MIME-Version: 1.0 Content-Type: multipart/related; boundary="----=_NextPart_01C82EB7.6AE4EDE0" このドキュメントは単一ファイル Web ページ (Web アーカイブ ファイル) です。お使いのブラウザ、またはエディタは Web アーカイブ ファイルをサポートしていません。Microsoft Internet Explorer など、Web アーカイブをサポートするブラウザをダウンロードしてください。 ------=_NextPart_01C82EB7.6AE4EDE0 Content-Location: file:///C:/8ECB5C14/07dist_4.htm Content-Transfer-Encoding: quoted-printable Content-Type: text/html; charset="shift_jis" データ分布と予測　4．点推定と区間推定

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<日付/時刻>
<フッター>
<#>
マスタ タイトルの書式設定
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<= /div>
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<ヘッダー>= ;
<= /div>
<= /div>
<= /div>
<日付/時刻>
<= /div>
<= /div>
<= /div>
<フッター>= ;
<= /div>
<= /div>
<= /div>
<#>
------=_NextPart_01C82EB7.6AE4EDE0 Content-Location: file:///C:/8ECB5C14/07dist_4.files/pres.xml Content-Transfer-Encoding: quoted-printable Content-Type: text/xml; charset="utf-8" ------=_NextPart_01C82EB7.6AE4EDE0 Content-Location: file:///C:/8ECB5C14/07dist_4.files/slide0001.htm Content-Transfer-Encoding: quoted-printable Content-Type: text/html; charset="shift_jis" データ分布と予測　4．点推定と区間推定
データ分布= と予測
推定：点推= 定と区間推定
堀田敬介
2007/11/30,Fri.
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Content-Type: text/html; charset="shift_jis" データ分布と予測　4．点推定と区間推定
Contents
n推定
¨点推定と区間推定 <= /div>
n点推定 = point estimation
¨モーメント法 method of moments
¨最尤法 maximum likelihood method
n区間推定 = interval estimation
¨母平均の推定：母分散が既知の場合〔= Z推定= 〕
¨母平均の推定：母分散が未知の場合〔= t 推定〕
¨母分散の推定：〔<= b>χ2= 定〕
¨母比率の推定：〔Z推定〕
¨
¨2つの正規母集団の比較
n母平均の差の区間推定 <= /div>
n母分散の比の区間推定
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推測統計
n推測対象による分類
推測統計学 =
inferential statistics
母数の値
母平均
母分散
3D"ホームベース:
推測対象
母集団の
従う分布 =
その他
推定：点推定，区間推定
仮説検定
適合度検定
独立性の検定
推測方= @
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母数の推定
n母集団の推定
標本
sample
母集団
population
母数 parameter<= /div>
推定量 estimator<= /div>
<= span style=3D'font-family:"Times New Roman";font-size:111%;text-shadow:auto; mso-fareast-hint:yes'>•パ= ラメトリック
母数θがわかると母= W団分布がわかる場合
•ノン・パラメトリック
母数θのみ推定した= ｢（母集団分布に関心がない）場合 =
•点推定
母数θをある1つの値　　で指定する方法
•区間推定
母数θの値が入る確率がある値以上を保<= /span>証する区間を求める方法
無作為抽出
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file:///C:/8ECB5C14/07dist_4.files/slide0009.htm Content-Transfer-Encoding: quoted-printable Content-Type: text/html; charset="shift_jis" データ分布と予測　4．点推定と区間推定
不偏推定量
n母数の推定量・推定値
¨母数θを推定するため= に用いる統計量W推定量という <= /span>
¨推定量W推定値という <= /span>
n不偏推定量
¨E(W)=3Dθが成り立つとき，統計量Wをθの不偏推定量という <= /span>
n例1
n
n
n例2
