講者：USER
日期：2022-09-17
觀看: 3165
  • 00:00 1.
    Linear Regression
  • 01:25 2.
    The Model
  • 01:32 3.
    Linear Regression Model (1)
  • 08:18 4.
    Linear Regression Model (2)
  • 09:40 5.
    Estimation
  • 09:48 6.
    Find the Least Squares Estimator
  • 14:03 7.
    Digression to Derivative of Matrix:
  • 14:04 8.
    Find the Least Squares Estimator
  • 21:06 9.
    Digression to Derivative of Matrix:
  • 21:51 10.
    Second Order Condition
  • 22:48 11.
    Digression to Derivative of Matrix:
  • 22:50 12.
    Find the Least Squares Estimator
  • 23:21 13.
    Digression to Derivative of Matrix:
  • 23:21 14.
    Second Order Condition
  • 27:41 15.
    Simple Regression Example
  • 29:44 16.
    Estimating Simple Regression
  • 31:07 17.
    Simple Regression
  • 31:23 18.
    Estimating Simple Regression
  • 34:40 19.
    Simple Regression
  • 34:54 20.
    Goodness of Fit
  • 35:23 21.
    Simple Regression
  • 35:24 22.
    Estimating Simple Regression
  • 36:10 23.
    Simple Regression
  • 39:00 24.
    Goodness of Fit
  • 41:19 25.
    Simple Regression
  • 41:46 26.
    Goodness of Fit
  • 43:05 27.
    Simple Regression
  • 43:06 28.
    Estimating Simple Regression
  • 43:07 29.
    Simple Regression Example
  • 43:51 30.
    Estimating Simple Regression
  • 43:51 31.
    Simple Regression
  • 43:55 32.
    Goodness of Fit
  • 44:20 33.
    LS Estimators and Properties
  • 44:30 34.
    Four Assumptions
  • 48:37 35.
    Four Assumptions
  • 51:42 36.
    A Digression to Variance Covariance Matrix of a Vector
  • 54:03 37.
    Assumption A3
  • 55:45 38.
    Properties of LS Estimator: Unbiased
  • 1:00:21 39.
    VAR-COV Of 𝛽 :VAR( 𝛽 )
  • 1:00:23 40.
    VAR-COV Of 𝛽 :VAR( 𝛽 )
  • 1:00:57 41.
    What Have We Got So Far?
  • 1:00:58 42.
    VAR-COV Of 𝛽 :VAR( 𝛽 )
  • 1:01:29 43.
    Properties of LS Estimator: Unbiased
  • 1:01:42 44.
    VAR-COV Of 𝛽 :VAR( 𝛽 )
  • 1:05:47 45.
    What Have We Got So Far?
  • 1:06:26 46.
    VAR-COV Of 𝛽 :VAR( 𝛽 )
  • 1:06:45 47.
    What Have We Got So Far?
  • 1:07:15 48.
    Gauss Markov Theorem (I)
  • 1:09:17 49.
    Gauss Markov Theorem (II)
  • 1:14:25 50.
    Gauss Markov Theorem (III)
  • 1:17:20 51.
    Estimating 𝜎 2
  • 1:20:56 52.
    Degrees of Freedom
  • 1:23:19 53.
    What do we need to do for statistical inference?
  • 1:24:39 54.
    Properties of the OLS Estimator
  • 1:26:29 55.
    Statistical Inference
  • 1:27:38 56.
    Hypothesis Testing
  • 1:27:40 57.
    Statistical Inference
  • 1:28:04 58.
    Hypothesis Testing
  • 1:28:29 59.
    The Presidential Election Example
  • 1:30:35 60.
    The Outcome of the US Presidential Election 1892-2012
  • 1:31:57 61.
    The US Presidential Election Again
  • 1:33:57 62.
    The Presidential Election
  • 1:35:20 63.
    Presidential Election Estimation
  • 1:35:39 64.
    The Presidential Election
  • 1:36:51 65.
    Presidential Election Estimation
  • 1:37:53 66.
    The Presidential Election
  • 1:37:53 67.
    The US Presidential Election Again
  • 1:37:54 68.
    The Outcome of the US Presidential Election 1892-2012
  • 1:37:57 69.
