, 13, 17, 21, 28, 32 Without the outlier IQR = 15 Range = 22 Data: 10, 13, 17, 21, 28, 32, 59 With the outlier IQR = 19 Range = 49 Conclusion: IQR is less outlier-sensitive than range. 54
Variance and Standard Deviation The sample variance (s 2 ) is defined as: s 2 1 2 2 ( x1 x) ( xn x). n 1 Subtract the mean from each value, square each difference, add up the squares, divide by one fewer than the sample size. The sample standard deviation (s), is the positive square root of sample variance, i.e. s 2 s. 55
Variance and Standard Deviation Larger the variance (and standard deviation) more dispersed are the observations around the mean. The unit of variance is square of the unit of the original data, whereas standard deviation has the same unit as the original data. Both variance and standard deviation are more appropriate for symmetric distributions. 56
Standard Deviation: An Example Data: 3, 12, 8, 9, 3 (n=5 in this case) Mean = (3+12+8+9+3)/5 = 35/5 =7. Data Deviations from mean Squared Deviations ------------------------------------------------------------------------------ 3 3 7 = -4 (-4)x(-4) =16 12 12 7 = 5 5 x 5 =25 8 8 7 = 1 1 x 1 = 1 9 9 7 = 2 2 x 2 = 4 3 3 7 = -4 (-4)x(-4) =16 ------------------------------------------------------------------------------ Total = 62 Now divide by n-1=4: s 2 = 62/4 = 15.50. s = 15.5 = 3.94. Answer: and the The variance standard is 15.50. deviation in this example is 3.94 57
Effect of Linear Transformation Suppose every observation is multiplied by a fixed constant. Then range/iqr/standard deviation of transformed observations is the range/iqr/standard deviation of the original observations times the absolute value of that same constant. variance of transformed observations is the variance of the original observations times the square of that same constant. Temperature data (in F): 10, 13, 18, 22, 29 Range = 19 F, IQR =14 F, s = 7.5 F, s 2 = 56.25 F 2. Suppose transformed data = (-3)*original data. So transformed data (in F): -30, -39, -54, -66, -87 Range = -3 *19 = 57 F, IQR = -3 *14 = 42 F, s = -3 * 7.5 = 22.50 F, s 2 = (-3) 2 *56.25 = 506.25 F 2. 58
Effect of Linear Transformation Suppose a fixed constant is added to (or subtracted from) each observation. Then range/iqr/standard deviation/variance of transformed observations remains the same as that of the original observations. Temperature data (in F): 10, 13, 18, 22, 29 Range = 19 F, IQR =14 F, s = 7.5 F, s 2 = 56.25 F 2. Suppose transformed data = original data + 2.5. Hence transformed data (in F): 12.5, 15.5, 20.5, 24.5, 31.5 Range = 19 F, IQR =14 F, s = 7.5 F, s 2 = 56.25 F 2. 59
Empirical rule & Chebyshev s rule 60
Empirical rule For approximately symmetric unimodal (bellshaped/mound shaped) distribution Approximately 68% of observations fall within 1 standard deviation of mean. Approximately 95% of observations fall within 2 standard deviations of mean. Approximately 99.7% of observations fall within 3 standard deviations of mean. 61
Empirical rule 62
Empirical rule 63
Chebyshev s rule For any distribution at least 1 1 k2 of the observations will fall within k standard deviations of mean, where k 1. Chebyshev s rule is for any distribution, whereas the empirical rule is valid only for approximately symmetric unimodal (mound-shaped) distribution. If k=1, not much information is available from Chebyshev s rule. According to Chebyshev at least 75% observations fall within 2 standard deviations of mean. According to Chebyshev at least 88.9% of observations fall within 3 standard deviations of mean. 64
Box plot 65
Box Plot Box plot is a graphical representation of the following 5 number summary: 1. Minimum Value, 2. Lower Quartile, 3. Median (the middle value), 4. Upper Quartile, 5. Maximum Value. NOTE: Data must be ordered from lowest value to highest value before finding the 5 number summary. 66
Box Plots Are a representation of the five number summary (Minimum, Maximum, Median, Lower Quartile, Upper Quartile). Half the data are in the box One-quarter of the data are in each whisker. If one part of the plot is long, the data are skewed. Box-plot is very useful for comparing distributions This box plot indicates data are skewed to the left. 67
Box Plot Box Plot is a pictorial representation of the 5-number summary. 68
Outliers Any observation farther than 1.5 times IQR from the closest boundary of the box is an outlier. If it is farther than 3 times IQR, it is an extreme outlier, otherwise a mild outlier. One can also indicate the outliers in a box plot, by drawing the whiskers only up to 1.5 times IQR on both sides, and indicating outliers with stars or crosses (or other symbols). 69
Suppose An example min = 2, Q 1 = 18, median = 20, Q 3 = 22, max = 35. Which of the following observations are outliers? A. 10 B. 15 C.25 D.30 70
Histogram vs. Box plot Both histogram and box plot capture the symmetry or skewness of distributions. Box plot cannot indicate the modality of the data. Box plot is much better in finding outliers. The shape of histogram depends to some extent on the choice of bins. 71
Comparing Distributions We can compare between distributions of various data-sets using Box Plots (or the 5-Number Summary), Histograms. We shall first compare distributions using box plots.