n
n
n例3
より不偏推定量
より不偏推定量ではない
より不偏推定量
標本の観測値から計算される量
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この店舗の平均来客数は294である
母数（母平均）の推定：点推定 point estimation
nある店舗の36= 日分の週末来客数のデータ
標本平均値：294
点推定
この店舗の週末の平均来客数を知りたい！
母集団 = population
ある店舗= ﾌ
週末来客=
標= 本
sample=
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LhVWgRWlsRVGzhOFkeSsYa2XwuT90BI2QRNkPhM28SIVji9GfuRIzuWcUA/csRhM3uRpkRE1nhV2 DhjSIRjU4eJt7uYJUg/ioBLp8A+GbugB0REeseWAjh2SUBZGMQ75MOlrLhCLDg+NftTJoBi+sA7r QA1+juGZbtyrYA+gPhAFUQmjvupIvQqururGHQgAOw== ------=_NextPart_01C82EB7.6AE4EDE0 Content-Location: file:///C:/8ECB5C14/07dist_4.files/slide0038.htm Content-Transfer-Encoding: quoted-printable Content-Type: text/html; charset="shift_jis" データ分布と予測　4．点推定と区間推定
母数の推定：点推定 point estimation
n点推定
¨積率法 method of moments
n積率（モーメント）を= 利用する方法
¨最尤法 maximum likelihood method
n最尤原理：「現実の標= 本は確率最大のものが実現した」に基づく方法 <= /div>
X<= span style=3D'font-family:"ＭＳ Ｐゴシック";mso-ascii-font-family:"Times New Ro= man"; mso-fareast-font-family:"ＭＳ Ｐゴシック";mso-hansi-font-family:"Times New= Roman"; mso-fareast-hint:yes'>の（原点まわりの）r次積率
X<= span style=3D'font-family:"ＭＳ Ｐゴシック";mso-ascii-font-family:"Times New Ro= man"; mso-fareast-font-family:"ＭＳ Ｐゴシック";mso-hansi-font-family:"Times New= Roman"; mso-fareast-hint:yes'>の期待値まわりのr次積率
X<= span style=3D'font-family:"ＭＳ Ｐゴシック";mso-ascii-font-family:"Times New Ro= man"; mso-fareast-font-family:"ＭＳ Ｐゴシック";mso-hansi-font-family:"Times New= Roman"; mso-fareast-hint:yes'>のr次標準化積率=
母数 =
標本 =
母集団確率分布
尤度関数
尤度関数を母数空間Θ上で最大にするものを推定値・推定量と= ｷる
<= span style=3D'font-family:"Times New Roman";mso-fareast-hint:yes'>3D"*"尤度関数を最大にするθ：最尤推定値 maximum likelihood estimate =
<= span style=3D'font-family:"Times New Roman";mso-fareast-hint:yes'>3D"*"母数空間Θ parameter space ：母数がとりうる値の集合
※注意：最尤法は尤度関数を作る関係上，母集団分布がわからな= いときは使えない！
n個の標本の実現値（観測値）
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母数の推定：点推定 point estimation
n最尤法 maximum likelihood method
¨ X=3D1,0 で = 1をとる確率 p のベルヌーイ分布Bi(1,p) p <= /div>
5つの標本をとったところ…
尤度関数を最大にするpを求めると…
尤度関数は
最尤推定値
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quoted-printable Content-Type: text/html; charset="shift_jis" データ分布と予測　4．点推定と区間推定
n点推定の基= 準
¨不偏性
n推定量　　の期待値が= ，真の母数　　の値となる性質
¨例1：標本平均　　は母平均　　の不偏推定量=
¨例2：標本分散　　は母分散　　の不偏推定量= ではない
¨例3：不偏分散　　は母分散　　の不偏推定量=
§
<= span style=3D'font-size:86%;mso-fareast-hint:yes'>¨一致性
<= span style=3D'font-family:"Times New Roman";font-size:83%;mso-fareast-hint:yes'= >n標本数nが大きくなれば，推定量　　が真の母数　= 　に近づく性質
¨例1：標本平均　　は母平均　　の一致推定量=
¨例2：標本分散　　は母分散　　の一致推定量=
¨例3：不偏分散　　は母分散　　の一致推定量=
