Ulasan MOOC: Intro to Inferential Statistics – Udacity

Review

Judul/Tautan:

Intro to Inferential Statistics – Udacity

Oleh:

Katie Kormanik – Udacity

Format:

Kursus online (MOOC) dengan kuis interaktif.

Durasi:

2 bulan

    • Dengan asumsi rata-rata komitmen belajar 6-8 jam per minggu
    • Waktu belajar fleksibel
    • Dengan belajar waktu penuh Anda bisa menyelesaikan dalam 1-2 minggu.

Tingkat

Pemula

Biaya

Gratis

Total Durasi Video

6 jam 45 menit.

Penilaian Saya

starf16starf16starf16starf16stare16 (4 dari 5)

Persyaratan

Lingkup Materi

Statistik inferensial memungkinkan kita menarik kesimpulan dari data yang mungkin tidak terlihat secara kasat mata. Topik yang dibahas terutama adalah yang berkaitan dengan hypothesis testing, yaitu apakah suatu sampel berbeda secara signifikan dari sampel lain atau berkorelasi dengan sampel lain, dan bagaimana mengukur perbedaan atau korelasi ini.

Topik yang diajarkan antara lain:

  • starf16 Hypothesis testing dengan Z-test, untuk mengetahui apakah suatu sampel berbeda secara signifikan dengan populasinya.
  • starf16 Hypothesis testing dengan T-test baik untuk samples yang dependen ataupun independen, untuk mengetahui apakah suatu sampel berbeda secara signifikan dengan sampel lainnya, tanpa harus mengetahui parameter dari populasi (mean dan standard deviation).
  • starf16 ANOVA (Analysis of Variance) atau F-test,  untuk menganalisa dua atau lebih grup (samples) dan mengetahui apakah ada grup yang berbeda secara signifikan dari grup lainnya dan kalau ada mengetahui grup mana yang berbeda ini.
  • starf16 Correlation, untuk menganalisa apakah perbedaan nilai di suatu grup (misalnya y) dapat dijelaskan oleh perbedaan nilai di grup lainnya (misalnya x).
  • starf16 Linear regression dengan menggunakan hasil proses correlation di atas (tanpa harus melalui proses belajar ala machine learning).
  • starf16 Chi-square, baik untuk goodness-of-fit maupun independent-test. Goodness-of-fit test dipakai untuk menentukan apakah dua grup sama/berbeda secara signifikan. Sedangkan independent-test dipakai untuk menentukan apakah dua variabel mempunyai relasi atau tidak.

Penilaian untuk lingkup materi: starf16starf16starf16starf16starf16

Pengajaran

  • starf16 Pengajaran sangat jelas, dengan alur yang memastikan Anda benar-benar mengerti apa yang dibahas. Suatu konsep sering akan ditanyakan secara berulang-ulang di kuis interaktif. Walaupun kadang terkesan lambat dan membosankan, tapi seringnya hal ini dapat mendeteksi kalau kita ternyata masih kurang mengerti atas konsep yang diajarkan.
  • stare16 Ada materi PDF yang bisa diunduh, tapi kualitasnya kurang bagus (dalam artian, materi yang ditulis di PDF sangat sedikit dibanding materi yang diajarkan).

Penilaian untuk pengajaran: starf16starf16starf16starf16starf16

Pemrograman

  • Tidak dibutuhkan praktek pemrograman dalam kursus ini.

Dukungan

  • stare16 Dukungan forum di Udacity untuk kursus-kursus gratis bisa dibilang sangat buruk.

Penilaian untuk dukungan: starf16starf16stare16stare16stare16

Ikhtisar

Saya mengikuti kursus ini untuk persiapan mengikuti Data Analyst NanoDegree, yang pra-review-nya saya tulis di sini.

Kali ini saya lebih serius belajar di kursus. Awalnya saya mencoba untuk membuat semacam catatan kuliah, agar di kemudian hari saya bisa me-review pelajarannya kalau lupa. Tapi setelah beberapa hari, saya punya ide yang lebih bagus, yaitu kenapa tidak bikin programnya sekalian.

Akhirnya saya buatlah repository di GitHub, https://github.com/stosia/indoml. Untuk kursus ini, programnya ada di https://github.com/stosia/indoml/tree/master/src/stat, dalam bahasa Python.

