Kuswanto 2012. Ukuran keragaman Dari tiga ukuran pemusatan, belum dapat memberikan deskripsi yang lengkap bagi suatu data. Dari tiga ukuran pemusatan,

Slides:



Advertisements
Similar presentations
Measures of Location and Dispersion
Advertisements

Quantitative Methods Topic 5 Probability Distributions
Review of Descriptive Graphs and Measures Here is a quick review of what we have covered so far. Pie Charts Bar Charts Pareto Tables Dotplots Stem-and-leaf.
Find the average of each set
EuroCondens SGB E.
Copyright © 2014 by McGraw-Hill Higher Education. All rights reserved.
STATISTICS Linear Statistical Models
Describing Data: Measures of Dispersion
Describing Data: Measures of Central Tendency
Population vs. Sample Population: A large group of people to which we are interested in generalizing. parameter Sample: A smaller group drawn from a population.
CALENDAR.
1 Week 1 Review of basic concepts in statistics handout available at Trevor Thompson.
Lecture 7 THE NORMAL AND STANDARD NORMAL DISTRIBUTIONS
Multiple-choice example
The 5S numbers game..
Chapter 3 Properties of Random Variables
DESCRIPTIVE STATISTICS
The basics for simulations
Get your calculator! Describing Data
Frequency Tables and Stem-and-Leaf Plots 1-3
Data Analysis 53 Given the two histograms below, which of the following statements are true?
Chapter 2: Frequency Distributions
Statistics Review – Part I
Introduction Our daily lives often involve a great deal of data, or numbers in context. It is important to understand how data is found, what it means,
Quantitative Analysis (Statistics Week 8)
When you see… Find the zeros You think….
2011 WINNISQUAM COMMUNITY SURVEY YOUTH RISK BEHAVIOR GRADES 9-12 STUDENTS=1021.
Before Between After.
2011 FRANKLIN COMMUNITY SURVEY YOUTH RISK BEHAVIOR GRADES 9-12 STUDENTS=332.
Summary Statistics When analysing practical sets of data, it is useful to be able to define a small number of values that summarise the main features present.
Static Equilibrium; Elasticity and Fracture
Measures of Dispersion. Here are two sets to look at A = {1,2,3,4,5,6,7} B = {8,9,10,11,12,13,14} Do you expect the sets to have the same means? Median?
Tutorial: Understanding the normal curve. Gauss Next mouse click.
Lial/Hungerford/Holcomb/Mullins: Mathematics with Applications 11e Finite Mathematics with Applications 11e Copyright ©2015 Pearson Education, Inc. All.
Simple Linear Regression Analysis
Section2.3 – Measures of Central Tendency
Measures of Variation 1.)Range ( R ) - the difference in value between the highest(maximum) and the lowest(minimum) observation. R = Highest – Lowest 2.)Mean.
Descriptive Statistics
Calculating & Reporting Healthcare Statistics
Descriptive Statistics – Central Tendency & Variability Chapter 3 (Part 2) MSIS 111 Prof. Nick Dedeke.
Ka-fu Wong © 2004 ECON1003: Analysis of Economic Data Lesson2-1 Lesson 2: Descriptive Statistics.
Descriptive Statistics
Analysis of Research Data
Data observation and Descriptive Statistics
1 Chapter 4: Variability. 2 Variability The goal for variability is to obtain a measure of how spread out the scores are in a distribution. A measure.
Graphical Summary of Data Distribution Statistical View Point Histograms Skewness Kurtosis Other Descriptive Summary Measures Source:
Statistics Recording the results from our studies.
Measures of Variability OBJECTIVES To understand the different measures of variability To determine the range, variance, quartile deviation, mean deviation.
Measures of Central Tendency and Dispersion Preferred measures of central location & dispersion DispersionCentral locationType of Distribution SDMeanNormal.
Describing Behavior Chapter 4. Data Analysis Two basic types  Descriptive Summarizes and describes the nature and properties of the data  Inferential.
Measures of Dispersion & The Standard Normal Distribution 2/5/07.
Measures of Dispersion & The Standard Normal Distribution 9/12/06.
Skewness & Kurtosis: Reference
An Introduction to Statistics. Two Branches of Statistical Methods Descriptive statistics Techniques for describing data in abbreviated, symbolic fashion.
Measures of Dispersion
Numerical Measures of Variability
Business Statistics, 4e, by Ken Black. © 2003 John Wiley & Sons. 3-1 Business Statistics, 4e by Ken Black Chapter 3 Descriptive Statistics.
Outline of Today’s Discussion 1.Displaying the Order in a Group of Numbers: 2.The Mean, Variance, Standard Deviation, & Z-Scores 3.SPSS: Data Entry, Definition,
1 Day 1 Quantitative Methods for Investment Management by Binam Ghimire.
Analysis and Empirical Results
Chapter 3 Describing Data Using Numerical Measures
Descriptive measures Capture the main 4 basic Ch.Ch. of the sample distribution: Central tendency Variability (variance) Skewness kurtosis.
CENTRAL MOMENTS, SKEWNESS AND KURTOSIS
Descriptive Statistics
Univariate Descriptive Statistics
Chapter 3 Describing Data Using Numerical Measures
Measures of Dispersion
MBA 510 Lecture 2 Spring 2013 Dr. Tonya Balan 4/20/2019.
Advanced Algebra Unit 1 Vocabulary
Presentation transcript:

Kuswanto 2012

Ukuran keragaman Dari tiga ukuran pemusatan, belum dapat memberikan deskripsi yang lengkap bagi suatu data. Dari tiga ukuran pemusatan, belum dapat memberikan deskripsi yang lengkap bagi suatu data. Perlu juga diketahui seberapa jauh pengamatan- pengamatan tersebut menyebar dari rata-ratanya. Perlu juga diketahui seberapa jauh pengamatan- pengamatan tersebut menyebar dari rata-ratanya. Ada kemungkinan diperoleh rata-rata dan median yang sama, namun berbeda keragamannya. Ada kemungkinan diperoleh rata-rata dan median yang sama, namun berbeda keragamannya. Beberapa ukuran keragaman yang sering kita temui adalah range (rentang=kisaran=wilayah), simpangan (deviasi), varian (ragam), simpangan baku (standar deviasi) dan koefisien keragaman. Beberapa ukuran keragaman yang sering kita temui adalah range (rentang=kisaran=wilayah), simpangan (deviasi), varian (ragam), simpangan baku (standar deviasi) dan koefisien keragaman.

Measures of Dispersion and Variability These are measurements of how spread the data is around the center of the distribution f X f X

2. DEVIATION DEVIASI = SIMPANGAN You could express dispersion in terms of deviation from the mean, however, a sum of deviations from the mean will always = 0. i.e. (X i - X) = 0 So, take an absolute value to avoid this Problem – the more numbers in the data set, the higher the SS

1.Range Kisaran = Rentang difference between lowest and highest numbers Place numbers in order of magnitude, then range = X n - X 1. Range = = = X 1 = X 2 = X 3 = X 4 = X 5 Problem - no information about how clustered the data is

Sample mean deviation = | X i - X | n Essentially the average deviation from the mean 3. Mean Deviation = Simpangan Rerata 4. Variance = Ragam Sample SS = (X i - X) 2 = SS is much more common than mean deviation Another way to get around the problem of zero sums is to square the deviations. Known as sum of squares or SS Xi 2 - ( Xi) 2 /n

Example = X 1 = X 2 = X 3 = X 4 = X 5 X = 3.2 Sample SS = (X i - X) 2 SS = ( ) 2 + ( ) 2 + ( ) 2 + ( ) 2 + (5 -3.2) 2 = = 6.8 Problem – the more numbers in the data set, the higher the SS

The mean SS is known as the variance Population Variance ( 2 ): 2 = (X i - ) 2 N This is just SS N Problem - units end up squared Our best estimate of 2 is sample variance (s 2 ): S 2 = (X i - X) 2 n - 1 Note : divide by n-1 known as degrees of freedom Xi 2 - ( Xi) 2 /n n - 1 =

5. Standard Deviation (Standar Deviasi) => square root of variance = (X i - ) 2 N For a population: For a sample: s = (X i - X ) 2 n - 1 = 2 s = s 2

Example = X 1 = X 2 = X 3 = X 4 = X 5 X = 3.2 s = (X i - X ) 2 n - 1 s = ( ) 2 + ( ) 2 + ( ) 2 + ( ) 2 + (5 -3.2) = =

6. Coefficient of Variation = Koefisien Keragaman = KK (V or sometimes CV ): CV = s X Variance (s 2 ) and standard deviation (s) have magnitudes that are dependent on the magnitudes of the data. The coefficient of variation is a relative measure, so variability of different sets of data may be compared (stdev relative to the mean) Note that there are no units – emphasizes that it is a relative measure Sometimes expressed as a % X 100%