母数の推定：点推定 point estimation
= <= ![endif]>
一致推定量 consistent estimator
= <= ![endif]>
モーメント法による
3D"*"母平均の推定量　．3D"*"母分散の推定量　．
不偏推定量 unbiased estimator
2推定量が最小限満たすべき性質
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Content-Transfer-Encoding: base64 Content-Type: image/gif R0lGODlhaABFAHcAMSH+GlNvZnR3YXJlOiBNaWNyb3NvZnQgT2ZmaWNlACH5BAEAAAAALAAAAQBm AEMAgQAAAJaWlv///wECAwL/hI+pyxANo5y02vCs3rwvLGTeSJZJIKShybYaqq7uTJ9wLNY6e8dp vgtyej6Z8FgBFXHIpoS4/DmnCmjUSHUqrz5gVmjlSr/HsBhLpm3PRW+6gYnL53K2mI7P6/f8vMMO GCg4SBhiVoiYqNh1uOj4CLgGOUl5JlmJmfnTqNmZeOkZqggqWjpIaprKhqraGsXqGqsCKxtLW9t6 i5uqu1va6xsKHNw5TJxpfFyZrDzJ3Pz4DL0oPf3JaY1ZnU24zS3o/R2JLR5NXk59jn69Lqze3v0O Dy4/P26PXI9vqb/PFe5vVr+ASwASNBgQoT+F+xjic2gP4jyJ8Ci2s7gOI7o+CRw7evwI0iOAAgA7 ------=_NextPart_01C82EB7.6AE4EDE0 Content-Location: file:///C:/8ECB5C14/07dist_4.files/slide0040.htm Content-Transfer-Encoding: quoted-printable Content-Type: text/html; charset="shift_jis" データ分布と予測　4．点推定と区間推定
<= span style=3D'mso-fareast-hint:yes'>n点推定の基= 準
¨漸近正規性 asymptotic normality
n標本分布の漸近分布が= 正規分布である性質
¨例：標本平均　　の漸= 近分布は，中心極限定理より，母集団分布に関係なく正規分布となる
<= span style=3D'font-size:86%;mso-fareast-hint:yes'>¨有効性 efficiency
n不偏性と一致性を満たす他のいかなる推定= 量よりも，分散が小さいという性質
<= span style=3D'font-family:"Times New Roman";font-size:90%;mso-fareast-hint:yes'= >¨例：母集団分布が正規分布の場合，標本平= 均　　は母平均　　の有効推定量
<= span style=3D'font-family:"Times New Roman";font-size:86%;mso-fareast-hint:yes'= >¨漸近有効性 asymptotic efficiency
<= span style=3D'font-family:"Times New Roman";font-size:83%;mso-fareast-hint:yes'= >n漸近分布が正規分布となる推定量のうち，= 漸近分散が最小となる性質
<= span style=3D'font-family:"Times New Roman";font-size:90%;mso-fareast-hint:yes'= >¨例：最尤推定量は一般に漸近有効性を持つ= <= /div>
母数の推定：点推定 point estimation
有効推定量 efficient estimator
〔最小分散不偏推定量 minimum variance unbiased estimator〕
漸近正規推定量
 asymptotic normally estimator
漸近的有効推定量
asymptotically efficient estimator
有効性の検証が難<= span style=3D'font-family:"ＭＳ Ｐゴシック";mso-ascii-font-family:"Times New Ro= man"; mso-fareast-font-family:"ＭＳ Ｐゴシック";mso-hansi-font-family:"Times New= Roman"; font-size:89%;mso-fareast-hint:yes'>しいため，漸近有効<= span style=3D'font-family:"ＭＳ Ｐゴシック";mso-ascii-font-family:"Times New Ro= man"; mso-fareast-font-family:"ＭＳ Ｐゴシック";mso-hansi-font-family:"Times New= Roman"; font-size:89%;mso-fareast-hint:yes'>性を用いる