Tujuan membuat program ini ada dua, pertama agar lain kali saya bisa melihat topiknya kalau lupa, dan kedua agar bisa menjawab kuis-kuis di kursus dengan lebih menyenangkan. Karena dari pengalaman di kursus statistik sebelumnya oleh pengajar yang sama, kuis-kuisnya bisa membuat kursusnya membosankan karena kita harus banyak memakai kalkulator.

Dengan adanya program, maka menjawab kuis jadi menyenangkan, karena kita sambil mengetes kebenaran program kita.

Tapi akibatnya waktu mengikuti kursus ini jadi lebih lama tentunya, karena saya tidak hanya mengikuti kursusnya saja tapi juga sambil membuat programnya. Total saya butuh hampir dua minggu untuk menyelesaikan kursus ini (dipotong menginap di rumah sakit selama 4 hari).

Secara umum kursus ini sangat bagus, topik-topik yang diajarkan adalah topik-topik yang saya sering dengar ketika orang melakukan analisis data, jadi semoga pelajarannya relevan untuk topik analisa dari. Penjelasannya bagus sampai kita mengerti, dan lalu dipastikan lagi melalui pertanyaan di kuis. Kualitas slides-nya bagus sekali. Pengajar benar-benar mempersiapkan materinya dengan baik.

Cuma dari pengalaman saya, cukup sering saya harus memainkan ulang video-video sebelumnya untuk mencari jawaban yang dicari. Mungkin karena saya sambil menulis program, sehingga saya pingin benar-benar mengerti topiknya, sehingga saya sering harus putar ulang pelajaran-pelajarannya lagi untuk mencari informasi yang saya butuhkan. Mungkin karena informasi yang diberikan hanya diucapkan saja dan tidak dituliskan dalam slide, sehingga kadang hal itu terlewatkan.

Juga ada satu-dua formula yang kurang dijelaskan sampai tuntas. Yang saya ingat terakhir adalah tentang Cramer’s V di topik Chi-Square. Penjelasan tentang bagaimana interpretasi dari nilai Cramer’s V ini sangatlah dangkal.

Namun demikian secara umum kursus ini sangat bagus, dalam artian topiknya bagus dan cara pengajarannya bagus sekali. Saya cukup merekomendasikan kursus ini kalau Anda ingin belajar inferential statistic.

Silabus

Total durasi video: 6 jam 45 menit.

