Statistika Dasar (1 - 3) pendahuluan

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Statistika Dasar (1 - 3) pendahuluan

  1. 1. Pertemuan 1 - 3 PENDAHULUAN Hdi Nasbey, M.Si Jurusan Fisika Fakultas Matematika dan Ilmu Pemgetahuan Alam
  2. 2. Outline <ul><li>Pendahuluan : </li></ul><ul><li>Peranan statistika dalam fisika dan Pendidikan Fisika. </li></ul>01/02/11 © 2010 Universitas Negeri Jakarta | www.unj.ac.id |
  3. 3. Learning Outcomes <ul><li>Pada akhir pertemuan ini, diharapkan mahasiswa </li></ul><ul><li>akan mampu : </li></ul><ul><li>Mahasiswa akan dapat menjelaskan tentang statistika, data, populasi, sampel, variabel , penyajian data, dan skala pengukuran. </li></ul>01/02/11 © 2010 Universitas Negeri Jakarta | www.unj.ac.id |
  4. 4. Outline Materi <ul><li>Definisi s tatistika </li></ul><ul><li>Jenis-jenis D ata </li></ul><ul><li>Pengumpulan dan Penyajian Data </li></ul><ul><li>Teknik Sampling </li></ul><ul><li>Distribusi Frekuensi </li></ul><ul><li>Skala pengukuran </li></ul>01/02/11 © 2010 Universitas Negeri Jakarta | www.unj.ac.id |
  5. 5. What is Statistics? <ul><li>Analysis of data (in short) </li></ul><ul><li>Design experiments and data collection </li></ul><ul><li>Summary information from collected data </li></ul><ul><li>Draw conclusions from data and make decision based on finding </li></ul>01/02/11 © 2010 Universitas Negeri Jakarta | www.unj.ac.id |
  6. 6. Design of Survey Research <ul><li>Choose an Appropriate Mode of Response </li></ul><ul><ul><li>Reliable primary modes </li></ul></ul><ul><ul><ul><li>Personal interview </li></ul></ul></ul><ul><ul><ul><li>Telephone interview </li></ul></ul></ul><ul><ul><ul><li>Mail survey </li></ul></ul></ul><ul><ul><li>Less reliable self-selection modes (not appropriate for making inferences about the population) </li></ul></ul><ul><ul><ul><li>Television survey </li></ul></ul></ul><ul><ul><ul><li>Internet survey </li></ul></ul></ul><ul><ul><ul><li>Printed survey in newspapers and magazines </li></ul></ul></ul><ul><ul><ul><li>Product or service questionnaires </li></ul></ul></ul>01/02/11 © 2010 Universitas Negeri Jakarta | www.unj.ac.id |
  7. 7. Reasons for Drawing a Sample <ul><li>Less Time Consuming Than a Census </li></ul><ul><li>Less Costly to Administer Than a Census </li></ul><ul><li>Less Cumbersome and More Practical to Administer Than a Census of the Targeted Population </li></ul>01/02/11 © 2010 Universitas Negeri Jakarta | www.unj.ac.id |
  8. 8. Types of Sampling Methods Quota Samples Non-Probability Samples (Convenience) Judgement Chunk Probability Samples Simple Random Systematic Stratified Cluster 01/02/11 © 2010 Universitas Negeri Jakarta | www.unj.ac.id |
  9. 9. Probability Sampling <ul><li>Subjects of the Sample are Chosen Based on Known Probabilities </li></ul>Probability Samples Simple Random Systematic Stratified Cluster 01/02/11 © 2010 Universitas Negeri Jakarta | www.unj.ac.id |
  10. 10. Variables and Data <ul><li>A variable is a characteristic that changes or varies over time and/or for different individuals or objects under consideration. </li></ul><ul><li>Examples: </li></ul><ul><ul><li>Body temperature is variable over time or (and) from person to person. </li></ul></ul><ul><ul><li>Hair color, white blood cell count, time to failure of a computer component. </li></ul></ul>01/02/11 © 2010 Universitas Negeri Jakarta | www.unj.ac.id |