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R0lGODlhfwA5AHcAMSH+GlNvZnR3YXJlOiBNaWNyb3NvZnQgT2ZmaWNlACH5BAEAAAAALAIAAAB8 ADgAgQAAAJaWlv///wECAwL1hI8Wy+0Po5y02guTBkH4D4biSJbmiaZqGGxJt8byTNdg6x6wzff+ jMtxfsSi8SZUHJfMWjC3a0qnpKcrSs1SrRus9rvkarzg8k+sI5vXNvSCDSdy3/F670m3650K9f6P wgA4CORHeCjSkofImJjWCPlhZRipN0dZCeeGmVmG1te596kTajeqVBp3OpSqmcTaurbKGRv2Sltr NJtrtssL5vurFSy8dVs8fIxsnIS7zNf8PEUsfURdXXSNfaa8rdvtLQce7qNNDi3kfG5ivi7T7r4C H58yT39if1+Srz/C389iHMB6AgfiK2hw3y0MDBs6fAjxQQEAOw== ------=_NextPart_01C82EB7.6AE4EDE0 Content-Location: file:///C:/8ECB5C14/07dist_4.files/slide0045.htm Content-Transfer-Encoding: quoted-printable Content-Type: text/html; charset="shift_jis" データ分布と予測　4．点推定と区間推定
母数（母平均）の推定：点推定
n例題
¨40人のクラスでテストを行った．10果は以下のとおりとなった．クラス全体の= 平均は何点だろうか？　ただし満点は100
¨
¨(1) 点推定で母平均を推定せよ
65=
60
71
85
75
67
73
82
62
70
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母数の推定：区間推定 interval estimation
<= span style=3D'mso-fareast-hint:yes'>n区間推定 … 母数の取り得る区間を推定する = ;
n 「母平均　　は○○から△△の間にある」
= 推測の区間だけではなく，
推測の当たる可能性（確からしさ）も重要
推測の区間の幅が広ければ広いほど，
当たる可能性は高くなる
=
n「<= span style=3D'font-family:"ＭＳ Ｐゴシック";mso-fareast-font-family:"ＭＳ Ｐゴ= Vック";font-size: 88%;mso-fareast-hint:yes'>母平均　　は = ;
n□□%= の確からしさで， = ;
n○○から△△の間にある」
信頼度（信頼係数= j
信頼区間
=
= 例：文教大学の男子学生の平均身長は？
　「平均身長は0cm300cmの間にある」
　「平均身長は100cm〜200cmの間にある」<= span style=3D'font-family:"ＭＳ Ｐゴシック";mso-ascii-font-family:"Times New Ro= man"; mso-fareast-font-family:"ＭＳ Ｐゴシック";mso-hansi-font-family:"Times New= Roman"; color:#666699;display:none'>
　「平均身長は160cm〜180cmの間にある」<= span style=3D'font-family:"ＭＳ Ｐゴシック";mso-ascii-font-family:"Times New Ro= man"; mso-fareast-font-family:"ＭＳ Ｐゴシック";mso-hansi-font-family:"Times New= Roman"; color:#666699;display:none'>
　「平均身長は170cm〜175cmの間にある」<= span style=3D'font-family:"ＭＳ Ｐゴシック";mso-ascii-font-family:"Times New Ro= man"; mso-fareast-font-family:"ＭＳ Ｐゴシック";mso-hansi-font-family:"Times New= Roman"; color:#666699;mso-special-format:lastCR;display:none'>
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母数の推定：区間推定 interval estimation
n区間推定
¨信頼度（信頼係数）
n推測した結果がどれだけ信頼できるかの目安
¨信頼区間
n推測の範囲&#= 13;
信頼区間の幅が広い
　⇒　推測が当たる可能性高い
　⇒　信頼度が高い
信頼区間の幅が狭い
　⇒　推測が当たる可能性低い
　⇒　信頼度が低い
信頼区間
0cm
300cm
100cm
200cm
160cm
180cm
170cm
175cm
信= 頼度
3D"テキスト
＞
3D"テキスト
＞
3D"テキスト
＞
ある程度充分な数の標本（n個）を収集し，信頼度を保ちながら，なる= べく狭い信頼区間を推定したい！= =
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file:///C:/8ECB5C14/07dist_4.files/slide0033.htm Content-Transfer-Encoding: quoted-printable Content-Type: text/html; charset="shift_jis" データ分布と予測　4．点推定と区間推定
母数の推定：区間推定 interval estimation
nパーセント= 点 percentage point
¨分布の100α%点
n= 上側100α%点
n= 下側100α%点
n= 両側100α%点
n
<= span style=3D'font-family:"Times New Roman";mso-fareast-hint:yes'>¨例：標準正規分布の <= /div>