  1. Google Spreadsheet Tutorial [08:15]
    1. Tutorial [08:15]
  2. Introduction and Lesson 7 Review [17:31]
    1. Laurens Intro Video [00:39]
    2. Intro [01:08]
    3. Klout [02:12]
    4. Klout Parameters [02:21]
    5. Klout Sampling Distribution Mean [00:36]
    6. Klout Sampling Distribution SD [00:27]
    7. Sampling Distribution Shape [00:42]
    8. What Do You Get with a Good Klout Score [01:55]
    9. Location of Mean on Distribution [01:35]
    10. Probability of Obtaining Mean [01:19]
    11. Does Low Probability Causation [00:18]
    12. Increase Sample Size [00:48]
    13. Location of Mean [00:38]
    14. Probability of Mean [01:01]
    15. Something Fun [01:52]
  3. Lesson 08: Estimation [54:41]
    1. Summary [02:07]
    2. Mean of Treated Population [00:46]
    3. Population Mean vs Sample Mean [01:04]
    4. Percent of Sample Means [01:07]
    5. Approximate Margin of Error [01:24]
    6. Interval Estimate for Population Mean [02:47]
    7. Confidence Interval Bounds [02:06]
    8. Exact Z-Scores [01:57]
    9. Sampling Distribution [00:40]
    10. 95 CI with Exact Z-Scores [03:06]
    11. Generalize Point Estimate [01:03]
    12. Generalize CI [03:43]
    13. CI Range for Larger Sample Size [01:19]
    14. CI When n 250 [01:25]
    15. Bigger Sample, Smaller CI [01:23]
    16. Z for 98 CI [01:29]
    17. Find 98 CI [02:14]
    18. Critical Values of Z [01:20]
    19. Engagement Ratio [03:40]
    20. Hypothesis Testing Song [01:25]
    21. Point Estimate Engagement Ratio [00:54]
    22. Standard Error [01:29]
    23. CI Bounds [04:51]
    24. Margin of Error [01:20]
    25. Rate Engagement and Learning [02:00]
    26. Results from Sample [01:16]
    27. What Statistics [01:31]
    28. Sampling Distributions [00:58]
    29. Z-Scores of Sample Means [01:21]
    30. Probability Sample Mean Is at Least [01:02]
    31. What Does This Mean [01:07]
    32. Wrap-Up [00:47]
  4. Lesson 09 Hypothesis Testing [49:56]
    1. Likely or Unlikely [00:45]
    2. Alpha Levels [01:59]
    3. Z-Critical Value 005 [01:36]
    4. Critical Values 001 [00:33]
    5. Critical Values 0001 [00:54]
    6. Critical Regions [02:15]
    7. Significance [01:43]
    8. Darts [01:22]
    9. Z-Score [02:09]
    10. Two-Tailed Critical Values 005 [02:09]
    11. Two-Tailed Test [01:55]
    12. Two-Tailed Probability [00:23]
    13. Two-Tailed Critical Values 001 [01:02]
    14. Two-Tailed Critical Values 0001 [00:38]
    15. Hypotheses [02:31]
    16. Fail to Reject the Null [01:09]
    17. Evidence to Reject the Null [00:21]
    18. Mean and SD [03:17]
    19. Null Hypothesis [01:01]
    20. Alternative Hypothesis [00:34]
    21. One tailed or two tailed [01:49]
    22. Conduct Hypothesis Test [02:18]
    23. Critical Values 005 [00:39]
    24. Z-Score of Sample Mean [00:56]
    25. Results of Hypothesis Test [00:43]
    26. Increase Sample Size [00:40]
    27. Reject or Fail to Reject [00:48]
    28. Probability of Obtaining Mean [02:09]
    29. Decision Errors [02:32]
    30. Hot Beverage [01:55]
    31. Raining [01:24]
    32. What Happened [04:32]
    33. Prone to Misinterpretations [00:18]
    34. To Finish This Lesson [00:05]
    35. Hypothesis Testing [00:49]
    36. Increase Engagement [00:03]
  5. Lesson 10a t-Tests, Part 1 [48:21]
    1. t-Distribution [02:37]
    2. Guinness [00:17]
    3. Degrees of Freedom [01:24]
    4. DF – Choose n Numbers [00:26]
    5. DF – Add to 10 [01:37]
    6. DF – Marginal Totals [02:11]
    7. DF – Sample SD [02:09]
    8. t-Table [01:32]
    9. One-Tailed t-Test [00:25]
    10. Two-Tailed t-Test [01:01]
    11. Bounds of Area [01:19]
    12. Affect t-Statistic [02:24]
    13. One-Sample t-Test [01:31]
    14. Increase t [00:54]
    15. Finches [02:07]
    16. Finches – n and DF [00:15]
    17. Finches – Mean and s [01:04]