  11. 11. Definitions <ul><li>An experimental unit is the individual or object on which a variable is measured. </li></ul><ul><li>A measurement results when a variable is actually measured on an experimental unit. </li></ul><ul><li>A set of measurements, called data, can be either a sample or a population. </li></ul>01/02/11 © 2010 Universitas Negeri Jakarta | www.unj.ac.id |
  12. 12. Example <ul><li>Variable </li></ul><ul><ul><li>Hair color </li></ul></ul><ul><li>Experimental unit </li></ul><ul><ul><li>Person </li></ul></ul><ul><li>Typical Measurements </li></ul><ul><ul><li>Brown, black, blonde, etc. </li></ul></ul>01/02/11 © 2010 Universitas Negeri Jakarta | www.unj.ac.id |
  13. 13. How many variables have you measured? <ul><li>Univariate data: One variable is measured on a single experimental unit. </li></ul><ul><li>Bivariate data: Two variables are measured on a single experimental unit. </li></ul><ul><li>Multivariate data: More than two variables are measured on a single experimental unit. </li></ul>01/02/11 © 2010 Universitas Negeri Jakarta | www.unj.ac.id |
  14. 14. Types of Variables 01/02/11 © 2010 Universitas Negeri Jakarta | www.unj.ac.id | Qualitative Quantitative Discrete Continuous
  15. 15. Types of Variables <ul><li>Qualitative variables measure a quality or characteristic on each experimental unit. (Categorical Data) </li></ul><ul><li>Examples: </li></ul><ul><ul><li>Hair color (black, brown, blonde…) </li></ul></ul><ul><ul><li>Make of car (Dodge, Honda, Ford…) </li></ul></ul><ul><ul><li>Gender (male, female) </li></ul></ul><ul><ul><li>State of birth (California, Arizona,….) </li></ul></ul>01/02/11 © 2010 Universitas Negeri Jakarta | www.unj.ac.id |
  16. 16. Types of Variables <ul><li>Quantitative variables measure a numerical quantity on each experimental unit. </li></ul><ul><ul><li>Discrete if it can assume only a finite or countable number of values. </li></ul></ul><ul><ul><li>Continuous if it can assume the infinitely many values corresponding to the points on a line interval . </li></ul></ul>01/02/11 © 2010 Universitas Negeri Jakarta | www.unj.ac.id |
  17. 17. Examples <ul><li>For each orange tree in a grove, the number of oranges is measured. </li></ul><ul><ul><li>Quantitative discrete </li></ul></ul><ul><li>For a particular day, the number of cars entering a college campus is measured. </li></ul><ul><ul><li>Quantitative discrete </li></ul></ul><ul><li>Time until a light bulb burns out </li></ul><ul><ul><li>Quantitative continuous </li></ul></ul>01/02/11 © 2010 Universitas Negeri Jakarta | www.unj.ac.id |
  18. 18. Graphing Qualitative Variables <ul><li>Use a data distribution to describe: </li></ul><ul><ul><li>What values of the variable have been measured </li></ul></ul><ul><ul><li>How often each value has occurred </li></ul></ul><ul><li>“ How often ” can be measured 3 ways: </li></ul><ul><ul><li>Frequency in each category </li></ul></ul><ul><ul><li>Relative frequency = Frequency/ n </li></ul></ul><ul><ul><li>(proportion in each category) </li></ul></ul><ul><ul><li>Percent = 100 x Relative frequency </li></ul></ul>01/02/11 © 2010 Universitas Negeri Jakarta | www.unj.ac.id |
  19. 19. Example <ul><li>A bag of M&M ® s contains 25 candies: </li></ul><ul><li>Raw Data: </li></ul><ul><li>Statistical Table: </li></ul>01/02/11 © 2010 Universitas Negeri Jakarta | www.unj.ac.id | Color Tally Frequency Relative Frequency Percent Red 5 5/25 = .20 20% Blue 3 3/25 = .12 12% Green 2 2/25 = .08 8% Orange 3 3/25 = .12 12% Brown 8 8/25 = .32 32% Yellow 4 4/25 = .16 16% m m m m m m m m m m m m m m m m m m m m m m m m m m m m m m m m m m m m m m m m m m m m m m m m m m