    The Presidential Election Example
  • 1:37:59 70.
    Hypothesis Testing
  • 1:38:48 71.
    The Presidential Election Example
  • 1:38:48 72.
    The Outcome of the US Presidential Election 1892-2012
  • 1:38:49 73.
    The US Presidential Election Again
  • 1:38:49 74.
    The Presidential Election
  • 1:38:50 75.
    Presidential Election Estimation
  • 1:39:40 76.
    Hypothesis Testing: Ex. 3a)
  • 1:40:56 77.
    Alternative Test for H0: b2 =b3 (b2-b3 =0)
  • 1:40:57 78.
    Hypothesis Testing: Ex. 3a)
  • 1:41:22 79.
    Presidential Election Estimation
  • 1:41:23 80.
    The Presidential Election
  • 1:41:42 81.
    Presidential Election Estimation
  • 1:41:43 82.
    Hypothesis Testing: Ex. 3a)
  • 1:41:45 83.
    Alternative Test for H0: b2 =b3 (b2-b3 =0)
  • 1:44:01 84.
    Hypothesis Testing: Ex. 3a)
  • 1:44:02 85.
    Presidential Election Estimation
  • 1:44:03 86.
    The Presidential Election
  • 1:44:52 87.
    Presidential Election Estimation
  • 1:44:53 88.
    Hypothesis Testing: Ex. 3a)
  • 1:44:55 89.
    Alternative Test for H0: b2 =b3 (b2-b3 =0)
  • 1:45:41 90.
    Statistical Inference for Linear Restrictions
  • 1:45:46 91.
    Statistical Inference for Linear Restrictions
  • 1:47:25 92.
    Examples of Linear Restrictions
  • 1:53:12 93.
    Develop a Test
  • 1:57:04 94.
    Proof of R β −γ Distribution
  • 1:58:19 95.
    F-test
  • 2:01:14 96.
    Slide 42
  • 2:01:59 97.
    Proof of F-Test
  • 索引
  • 筆記
  • 討論
  • 全螢幕
metrics-2-multiple regression-1
長度: 2:03:37, 瀏覽: 3166, 最近修訂: 2022-09-17
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    • 00:00 1.
      Linear Regression
    • 01:25 2.
      The Model
    • 01:32 3.
      Linear Regression Model (1)
    • 08:18 4.
      Linear Regression Model (2)
    • 09:40 5.
      Estimation
    • 09:48 6.
      Find the Least Squares Estimator
    • 14:03 7.
      Digression to Derivative of Matrix:
    • 14:04 8.
      Find the Least Squares Estimator
    • 21:06 9.
      Digression to Derivative of Matrix:
    • 21:51 10.
      Second Order Condition
    • 22:48 11.
      Digression to Derivative of Matrix:
    • 22:50 12.
      Find the Least Squares Estimator
    • 23:21 13.
      Digression to Derivative of Matrix:
    • 23:21 14.
      Second Order Condition
    • 27:41 15.
      Simple Regression Example
    • 29:44 16.
      Estimating Simple Regression
    • 31:07 17.
      Simple Regression
    • 31:23 18.
      Estimating Simple Regression
    • 34:40 19.
      Simple Regression
    • 34:54 20.
      Goodness of Fit
    • 35:23 21.
      Simple Regression
    • 35:24 22.
      Estimating Simple Regression
    • 36:10 23.
      Simple Regression
    • 39:00 24.
      Goodness of Fit
    • 41:19 25.
      Simple Regression
    • 41:46 26.
      Goodness of Fit
    • 43:05 27.
      Simple Regression
    • 43:06 28.
      Estimating Simple Regression
    • 43:07 29.
      Simple Regression Example
    • 43:51 30.
      Estimating Simple Regression
    • 43:51 31.
      Simple Regression
    • 43:55 32.
      Goodness of Fit
    • 44:20 33.
      LS Estimators and Properties
    • 44:30 34.
      Four Assumptions
    • 48:37 35.
      Four Assumptions
    • 51:42 36.
      A Digression to Variance Covariance Matrix of a Vector
    • 54:03 37.
      Assumption A3
    • 55:45 38.
      Properties of LS Estimator: Unbiased
    • 1:00:21 39.