    18. Finches – Find t-Statistic [00:55]
    19. Finches – Decision [01:03]
    20. P-Value [01:27]
    21. Visualize P-Value [00:26]
    22. Find P-Value [02:24]
    23. Rent – t-Critical Values [01:27]
    24. Rent – t-Statistic [00:24]
    25. Rent – Decision [00:25]
    26. Rent – Cohens d [01:00]
    27. Rent – CI [00:53]
    28. Rent – Find CI [01:20]
    29. Rent – Margin of Error [00:43]
    30. Rent – Increase n [01:37]
    31. Dependent Samples [01:36]
    32. Keyboards [02:00]
    33. Keyboards Point Estimate for Difference [00:26]
    34. Keyboards – SD of Differences [01:21]
    35. Keyboards – t-Statistic [00:24]
    36. Keyboards – t-Critical Values [00:23]
    37. Keyboards – Decision [00:31]
    38. Keyboards – Cohens d [00:22]
    39. Keyboards – CI for Dependent Samples [01:13]
    40. Notation for Difference [01:11]
    41. Types of Designs [01:37]
  6. Lesson 10b t-Tests, Part 2 [26:34]
    1. Effect Size [00:42]
    2. Everyday Meaning [00:50]
    3. Types of Effect-Size Measures [01:12]
    4. Statistical Significance [02:27]
    5. Cohens d [01:27]
    6. r2 [01:37]
    7. Compute r2 [01:35]
    8. Report Results [02:51]
    9. Report CI Results [00:24]
    10. Report CI Results 2 [00:34]
    11. Report Results Effect Size [00:56]
    12. One-Sample t-Test [01:18]
    13. Mu [01:08]
    14. Dependent Variable [00:21]
    15. Treatment [00:26]
    16. Null Hypothesis [00:24]
    17. Alternative Hypothesis [00:31]
    18. Hypotheses [01:06]
    19. Which-Tailed Test [00:41]
    20. Degrees of Freedom [00:16]
    21. t-Critical [00:25]
    22. SEM [00:52]
    23. Mean Difference [00:21]
    24. t-Statistic [00:22]
    25. Critical Region [00:39]
    26. P-Value [01:15]
    27. Statistically Significant [00:13]
    28. Meaningful Results [00:23]
    29. Margin of Error [00:26]
    30. Compute CI [00:52]
  7. Lesson 11 t-Tests, Part 3 [31:57]
    1. Independent Samples [03:32]
    2. Standard Error [03:44]
    3. Meal Prices [01:07]
    4. Average Meal Price [00:23]
    5. SD for Meal Price [00:42]
    6. Meal Price SEM [00:36]
    7. Meal Price t-Statistic [00:59]
    8. Calculate t-Statistic [00:46]
    9. t-Critical Values [00:55]
    10. Gettysburg or Wilma [01:11]
    11. Acne Medication [00:49]
    12. Acne Medication t-Statistic [00:47]
    13. Acne Medication – t-Critical Values [00:34]
    14. Acne Medication – Decision [00:26]
    15. Who Has More Shoes [01:05]
    16. Mean Number of Shoes [02:17]
    17. Shoes – Standard Error [00:35]
    18. Shoes – t-Statistic [00:27]
    19. Shoes – Decision [01:10]
    20. Shoes – 95 CI [01:17]
    21. Shoes – Calculate CI [01:04]
    22. Gender and Shoes [01:17]
    23. Pooled Variance Sum of Squares [01:59]
    24. Calculate Pooled Variance [00:18]
    25. Corrected Standard Error [00:29]
    26. t-Statistic [00:30]
    27. t-Critical and Decision [00:59]
    28. Assumptions [01:59]
  8. Lesson 12 One-Way ANOVA [31:48]
    1. Intuition [01:03]
    2. Number of t-Tests [01:48]
    3. Extended t-Test Numerator [03:16]
    4. Grand Mean [03:17]
    5. Between-Group Variability [01:26]
    6. Significantly Different Means [01:49]
    7. Sample Variability and Significance [00:32]
    8. ANOVA [00:46]
    9. Hypotheses [01:41]
    10. Within-Group Variability [00:40]
    11. F-Ratio [02:06]
    12. Visualize Statistical Outcome [01:07]
    13. Formalize Within-Group Variability [02:13]
    14. Formula for F-Ratio [00:32]
    15. Degrees of Freedom [01:25]
    16. Total Variation [00:43]
    17. F-Distribution [00:43]
    18. F-Distribution Shape [01:32]
    19. Table for F-Critical [00:29]
    20. Sample Means and Grand Mean [00:35]
    21. SS Between [00:20]
    22. SS Within [00:57]
    23. Mean Squares [00:25]
    24. F-Statistic [00:31]
    25. F-Critical [00:49]
    26. Decision [01:03]