  20. 20. Graphs Bar Chart: How often a particular category was observed Pie Chart: How the measurements are distributed among the categories 01/02/11 © 2010 Universitas Negeri Jakarta | www.unj.ac.id |
  21. 21. Graphing Quantitative Variables <ul><li>A single quantitative variable measured for different population segments or for different categories of classification can be graphed using a pie or bar chart . </li></ul>A Big Mac hamburger costs $3.64 in Switzerland, $2.44 in the U.S. and $1.10 in South Africa. 01/02/11 © 2010 Universitas Negeri Jakarta | www.unj.ac.id |
  22. 22. Dotplots <ul><li>The simplest graph for quantitative data </li></ul><ul><li>Plots the measurements as points on a horizontal axis, stacking the points that duplicate existing points. </li></ul><ul><li>Example: The set 4, 5, 5, 7, 6 </li></ul>Applet 01/02/11 © 2010 Universitas Negeri Jakarta | www.unj.ac.id | 4 5 6 7
  23. 23. Stem and Leaf Plots <ul><li>A simple graph for quantitative data </li></ul><ul><li>Uses the actual numerical values of each data point. </li></ul><ul><ul><li>Divide each measurement into two parts: the stem and the leaf. </li></ul></ul><ul><ul><li>List the stems in a column, with a vertical line to their right. </li></ul></ul><ul><ul><li>For each measurement, record the leaf portion in the same row as its matching stem. </li></ul></ul><ul><ul><li>Order the leaves from lowest to highest in each stem. </li></ul></ul><ul><ul><li>Provide a key to your coding. </li></ul></ul>01/02/11 © 2010 Universitas Negeri Jakarta | www.unj.ac.id |
  24. 24. Example The prices ($) of 18 brands of walking shoes: 90 70 70 70 75 70 65 68 60 74 70 95 75 70 68 65 40 65 01/02/11 © 2010 Universitas Negeri Jakarta | www.unj.ac.id | 4 0 5 6 5 8 0 8 5 5 7 0 0 0 5 0 4 0 5 0 8 9 0 5 4 0 5 6 0 5 5 5 8 8 7 0 0 0 0 0 0 4 5 5 8 9 0 5 Reorder
  25. 25. Interpreting Graphs: Location and Spread <ul><li>Where is the data centered on the horizontal axis, and how does it spread out from the center? </li></ul>01/02/11 © 2010 Universitas Negeri Jakarta | www.unj.ac.id |
  26. 26. Tabulating and Graphing Numerical Data Numerical Data Ordered Array Stem and Leaf Display Histograms Ogive Tables 41, 24, 32, 26, 27, 27, 30, 24, 38, 21 21, 24, 24, 26, 27, 27, 30, 32, 38, 41 Frequency Distributions Cumulative Distributions Polygons 01/02/11 © 2010 Universitas Negeri Jakarta | www.unj.ac.id | 2 144677 3 028 4 1
  27. 27. Tabulating Numerical Data: Frequency Distributions <ul><li>Sort raw data in ascending order: 12, 13, 17, 21, 24, 24, 26, 27, 27, 30, 32, 35, 37, 38, 41, 43, 44, 46, 53, 58 </li></ul><ul><li>Find range: 58 - 12 = 46 </li></ul><ul><li>Select number of classes: 5 (usually between 5 and 15) </li></ul><ul><li>Compute class interval (width): 10 (46/5 then round up) </li></ul><ul><li>Determine class boundaries (limits): 10, 20, 30, 40, 50, 60 </li></ul><ul><li>Compute class midpoints: 15, 25, 35, 45, 55 </li></ul><ul><li>Count observations & assign to classes </li></ul>01/02/11 © 2010 Universitas Negeri Jakarta | www.unj.ac.id |