Which type of car has the largest median Time to accelerate? A. upscale B. sports C. small D. large E. family 73
Which type of car has the smallest median time value? A. upscale B. sports C. small D. Large E. Luxury 74
Which type of car always take less than 3.6 seconds to accelerate? A. upscale B. sports C. small D. Large E. Luxury 75
Which type of car has the smallest IQR for Time to accelerate? A. upscale B. sports C. small D. Large E. Luxury 76
What is the shape of the distribution of acceleration times for luxury cars? A. Left skewed B. Right skewed C. Roughly symmetric D. Cannot be determined from the information given. 77
What percent of luxury cars accelerate to 30 mph in less than 3.5 seconds? A. Roughly 25% B. Exactly 37.5% C. Roughly 50% D. Roughly 75% E. Cannot be determined from the information given 78
What percent of family cars accelerate to 30 mph in less than A. Less than 25% B. More than 50% C. Less than 50% D. Exactly 75% E. None of the above 3.5 seconds? 79
Comparing Distributions Use of Histograms
FREQUENCY Which data have more A 6 variability? 6 B 5 4 3 5 4 3 2 1 2 1 0 2 4 6 8 10 SCORE A. Graph A B. Graph B 12 0 2 4 6 8 10 12 SCORE C. Both have the same variability 81
Which data have more A variability? B A. Graph A B. Graph B C. Both have the same variability 82
Which data have a higher A median? B A. Graph A B. Graph B C. Both have the same median 83
FREQUENCY FREQUENCY Which data have more A 6 variability? 6 B 5 5 4 3 4 3 2 1 2 1 0 2 4 6 8 10 SCORE A. Graph A B. Graph B 12 0 2 4 6 8 10 12 SCORE C. Roughly, both have the same variability 84
z-score 85
How to compare apples with oranges? A college admissions committee is looking at the files of two candidates, one with a total SAT score of 1500 and another with an ACT score of 22. Which candidate scored better? How do we compare things when they are measured on different scales? We need to standardize the values. 86
How to standardize? Subtract mean from the value and then divide this difference by the standard deviation. The standardized value = the z-score value mean std.dev. z-scores are free of units. 87
z-scores: An Example Data: 4, 3, 10, 12, 8, 9, 3 (n=7 in this case) Mean = (4+3+10+12+8+9+3)/7 = 49/7 =7. Standard Deviation = 3.65. Original Value z-score -------------------------------------------------------------- 4 (4 7)/3.65 = -0.82 3 (3 7)/3.65 = -1.10 10 (10 7)/3.65 = 0.82 12 (12 7)/3.65 = 1.37 8 (8 7)/3.65 = 0.27 9 (9 7)/3.65 = 0.55 3 (3 7)/3.65 = -1.10 -------------------------------------------------------------- 88
Interpretation of z-scores The z-scores measure the distance of the data values from the mean in the standard deviation scale. A z-score of 1 means that data value is 1 standard deviation above the mean. A z-score of -1.2 means that data value is 1.2 standard deviations below the mean. Regardless of the direction, the further a data value is from the mean, the more unusual it is. A z-score of -1.3 is more unusual than a z-score of 1.2. 89
How to use z-scores? A college admissions committee is looking at the files of two candidates, one with a total SAT score of 1500 and another with an ACT score of 22. Which candidate scored better? SAT score mean = 1600, std dev = 500. ACT score mean = 23, std dev = 6. SAT score 1500 has z-score = (1500-1600)/500 = -0.2. ACT score 22 has z-score = (22-23)/6 = -0.17. ACT score 22 is better than SAT score 1500. 90
Which is more unusual? A. A 58 in tall woman z-score = (58-63.6)/2.5 = -2.24. B. A 64 in tall man z-score = (64-69)/2.8 = -1.79. C. They are the same. Heights of adult women have mean of 63.6 in. std. dev. of 2.5 in. Heights of adult men have mean of 69.0 in. std. dev. of 2.8 in. 91
Using z-scores to solve problems An example using height data and U.S. Marine and Army height requirements Question: Are the height restrictions set up by the U.S. Army and U.S. Marine more restrictive for men or women or are they roughly the same? 92
Data from a National Health Survey Heights of adult women have mean of 63.6 in. standard deviation of 2.5 in. Heights of adult men have mean of 69.0 in. standard deviation of 2.8 in. Height Restrictions Men Minimum Women Minimum U.S. Army 60 in 58 in U.S. Marine Corps 64 in 58 in 93
Heights of adult men have mean of 69.0 in. standard deviation of 2.8 in. Heights of adult women have mean of 63.6 in. standard deviation of 2.5 in. Men Minimum 60 in Women minimum 58 in U.S. Army U.S. Marine z-score = -3.21 Less restrictive 64 in z-score = -1.79 z-score = -2.24 More restrictive 58 in z-score = -2.24 More restrictive Less restrictive 94