  9. Lesson 13 ANOVA, Continued [28:24]
    1. Cows and Food [01:40]
    2. Grand Mean [00:37]
    3. Group Means [00:12]
    4. SS Between [01:35]
    5. SS Within [01:24]
    6. Degrees of Freedom [00:42]
    7. Mean Squares [00:18]
    8. F-Statistic [00:07]
    9. F-Critical and Decision [01:21]
    10. Deviation from Grand Mean [00:25]
    11. SS Total [00:32]
    12. Conclusion [00:09]
    13. Multiple Comparison Tests [02:34]
    14. Tukeys HSD [00:49]
    15. Which Differences Are Significant [00:59]
    16. Cohens d for Multiple Comparisons [01:05]
    17. 2 [00:51]
    18. Calculate 2 [00:42]
    19. Range of 2 [01:01]
    20. Software Output [02:35]
    21. Missing Mean Differences [01:34]
    22. Different Sample Sizes [00:47]
    23. MS and F [00:31]
    24. Proportion Due to Drug Type [00:39]
    25. Power [02:13]
    26. ANOVA Assumptions and Wrap-Up [03:02]
  10. Lesson 14 Correlation [27:43]
    1. Relationships [01:13]
    2. The Variables x and y [01:08]
    3. Show Relationship [00:32]
    4. Scatterplot [01:03]
    5. Stronger Relationship [00:56]
    6. As x Increases [01:21]
    7. Strength and Direction [00:51]
    8. Correlation Coefficient [02:18]
    9. Match with r [00:59]
    10. Age in Months and Years [00:46]
    11. Hours Asleep vs Awake [00:49]
    12. Create Scatterplot [01:52]
    13. Calculate r [01:15]
    14. Stronger [00:10]
    15. Hypothesis Testing for [01:07]
    16. Hypothesis testing for [00:27]
    17. Testing for Significance [01:45]
    18. CI for [01:23]
    19. Find p [01:19]
    20. Add Outlier [01:43]
    21. Correlation vs Causation [02:11]
    22. Fallacies [02:35]
  11. Lesson 15 Regression [38:46]
    1. Intro to Linear Regression [01:30]
    2. Airplane Flights [02:15]
    3. Symbolize Regression Equation [02:33]
    4. Guess Best Fit Line [02:02]
    5. Minimize Sum of Squares [02:14]
    6. Calculate r [01:01]
    7. Calculate Standard Deviations [01:10]
    8. Calculate Slope [00:28]
    9. Find y-Intercept [00:58]
    10. What Point Does the Line Go Through [00:41]
    11. Calculate Means [00:18]
    12. Calculate y-Intercept [01:14]
    13. Travel 4000 Miles [00:36]
    14. Additional Cost per Mile [00:38]
    15. Cost to Travel 0 Miles [00:37]
    16. Travel on a Budget [00:59]
    17. Which Has More Error [01:43]
    18. Standard Error of Estimate [00:44]
    19. Confidence Intervals [02:23]
    20. Hypothesis Testing for Slope [01:36]
    21. t-Test for Slope [01:50]
    22. R Output [01:46]
    23. Factors Affecting Linear Regression [00:27]
    24. Summary of Linear Regression [01:57]
    25. Intro to Multiple Regression [02:57]
    26. Alcohol, Religiosity, Self-Esteem [01:20]
    27. Make Predictions [00:42]
    28. Relationship [00:49]
    29. Causation [01:00]
    30. Applets [00:18]
  12. Lesson 16 Chi-square Tests [41:11]
    1. Scales of Measurement [04:11]
    2. Choose Type of Data [02:05]
    3. Non-Parametric Tests [01:04]
    4. Mount Shasta [02:18]
    5. Expected Frequencies [04:50]
    6. Observed Frequency [00:24]
    7. Hypotheses Percent [01:19]
    8. Hypotheses Frequency [00:28]
    9. 2 Goodness-of-Fit Test [01:47]
    10. 2 Statistic [00:59]
    11. Observed Equals Expected [00:14]
    12. 2 Values [01:27]
    13. Degrees of Freedom [01:37]
    14. Which Has More df [01:21]
    15. Calculate 2 Statistic [01:50]
    16. Find df [01:07]
    17. Calculate p [01:32]
    18. 2 Test for Independence [01:04]
    19. Remember Details [00:54]
    20. Broken Glass [01:16]
    21. Broken glass [00:45]
    22. Decision [01:41]
    23. Effect Size [01:11]
    24. Calculate Cramers V [01:08]
    25. Assumptions and Restrictions [01:28]
    26. Summary [02:04]
    27. Congrats [00:23]
    28. Laurens Outro Video [00:44]

 

 

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