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Lecture 5 Dustin Lueker Mean - Arithmetic Average Mean of a Sample - x Mean of a Population - μ Median - Midpoint of the observations when they are arranged in increasing order Notation: Subscripted variables n = # of units in the sample N = # of units in the population x = Variable to be measured xi = Measurement of the ith unit Mode - Most frequent value. STA 291 Spring 2010 Lecture 5 2 (mu) (sigma) population mean population standard deviation 2 (sigma-squared) population variance x or xi (x-i) observation x (x-bar) s 2 s sample mean sample standard deviation sample variance summation symbol STA 291 Spring 2010 Lecture 5 3 Sample ◦ Variance s2 2 ( x i x ) n 1 ◦ Standard Deviation Population ◦ Variance 2 2 ( x i x ) s n 1 2 ( x i ) ◦ Standard Deviation N 2 ( x i ) N STA 291 Spring 2010 Lecture 5 4 1. 2. 3. 4. 5. Calculate the mean For each observation, calculate the deviation For each observation, calculate the squared deviation Add up all the squared deviations Divide the result by (n-1) Or N if you are finding the population variance (To get the standard deviation, take the square root of the result) STA 291 Spring 2010 Lecture 5 5 If the data is approximately symmetric and bell-shaped then ◦ About 68% of the observations are within one standard deviation from the mean ◦ About 95% of the observations are within two standard deviations from the mean ◦ About 99.7% of the observations are within three standard deviations from the mean STA 291 Spring 2010 Lecture 5 6 STA 291 Spring 2010 Lecture 5 7 The pth percentile (Xp) is a number such that p% of the observations take values below it, and (100-p)% take values above it ◦ 50th percentile = median ◦ 25th percentile = lower quartile ◦ 75th percentile = upper quartile The index of Lp ◦ (n+1)p/100 STA 291 Spring 2010 Lecture 5 8 25th percentile ◦ lower quartile ◦ Q1 ◦ (approximately) median of the observations below the median 75th percentile ◦ upper quartile ◦ Q3 ◦ (approximately) median of the observations above the median STA 291 Spring 2010 Lecture 5 9 Find the 25th percentile of this data set ◦ {3, 7, 12, 13, 15, 19, 24} STA 291 Spring 2010 Lecture 5 10 Use when the index is not a whole number Want to start with the closest index lower than the number found then go the distance of the decimal towards the next number If the index is found to be 5.4 you want to go to the 5th value then add .4 of the value between the 5th value and 6th value ◦ In essence we are going to the 5.4th value STA 291 Spring 2010 Lecture 5 11 Find the 40th percentile of the same data set ◦ {3, 7, 12, 13, 15, 19, 24} Must use interpolation STA 291 Spring 2010 Lecture 5 12 Five Number Summary ◦ ◦ ◦ ◦ ◦ Minimum Lower Quartile Median Upper Quartile Maximum ◦ ◦ ◦ ◦ ◦ minimum=4 Q1=256 median=530 Q3=1105 maximum=320,000. Example What does this suggest about the shape of the distribution? STA 291 Spring 2010 Lecture 5 13 The Interquartile Range (IQR) is the difference between upper and lower quartile ◦ IQR = Q3 – Q1 ◦ IQR = Range of values that contains the middle 50% of the data ◦ IQR increases as variability increases Murder Rate Data ◦ Q1= 3.9 ◦ Q3 = 10.3 ◦ IQR = STA 291 Spring 2010 Lecture 5 14 Displays the five number summary (and more) graphical Consists of a box that contains the central 50% of the distribution (from lower quartile to upper quartile) A line within the box that marks the median, And whiskers that extend to the maximum and minimum values This is assuming there are no outliers in the data set STA 291 Spring 2010 Lecture 5 15 An observation is an outlier if it falls ◦ more than 1.5 IQR above the upper quartile or ◦ more than 1.5 IQR below the lower quartile STA 291 Spring 2010 Lecture 5 16 Whiskers only extend to the most extreme observations within 1.5 IQR beyond the quartiles If an observation is an outlier, it is marked by an x, +, or some other identifier STA 291 Spring 2010 Lecture 5 17 Values Min = 148 Q1 = 158 Median = Q2 = 162 Q3 = 182 Max = 204 Create a box plot STA 291 Spring 2010 Lecture 5 18 On right-skewed distributions, minimum, Q1, and median will be “bunched up”, while Q3 and the maximum will be farther away. For left-skewed distributions, the “mirror” is true: the maximum, Q3, and the median will be relatively close compared to the corresponding distances to Q1 and the minimum. Symmetric distributions? STA 291 Spring 2010 Lecture 5 19 Value that occurs most frequently ◦ Does not need to be near the center of the distribution Not really a measure of central tendency ◦ Can be used for all types of data (nominal, ordinal, interval) Special Cases ◦ Data Set {2, 2, 4, 5, 5, 6, 10, 11} Mode = ◦ Data Set {2, 6, 7, 10, 13} Mode = STA 291 Spring 2010 Lecture 5 20 Mean ◦ Interval data with an approximately symmetric distribution Median ◦ Interval or ordinal data Mode ◦ All types of data STA 291 Spring 2010 Lecture 5 21 Mean is sensitive to outliers ◦ Median and mode are not Why? In general, the median is more appropriate for skewed data than the mean ◦ Why? In some situations, the median may be too insensitive to changes in the data The mode may not be unique STA 291 Spring 2010 Lecture 5 22 “How often do you read the newspaper?” Response Frequency every day 969 a few times a week 452 once a week 261 less than once a week 196 Never 76 TOTAL 1954 • Identify the mode • Identify the median response STA 291 Spring 2010 Lecture 5 23 Statistics that describe variability ◦ Two distributions may have the same mean and/or median but different variability Mean and Median only describe a typical value, but not the spread of the data ◦ ◦ ◦ ◦ Range Variance Standard Deviation Interquartile Range All of these can be computed for the sample or population STA 291 Spring 2010 Lecture 5 24 Difference between the largest and smallest observation ◦ Very much affected by outliers A misrecorded observation may lead to an outlier, and affect the range The range does not always reveal different variation about the mean STA 291 Spring 2010 Lecture 5 25 Sample 1 ◦ Smallest Observation: 112 ◦ Largest Observation: 797 ◦ Range = Sample 2 ◦ Smallest Observation: 15033 ◦ Largest Observation: 16125 ◦ Range = STA 291 Spring 2010 Lecture 5 26