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Analisis data SMT 310

Analisis data SMT 310. retnosubekti@uny.ac.id. Motivasi. Memahami analisis eksplorasi dan konfirmasi Landasan statistika deskriptif dan inferensi Bersinergi dengan komputasi statistik untuk meng -upgrade kemampuan analisis data. Deskripsi. Penyusunan dan rangkuman data numerik

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Analisis data SMT 310

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  1. Analisis dataSMT 310 retnosubekti@uny.ac.id

  2. Motivasi • Memahamianalisiseksplorasidankonfirmasi • Landasanstatistikadeskriptifdaninferensi • Bersinergidengankomputasistatistikuntukmeng-upgrade kemampuananalisis data

  3. Deskripsi • Penyusunandanrangkuman data numerik • Penyajian data univariat • Transformasi data • Sampelacak • Statistikakonfirmasi • Analisisvariansi • Hubunganantaraduavariabel • Analisis data kategorik

  4. Referensi • Erickson, Bonnie H & Nosanchuk. 1987. Memahami Data : StatistikauntukIlmuSosial. (terjemahan RK. Sembiring & ManaseMalo). Jakarta: LP3ES • Griffiths D., Stirling W.D, Weldon K.L . 1998. Understanding Data : Principles and Practice of Statistics. Brisbane : John Willey & Sons

  5. Kontrak • Penilaian • Bobot : • Tugas : 20% • Kuis : 15% • Usip : 25% • Uas : 40%

  6. REVIEW • STATISTIKA ? • STATISTIK ? • STATISTIKA DESKRIPTIF ? • Statistikainferensi • Populasi • Sampel • Parameter • Statistik

  7. Data • Nilaiujianmetodestastistik 20 orangmahasiswaadalah: • Misalkandiketahuinilaiujiankomputasistatistika 50 mahasiswa

  8. BanyaknyapenjualanHP disuatutoko :

  9. Skalapengukuran • Nominal : • Ordinal : • Interval : • Rasio : Contoh: • Nominal: jenispekerjaan, warna • Ordinal: kepangkatan, tingkatpendidikan • Interval: tahunkalender (Masehi, Hijriyah), temperatur • (Celcius, Fahrenheit) • Rasio: berat, panjang, isi

  10. Statistikadeskriptif • Metodeataucara-cara yang digunakanuntukmeringkasdanmenyajikandata dalambentuktabel, grafikatauringkasannumerikdata.

  11. Grafik Stem-and-leaf • Untukmenunjukkanbentukdistribusi data • Data berupaangkadengan minimal duadigit • Contoh (Data penghasilanburuh): 4 3 9 5 1 1 5 5 5 6 8 9 6 0 2 3 3 4 4 4 5 5 5 6 7 7 7 8 8 9 7 1 2 2 3 4 4 5 5 8 8 3 4 9 9 2 Stem= 10, Leaf = 1

  12. Intro… Why study statistics? Make decision without complete informations Understanding population, sample Parameter, statistic Descriptive and inferential statistics

  13. glossary A population is the collection of all items of interest or under investigation N represents the population size A sample is an observed subset of the population n represents the sample size A parameter is a specific characteristic of a population Mean, Variance, Standard Deviation, Proportion, etc. A statistic is a specific characteristic of a sample Mean, Variance, Standard Deviation, Proportion, etc.

  14. Population vs. Sample Population Sample a b c d ef gh i jk l m n o p q rs t u v w x y z b c g i n o r u y Values calculated using population data are called parameters Values computed from sample data are called statistics

  15. Examples of Populations Incomes of all families living in yogyakarta All women with pregnancy problem. Grade point averages of all the students in your university …

  16. Random sampling Simple random sampling is a procedure in which each member of the population is chosen strictly by chance, each member of the population is equally likely to be chosen, and every possible sample of n objects is equally likely to be chosen The resulting sample is called a random sample

  17. Descriptive and Inferential Statistics Two branches of statistics: Descriptive statistics Collecting, summarizing, and processing data to transform data into information Inferential statistics Provide the bases for predictions, forecasts, and estimates that are used to transform information into knowledge and decision

  18. Descriptive Statistics Collect data e.g., Survey Present data e.g., Tables and graphs Summarize data e.g., Sample mean =

  19. Inferential Statistics Estimation e.g., Estimate the population mean weight using the sample mean weight Hypothesis testing e.g., Test the claim that the population mean weight is 120 pounds Inference is the process of drawing conclusions or making decisions about a populationbased on sample results

  20. The Decision Making Process Decision Knowledge Experience, Theory, Literature, Inferential Statistics, Computers Information Descriptive Statistics, Probability, Computers Begin Here: Identify the Problem Data

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