[PDF] Quartiles within SAS



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median, quartiles, quantiles, order statistics, lower forth

the first quartile is located at the third smallest value, since there are 5 data values in the lower half of the sample So, Q 1 = x (3) =4 5isthefirstsamplequartileandQ 3 = x (8) = 10 1 In the above example, I used an approach similar to the approach given on page 39 of the textbook



Percentiles, Quartiles and Deciles

b) Calculate the first quartile c) Calculate the third quartile d) Calculate the 4th decile Ordered data set: 111 131 147 151 151 151 + 0 75(179­151) = 172 179 182 190 197 197 + 0 2(201­197) = 197 8 201 209 209+ 0 5(234­209) = 221 5 234 286 294 295 310 319 319 + 0 25(337­319) = 323 5 337 353 377 377 439



Quartile - DePaul University

Quartile From Wikipedia, the free encyclopedia In descriptive statistics, a quartile is any of the three values which divide the sorted data set into four equal parts, so that each part represents one fourth of the sampled population In epidemiology, the quartiles are the four ranges defined by the three values discussed here Definitions



Quartiles within SAS

quartile is the part that contains the next-lowest data values Similarly the upper quartile can also be the cut-off value (referred to as Q3 or UQ) between the upper middle quartile values and upper quartile values, and the lower quartile can also be the cut-off value (referred to as Q1 or LQ)



Quantiles and Quantile Based Plots - Auckland

• The 25th percentile is known as the lower quartile • The 50th percentile is known as the median • The 75th percentile is known as the upper quartile It is more common in statistics to refer to quantiles These are the same as percentiles, but are indexed by sample fractions rather than by sample percentages



2 Descriptive Statistics: Mean, Median, Mode and Skewness

First quartile comes after 18,854 / 4 = 4713 5 cases This point comes within the first group because there are 9,674 cases within this group; so first quartile = 1 Second quartile = median = 1 Third quartile comes after 18,854 × 3/4 cases = 14,140 5 cases This point comes in the third group: (9,674 + 4,351) < 14,140 5 < (9,674 + 4,351



Chapitre - auvraymathfileswordpresscom

quartile est Q1 = 3 et le troisième Q3 = 9 Dé nition 11 6 art c L'é quartile inter e I d'une série statistique est la e enc di ér e entr le oisième tr quartile et le emier pr quartile de ette c série: e I = Q3 −Q1 L'étendue e d'une série statistique est la e enc di ér e entr plus ande gr valeur x p et etite p valeur x1 de ette c



wwwmathsenlignenet STATISTIQUES EXERCICES 4A

Moyenne : eÉtendue : 1 r quartile : Médiane : 3ème quartile : tracer le diagramme en boite de cette série EXERCICE 4A 2 Cette série statistique représente les poids (en kg) de 28 personnes 67 78 91 75 64 76 75 87 69 75 72 78 70 81 75 86 76 82 83 75 79 80 101 89 92 73 76 76 Déterminer pour cette série :



Calcul - Statistiques

222) 2)) ) Déterminer le 1Déterminer le 1 ereerrer et le 3et le 3 eeee quartile d’une série statistiquequartile d’une série statistiquequartile d’une série statistique • Ranger les nombres de la série dans l’ordre croissant

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Quartiles within SAS

PhUSE 2012

1

Paper PP16

Quartiles within SAS

Jorine Putter, Quanticate, Oxford, United Kingdom

Liza Faber, Quanticate, Bloemfontein, South Africa

ABSTRACT

Many times during the reporting of a study, programmers blindly report whichever statistics are generated by default

by the specific SAS® procedure, often without reading up on the SAS documentation and thus knowing the default

behaviour of the procedure.

Unbeknownst to the programmer, the reported statistics may not accurately reflect what the statistician is expecting to

see.

The purpose of this paper is to educate programmers on the different methods for calculating Q1 and Q3, and

ensuring the statistician has clearly documented the appropriate method to use.

The paper will explore the different methods used by specific SAS procedures, highlight any differences between the

procedures, and also demonstrate how these methods can be set within specific SAS procedures. Comparisons with

other software, such as Excel will also be included.

INTRODUCTION

To understand the different methods used to calculate quartiles, you first need to understand what a quartile is. When

arranging a data set of values in ascending order and divide them into four roughly equal parts, these four parts are

called quartiles.

The upper quartile is the part that contains the highest values, the upper middle quartile is the part that contains the

next-highest data values, the lower quartile is the part that contains the lowest data values and the lower middle

quartile is the part that contains the next-lowest data values.

Similarly the upper quartile can also be the cut-off value (referred to as Q3 or UQ) between the upper middle quartile

values and upper quartile values, and the lower quartile can also be the cut-off value (referred to as Q1 or LQ)

between the lower middle quartile and lower quartile values.

Definition according to SAS

1 : ‘"A percentile is a value at or below which a given percentage or fraction of the variable

values lie. For a set of measurements arranged in order of magnitude, the p-th percentile is the value that has p% of

the measurements below it and (100-p)% above it. Thus, the 20th percentile is the value such that one fifth of the

data lie below it. It is higher than 20% of the data values and lower than 80% of the data values."" The concept is simple; however the execution can be much more complicated.

Throughout this paper, we will look at different statistical methods of determining the Q1 and Q3 cut-off values,

making use of examples through SAS and other statistical software packages available. 1

PhUSE 2012

SOME STATISTICAL METHODS Name

John Tukey

Moore & McCabe

(M&M)

Mendenhall & Sincich

(M&S)

Minitab

Freud & Perles

(Excel)

Method

Find the median of the

entire set

If the number of data

points in the entire set is odd, find the median of the lower and upper halves by including the overall median as a data point in both halves

If the number of data

points in the entire set is even, find the median of the lower and upper halves by excluding the overall median as a data point in both halves

Find the median of the

entire set

Find the median of the

lower and upper halves, excluding the overall median as a data point in both halves

L = (¼)(n+1), rounded to

the nearest integer value (however if L falls exactly halfway between to integers, round up)

U = (¾)(n+1), rounded to

the nearest integer value (however if U falls exactly halfway between to integers, round down)

Q1 = L

th element in the entire set Q3 = U th element in the entire set

L = (¼)(n+1), instead of

rounding to the nearest integer, use linear interpolation between the closest data points

U = (¾)(n+1), instead of

rounding to the nearest integer, use linear interpolation between the closest data points

Q1 = L

th element in the entire set Q3 = U th element in the entire set

L = (¼)(n+3), instead of

rounding to the nearest integer, use linear interpolation between the closest data points

U = (¼)(3n+1), instead

of rounding to the nearest integer, use linear interpolation between the closest data points

Q1 = L

th element in the entire set Q3 = U thquotesdbs_dbs2.pdfusesText_3