¨ 　　両側100α<= span lang=3DEN-US style=3D'font-family:"Times New Roman";mso-ascii-font-family:= "Times New Roman"; mso-hansi-font-family:"Times New Roman";mso-fareast-hint:yes;mso-fareast-l= anguage: JA'>%点
例：信頼率0.95(95%)なら，α=3D0.05&#= 13;
信頼率0.90(90%)= なら，α=3D0.10
例：正規分布の両側5%点（上側2.5%,下側2.5%）
約1.96,　約-1.96
両側100α%点
100α%
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区間推定 interval estimation <= /span>
nパーセント点 percentage point
= N例：自由度10のt = 分布の
¨　　　上側100α%点
<= span style=3D'font-family:"Times New Roman";mso-fareast-hint:yes'>¨例：自由度5のχ2分布の
¨　　　両側100α<= span lang=3DEN-US style=3D'font-family:"Times New Roman";mso-ascii-font-family:= "Times New Roman"; mso-hansi-font-family:"Times New Roman";mso-fareast-hint:yes;mso-fareast-l= anguage: JA'>%点
上側100α%点
100α%
両側100α%点
100α%
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95％
-1.96
1.96
母数の推定：区間推定 interval estimation
n区間推定
¨標準正規分布に従う確率変数 Z <= /span>を使う <= /div>
¨標準正規分布に従う確率変数 Z が ｰ1.96以上<= span lang=3DEN-US style=3D'font-family:"Times New Roman";mso-ascii-font-family:= "Times New Roman"; mso-hansi-font-family:"Times New Roman";mso-fareast-language:JA'>1.96以下の値をとる確率は0.95
2.5％ =
2.5％ =
N(0= ,1)
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file:///C:/8ECB5C14/07dist_4.files/slide0007.htm Content-Transfer-Encoding: quoted-printable Content-Type: text/html; charset="shift_jis" データ分布と予測　4．点推定と区間推定
母数の推定：区間推定 interval estimation
n区間推定（母分散が既知の場合）
¨母平均μは95%の信頼度で以下の信頼区間の間にあると推測できる！
¨
¨=
n=
n=
n=
¨= 信頼度95％の信頼区間の幅
母分散σ2がわかると計算できる
注：有限母集団（大きさN<= span style=3D'font-family:"ＭＳ Ｐゴシック";mso-ascii-font-family:"Times New Ro= man"; mso-fareast-font-family:"ＭＳ Ｐゴシック";mso-hansi-font-family:"Times New= Roman"; mso-fareast-hint:yes'>）の場合
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母数（母平均）の推定：区間推定
n区間推定（母分散が既知の場合: Z推定） = ;
¨母平均μ100(1-α)%で以下信頼区間の間にある
標準正規分布 N(0,1) で Zα/2 のとる確率によって定まる
2.58
1.96
1.64
Zα/2
99%
95%
90%
信頼度
90%
95%
99%
1.64
1.96
2.58
広
狭
信頼区間
信頼度・標本数と信頼区間の相対的関係
n：大
n：小
大<= /b>
小<= /b>
N(0,1)
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母数（母平均）の推定：区間推定
n例題
¨40人のクラスでテストを行った．10は以下のとおりとなった．クラス全体の平= 均は何点だろうか？　ただし満点は100
¨
¨(1) 母分散が59のとき，信頼度95%で区間推定せよ
信頼度95%　→　α=3D0.05　→　Zα/2=3D1.96
65=
60
71
85
75
67
73
82
62
70
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母数（母平均）の推定：区間推定
<= span style=3D'mso-fareast-hint:yes'>n区間推定（母分散が未知の場合）
= N自由度n-1の t = 分布に従う確率変数 T を使う
<= span style=3D'font-family:"Times New Roman";mso-fareast-hint:yes'>¨自由度9の t = 分布に従う確率変数 T がｰ2.262以上2.262以下の値をとる確率は0.95 <= /span>
-2.262
2.262
95％
2.5％ =
2.5％ =
t(9= )