Example: = X 1 = X 2 = X 3 = X 4 = X 5 s = g CV = s X X = 3.2 g CV = g 3.2 g CV = or CV = 40.75% (X 100%) Attention there is not any UNIT, or %

8. The Normal Distribution (Distribusi Normal) : There is an equation which describes the height of the normal curve in relation to its standard dev ( ) X % 95.44% 99.73% f

ƒ μ = 0 Normal distribution with σ = 1, with varying means μ = 1 μ = 2 5 If you get difficulties to keep this term, read statistics books

ƒ σ = 1 σ = 1.5 σ = 2 Normal distribution with μ = 0, with varying standard deviations

9. Symmetry and Kurtosis Symmetry means that the population is equally distributed around the mean i.e. the curve to the right side of the mean is a mirror image of the curve to the left side ƒ Mean, median and mode

Data may be positively skewed (skewed to the right) Symmetry ƒ ƒ Or negatively skewed (skewed to the left) So direction of skew refers to the direction of longer tail

Symmetry ƒ mode median mean

ƒ Kurtosis refers to how flat or peaked a curve is (sometimes referred to as peakedness or tailedness) The normal curve is known as mesokurtic ƒ A more peaked curve is known as leptokurtic A flatter curve is known as platykurtic

Latihan dan diskusi 1. Banyaknya buah pisang yang tersengat hama dari 16 tanaman adalah 4, 9, 0, 1, 3, 24, 12, 3, 30, 12, 7, 13, 18, 4, 5, dan 15. Dengan menganggap data tersebut sebagai contoh, hitunglah varian, simpangan baku dan koefisien keragamannya. Statistik mana yang paling tepat untuk menggambarkan keragaman data tersebut? 2. To study how first-grade students utilize their time when assigned to a math task, researcher observes 24 students and records their time off task out of 20 minutes. Times off task (minutes) : 4, 0, 2, 2, 4, 1, 4, 6, 9, 7, 2, 7, 5, 4,13, 7, 7, 10, 10, 0, 5, 3, 9 and 8. For this data set, find : a) Mean and standard deviation, median and range b) Display the data in the histogram plot, dot diagram and also stem-and-leaf diagram c) Determine the intervals x ± s, x ± 2s, x ± 3s d) Find the proportion of the meausurements that lie in each of this intervals. e) Compare your finding with empirical guideline of bell-shaped distribution

3. The data below were obtained from the detailed record of purchases over several month. The usage vegetables (in weeks) for a household taken from consumer panel were (gram) : a. Plot a histogram of the data! a. Plot a histogram of the data! b. Find the relative frequency of the usage time that did not exceed 80. b. Find the relative frequency of the usage time that did not exceed 80. c. Calculate the mean, variance and the standard deviation c. Calculate the mean, variance and the standard deviation d. Calculate the median and quartiles. d. Calculate the median and quartiles. 4. The mean of corn weight is 278 g by ear and deviation standard is 9,64 g, and than we have 10 ears. If they are gotten from ten different fields, mean of plant height is Rp. 1200,- and its deviation standard is Rp 90,-, which one have more homogenous, the weight of corn ear or the plant height? Explain your answer! Verify your results by direct calculation with the other data.

5. The employments salary at seed company, abbreviated, as follows : 18, 15, 21, 19, 13, 15, 14, 23, 18 and 16 rupiah. If these abbreviation is real salary divide Rp ,-, find the mean, variance and deviation standard of them. 6. Computer-aided statistical calculations. Calculation of the descriptive statistic such as x and s are increasingly tedious with large data sets. Modern computers have come a long way in alleviating the drudgery of hand calculation. Microsoft Exel, Minitab or SPSS are three of computing packages those are easy accessible to student because its commands are in simple English. Find these programs and install its at your computers. Bellow main and sub menu of Microsoft Exel, Minitab and SPSS program. Use these software to find x, s, s 2, and coefisien of variation (CV) for data set in exercise b. Histogram and another illustration can also be created.

7. Some properties of the standard deviation a) if a fixed number c is added to all measurements in a data set, will the deviations (x i - x) remain changed? And consequently, will s² and s remain changed, too? Take data sample. b) If all measurements in a data set are multiplied by a fixed number d, the deviation (x i - x) get multiplied by d. Is it right? What about the s² and s? Take data sample. c) Apply your computer software to explain your data sample. Verify your results by other data.