Effect of Standardization Standardization into z-scores does not change the shape of the histogram. Standardization into z-scores changes the center of the distribution by making the mean 0. Standardization into z-scores changes the spread of the distribution by making the standard deviation 1. 95
Z-score and Empirical Rule When data are bell shaped, the z-scores of the data values follow the empirical rule. 96
Outlier detection with z-score Empirical Rule tells us that if data are mound-shaped distributed, then almost all the data-points are within plus minus 3 standard deviations from the mean. So an absolute value of z-score larger than 3 can be considered as an outlier. 97
2004 Olympics Women s Heptathlon Austra Skujyte (Lithunia) Shot Put = 16.40m, Long Jump = 6.30m. Carolina Kluft (Sweden) Shot Put = 14.77m, Long Jump = 6.78m. Shot Put Long Jump Mean (all contestant) 13.29m 6.16m Std.Dev. 1.24m 0.23m n 28 26 98
Which performance was better? A. Skujyte s shot put, z-score of Skujyte s shot put = 2.51. B. Kluft s long jump, z-score of Kluft s long jump = 2.70. C. Both were same. Mean (all contestant) Shot Put Long Jump 13.29m 6.16m Std.Dev. 1.24m 0.23m n 28 26 99
Based on shot put and long jump whose performance was better? A. Skujyte s, z-score: shot put = 2.51, long jump = 0.61. Total z-score = (2.51+0.61) = 3.12. B. Kluft s, z-score: shot put = 1.19, long jump = 2.70. Total z-score = (1.19+2.70) = 3.89. C. Both were same. 100
Scatterplot 101
Example: Height and Weight How is weight of an individual related to his/her height? Typically, one can expect a taller person to be heavier. Is it supported by the data? If yes, how to determine this association? 102
What is a scatterplot? A scatterplot is a diagram which is used to display values of two quantitative variables from a data-set. The data is displayed as a collection of points, each having the value of one variable determining the position on the horizontal axis and the value of the other variable determining the position on the vertical axis. 103
Example 1: Scatterplot of height and weight 104
Example 2: Scatterplot of hours watching TV and test scores 105
Looking at Scatterplots We look at the following features of a scatterplot:- Direction (positive or negative) Form (linear, curved) Strength (of the relationship) Unusual Features. When we describe histograms we mention Shape Center Spread Outliers 106
Asking Questions on a Scatterplot Are test scores higher or lower when the TV watching is longer? Direction (positive or negative association). Does the cloud of points seem to show a linear pattern, a curved pattern, or no pattern at all? Form. If there is a pattern, how strong does the relationship look? Strength. Are there any unusual features? (2 or more groups or outliers). 107
Positive and Negative Associations Positive association means for most of the datapoints, a higher value of one variable corresponds to a higher value of the other variable and a lower value of one variable corresponds to a lower value of the other variable. Negative association means for most of the datapoints, a higher value of one variable corresponds to a lower value of the other variable and vice-versa. 108
This association is: A. positive B. negative. 109
This association is: A. positive B. negative. 110
Linear Scatterplot Unless we see a curve, we shall call the scatterplot linear. 111
Curved Scatterplot When the plot shows a clear curved pattern, we shall call it a curved scatterplot. 112
Which one has stronger linear association? A.left one, B.right one. Because, in the right graph the points are closer to a straight line. 113
Which one has stronger linear A.left one, B.right one. association? Hard to say. 114
Unusual Feature: Presence of Outlier This scatterplot clearly has an outlier. 115
Unusual Feature: Two Subgroups This scatterplot clearly has two subgroups. 116
Time series plot (Time plot) 117
Time plot Time series is a collection of observations made sequentially through time. In time plot (or time series plot) the time series data are plotted (on vertical axis) against the time (on horizontal axis), and the plots are connected with straight line. From time series plot one can find the movement of the observed values over time and find patterns such as: Trend Seasonality Business cycle (for business data) Unusual features 118
US Example: US population 350000000 Time Series Plot of US 300000000 250000000 200000000 150000000 100000000 50000000 0 1800 1820 1840 1860 1880 1900 t 1920 1940 1960 1980 2000 119
deaths Example: US accidental death Time Series Plot of deaths 11000 10000 9000 8000 7000 1 7 14 21 28 35 Index 42 49 56 63 70 120
Example: Australian red wine sell 121