      VAR-COV Of 𝛽 :VAR( 𝛽 )
    • 1:00:23 40.
      VAR-COV Of 𝛽 :VAR( 𝛽 )
    • 1:00:57 41.
      What Have We Got So Far?
    • 1:00:58 42.
      VAR-COV Of 𝛽 :VAR( 𝛽 )
    • 1:01:29 43.
      Properties of LS Estimator: Unbiased
    • 1:01:42 44.
      VAR-COV Of 𝛽 :VAR( 𝛽 )
    • 1:05:47 45.
      What Have We Got So Far?
    • 1:06:26 46.
      VAR-COV Of 𝛽 :VAR( 𝛽 )
    • 1:06:45 47.
      What Have We Got So Far?
    • 1:07:15 48.
      Gauss Markov Theorem (I)
    • 1:09:17 49.
      Gauss Markov Theorem (II)
    • 1:14:25 50.
      Gauss Markov Theorem (III)
    • 1:17:20 51.
      Estimating 𝜎 2
    • 1:20:56 52.
      Degrees of Freedom
    • 1:23:19 53.
      What do we need to do for statistical inference?
    • 1:24:39 54.
      Properties of the OLS Estimator
    • 1:26:29 55.
      Statistical Inference
    • 1:27:38 56.
      Hypothesis Testing
    • 1:27:40 57.
      Statistical Inference
    • 1:28:04 58.
      Hypothesis Testing
    • 1:28:29 59.
      The Presidential Election Example
    • 1:30:35 60.
      The Outcome of the US Presidential Election 1892-2012
    • 1:31:57 61.
      The US Presidential Election Again
    • 1:33:57 62.
      The Presidential Election
    • 1:35:20 63.
      Presidential Election Estimation
    • 1:35:39 64.
      The Presidential Election
    • 1:36:51 65.
      Presidential Election Estimation
    • 1:37:53 66.
      The Presidential Election
    • 1:37:53 67.
      The US Presidential Election Again
    • 1:37:54 68.
      The Outcome of the US Presidential Election 1892-2012
    • 1:37:57 69.
      The Presidential Election Example
    • 1:37:59 70.
      Hypothesis Testing
    • 1:38:48 71.
      The Presidential Election Example
    • 1:38:48 72.
      The Outcome of the US Presidential Election 1892-2012
    • 1:38:49 73.
      The US Presidential Election Again
    • 1:38:49 74.
      The Presidential Election
    • 1:38:50 75.
      Presidential Election Estimation
    • 1:39:40 76.
      Hypothesis Testing: Ex. 3a)
    • 1:40:56 77.
      Alternative Test for H0: b2 =b3 (b2-b3 =0)
    • 1:40:57 78.
      Hypothesis Testing: Ex. 3a)
    • 1:41:22 79.
      Presidential Election Estimation
    • 1:41:23 80.
      The Presidential Election
    • 1:41:42 81.
      Presidential Election Estimation
    • 1:41:43 82.
      Hypothesis Testing: Ex. 3a)
    • 1:41:45 83.
      Alternative Test for H0: b2 =b3 (b2-b3 =0)
    • 1:44:01 84.
      Hypothesis Testing: Ex. 3a)
    • 1:44:02 85.
      Presidential Election Estimation
    • 1:44:03 86.
      The Presidential Election
    • 1:44:52 87.
      Presidential Election Estimation
    • 1:44:53 88.
      Hypothesis Testing: Ex. 3a)
    • 1:44:55 89.
      Alternative Test for H0: b2 =b3 (b2-b3 =0)
    • 1:45:41 90.
      Statistical Inference for Linear Restrictions
    • 1:45:46 91.
      Statistical Inference for Linear Restrictions
    • 1:47:25 92.
      Examples of Linear Restrictions
    • 1:53:12 93.
      Develop a Test
    • 1:57:04 94.
      Proof of R β −γ Distribution
    • 1:58:19 95.
      F-test
    • 2:01:14 96.
      Slide 42
    • 2:01:59 97.
      Proof of F-Test
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    資料夾名稱
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    發表人
    李阿乙
    單位
    powercam.fju.edu.tw (root)
    建立
    2022-09-17 12:51:46
    最近修訂
    2022-09-17 14:22:53
    長度
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