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母数（母平均）の推定：区間推定
n区間推定（母分散が未知の場合）
¨母平均μは95%の信頼度で以下の信頼区間の間にあると推測できる！（自由度9
¨=
¨=
n=
n=
n=
¨= 信頼度95％の信頼区間の幅（自由度9
標本平均　　と標本分散　　から計算できる
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Content-Location: file:///C:/8ECB5C14/07dist_4.files/slide0011.htm Content-Transfer-Encoding: quoted-printable Content-Type: text/html; charset="shift_jis" データ分布と予測　4．点推定と区間推定
母数（母平均）の推定：区間推定
n区間推定（母分散が未知の場合: t 推定）
¨母平均　　= が100(1-α)%の信頼度で以下信頼区間の間
自由度 n-1 の t 分布で tα/2 のとる確率によって定まる
3.250
2.262
1.833
tα/2(9)
99%
95%
90%
信頼度
広
狭
信頼区間
信頼度・標本数と信頼区間の相対的関係
n：大
n：小
大<= /b>
小<= /b>
90%
95%
99%
1.83
2.26
3.25
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file:///C:/8ECB5C14/07dist_4.files/slide0013.htm Content-Transfer-Encoding: quoted-printable Content-Type: text/html; charset="shift_jis" データ分布と予測　4．点推定と区間推定
母数（母平均）の推定：区間推定
n例題 <= /span>
¨40人のクラスでテストを行った．10は以下のとおりとなった．クラス全体の平= 均は何点だろうか？　ただし満点は100
¨
¨(1) 母分散が未知のとき，信頼度90%で区間推定せよ
信頼度90%　→　α=3D0.10　→　tα/2(9)=3D1.833
65=
60
71
85
75
67
73
82
62
70
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母数（母平均）の推定：区間推定
n演習
= N正規母集団から標本 <= /div>
¨ 9,   7,   12,   8,   9
¨ を得た．
¨(1) 母平均μを点推定せよ．
¨(2) 母分散σ2=3D4の時，信頼度95%で母平均μを区間推定せよ．
¨(3) 母分散σ2=3D4の時，信頼度99%
¨(4) 母分散が未知の時，信頼度90%で母平均μを区間推定せよ．
¨(5) 母分散が未知の時，信頼度95%
母数（母平均）の推定：区間推定（まとめ）=
n母平均の区間推定
¨= 母分散が既= 知のとき <= /div>
n⇒ Z推定
¨
¨
¨
¨= 母分散が= ｢知のとき <= /div>
n⇒ t 推定
母分散σ2 の値が既知のときに，標準正規分布N(0,1)の性質を利用して母平均μの信頼区間を求める
母分散σ2 の値が未知のときに，標本分散S2 を用い，自由度n-1= の= = t 分布の性質を利用して母平均μの信頼区間を求める
〔信頼率 1-α〕
〔信頼率 1-α〕
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------=_NextPart_01C82EB7.6AE4EDE0 Content-Location: file:///C:/8ECB5C14/07dist_4.files/slide0053.htm Content-Transfer-Encoding: quoted-printable Content-Type: text/html; charset="shift_jis" データ分布と予測　4．点推定と区間推定
参考：母平均区間推定の標本数設計法
n1-α）〔母分散σ2既知の場合〕
¨ <= /div>
この幅をδ以= 下にしたい！
これより，標本数 n を
この数以上にすればよい．
¨例題：全国男子大学生の平均身長を区間推= 定したい．95%2cm散はσ2= =3D49とする．
よって，n<= /span>=3D189人を調べれば充分
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file:///C:/8ECB5C14/07dist_4.files/slide0054.htm Content-Transfer-Encoding: quoted-printable Content-Type: text/html; charset="shift_jis" データ分布と予測　4．点推定と区間推定
参考：母平均区間推定の標本数設計法
n1-α）〔母分散σ2未知の場合〕
¨ <= /div>
この幅をδ以= 下にしたい！
区間幅　　　　　　　　　　をδ以下にすればよいが，確率変数S<= span style=3D'font-family:"ＭＳ Ｐゴシック";mso-ascii-font-family:"Times New Ro= man"; mso-fareast-font-family:"ＭＳ Ｐゴシック";mso-hansi-font-family:"Times New= Roman"; mso-fareast-hint:yes'>が含まれているので，区間幅の期待値をδ以下に抑える．