  28. 28. Frequency Distributions, Relative Frequency Distributions and Percentage Distributions Class Frequency 10 but under 20 3 .15 15 20 but under 30 6 .30 30 30 but under 40 5 .25 25 40 but under 50 4 .20 20 50 but under 60 2 .10 10 Total 20 1 100 Relative Frequency Percentage Data in ordered array: 12, 13, 17, 21, 24, 24, 26, 27, 27, 30, 32, 35, 37, 38, 41, 43, 44, 46, 53, 58 01/02/11 © 2010 Universitas Negeri Jakarta | www.unj.ac.id |
  29. 29. Graphing Numerical Data: The Histogram Data in ordered array: 12, 13, 17, 21, 24, 24, 26, 27, 27, 30, 32, 35, 37, 38, 41, 43, 44, 46, 53, 58 No Gaps Between Bars Class Midpoints Class Boundaries 01/02/11 © 2010 Universitas Negeri Jakarta | www.unj.ac.id |
  30. 30. Graphing Numerical Data: The Frequency Polygon Class Midpoints Data in ordered array: 12, 13, 17, 21, 24, 24, 26, 27, 27, 30, 32, 35, 37, 38, 41, 43, 44, 46, 53, 58 01/02/11 © 2010 Universitas Negeri Jakarta | www.unj.ac.id |
  31. 31. Tabulating Numerical Data: Cumulative Frequency Cumulative Cumulative Class Frequency % Frequency 10 but under 20 3 15 20 but under 30 9 45 30 but under 40 14 70 40 but under 50 18 90 50 but under 60 20 100 Data in ordered array: 12, 13, 17, 21, 24, 24, 26, 27, 27, 30, 32, 35, 37, 38, 41, 43, 44, 46, 53, 58 01/02/11 © 2010 Universitas Negeri Jakarta | www.unj.ac.id |
  32. 32. Graphing Numerical Data: The Ogive (Cumulative % Polygon) Class Boundaries ( Not Midpoints ) Data in ordered array: 12, 13, 17, 21, 24, 24, 26, 27, 27, 30, 32, 35, 37, 38, 41, 43, 44, 46, 53, 58 01/02/11 © 2010 Universitas Negeri Jakarta | www.unj.ac.id |
  33. 33. <ul><li>Skala Pengukuran : </li></ul><ul><ul><li>Nominal </li></ul></ul><ul><ul><li>Ordinal </li></ul></ul><ul><ul><li>Interval </li></ul></ul><ul><ul><li>Rasio </li></ul></ul>01/02/11 © 2010 Universitas Negeri Jakarta | www.unj.ac.id |
  34. 34. Karakteristik SkalaPengukuran 01/02/11 © 2010 Universitas Negeri Jakarta | www.unj.ac.id | Skala Pengukuran Pembedaan Kategori Urutan/ Peringkat Jarak Di antara Kategori Nilai Absolut Nominal Ya - - - Ordinal Ya Ya - - Interval Ya Ya Ya - Rasio Ya Ya Ya Ya
  35. 35. Key Concepts <ul><li>I. How Data Are Generated </li></ul><ul><li>1. Experimental units, variables, measurements </li></ul><ul><li>2. Samples and populations </li></ul><ul><li>3. Univariate, bivariate, and multivariate data </li></ul><ul><li>II. Types of Variables </li></ul><ul><li>1. Qualitative or categorical </li></ul><ul><li>2. Quantitative </li></ul><ul><li>a. Discrete </li></ul><ul><li>b. Continuous </li></ul><ul><li>III. Graphs for Univariate Data Distributions </li></ul><ul><li>1. Qualitative or categorical data </li></ul><ul><li>a. Pie charts </li></ul><ul><li>b. Bar charts </li></ul>01/02/11 © 2010 Universitas Negeri Jakarta | www.unj.ac.id |
  36. 36. Key Concepts <ul><li>2. Quantitative data </li></ul><ul><li>a. Pie and bar charts </li></ul><ul><li>b. Line charts </li></ul><ul><li>c. Dotplots </li></ul><ul><li>d. Stem and leaf plots </li></ul><ul><li>e. Relative frequency histograms </li></ul><ul><li>3. Describing data distributions </li></ul><ul><li>a. Shapes — symmetric, skewed left, skewed right, unimodal, bimodal </li></ul><ul><li>b. Proportion of measurements in certain intervals </li></ul><ul><li>c. Outliers </li></ul>01/02/11 © 2010 Universitas Negeri Jakarta | www.unj.ac.id |
  37. 37. <ul><li>Selamat Belajar Semoga Sukses. </li></ul>01/02/11 © 2010 Universitas Negeri Jakarta | www.unj.ac.id |

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