E<= /span>(<= span lang=3DEN-US style=3D'font-family:"Times New Roman";mso-ascii-font-family:= "Times New Roman"; mso-hansi-font-family:"Times New Roman";mso-fareast-language:JA'>S<= /span>)<= span style=3D'font-family:"ＭＳ Ｐゴシック";mso-ascii-font-family:"Times New Ro= man"; mso-fareast-font-family:"ＭＳ Ｐゴシック";mso-hansi-font-family:"Times New= Roman"; mso-fareast-hint:yes'>は未知母数σに依存するので，何らかの情報からσを想定し，標本数 n を設定することになる．
だが<= /div>
であることに= 注意
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母数（母分散）の推定：区間推定
n母分散の区間推定 <= /div>
= N自由度n-1のχ<= span lang=3DEN-US style=3D'font-family:"Times New Roman";mso-ascii-font-family:= "Times New Roman"; mso-hansi-font-family:"Times New Roman";font-size:67%;position:relative; top:-.45em;mso-text-raise:30%;mso-fareast-language:JA'>2 分布に従う確率変数χ2 を使う
<= span style=3D'font-family:"Times New Roman";mso-fareast-hint:yes'>¨自由度9のχ2 分布に従う確率変数χ2 がｰ2.700以上19.023以下の値をとる確率は0.95
2.7
19.023
=
95％
2.5％ =
2.5％ =
=
χ2(9= )
注= ：χ2分布は左右対= 称ではないので，= 左右それぞれのすその面積が0.025と= なる点を考える必要がある．
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母数（母分散）の推定：区間推定
n母分散の区間推定
¨母分散σ<= span lang=3DEN-US style=3D'font-family:"Times New Roman";mso-ascii-font-family:= "Times New Roman"; mso-hansi-font-family:"Times New Roman";font-size:67%;position:relative; top:-.45em;mso-text-raise:30%;mso-fareast-language:JA'>2は95%の信頼度で以= 下の信頼区間の間にあると推測できる！9
標本分散　　から計算できる
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母数（母分散）の推定：区間推定
n母分散の区間推定（χ= 2 推定）
¨母分散　　が<= span lang=3DEN-US style=3D'font-family:"Times New Roman";mso-ascii-font-family:= "Times New Roman"; mso-hansi-font-family:"Times New Roman";font-size:86%;mso-fareast-language: JA'>100(１-α)%の信頼度で以下信頼区間の間
自由度n-1のχ2分布で　　　　　　　のとる確率によっ= ﾄ定まる
=
広
狭
信頼区間
信頼度・標本数と信頼区間の相対的関係
n：大
n：小
大<= /b>
小<= /b>
90%
95%
99%
3.33
2.70
1.73
16.92
23.59
19.02
χ= 2(9= )
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母数（母分散）の推定：区間推定
n例題 <= /span>
¨40人のクラスでテストを行った．10は以下のとおりとなった．クラス全体の標= 準偏差はどのぐらいだろうか？　ただし，満点は100
¨
¨(1) 信頼度95%で区間推定せよ
信頼度95%　→　α=3D0.05　→　χ21-α/2(9)=3D2.70039,χ2α/2(9)=3D19.0228
65=
60
71
85
75
67
73
82
62
70
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母数（母分散）の推定：区間推定
n演習（出展：「確率・統計= の仕組みがわかる本」技評p.367)=
¨16
¨
¨
¨
n(1) 信頼度90%<= span style=3D'font-family:"ＭＳ Ｐゴシック";mso-ascii-font-family:"Times New Ro= man"; mso-fareast-font-family:"ＭＳ Ｐゴシック";mso-hansi-font-family:"Times New= Roman"; mso-fareast-hint:yes'>で母分散σ2 を区間推定せよ．
n(2) 信頼度95%<= span style=3D'font-family:"ＭＳ Ｐゴシック";mso-ascii-font-family:"Times New Ro= man"; mso-fareast-font-family:"ＭＳ Ｐゴシック";mso-hansi-font-family:"Times New= Roman"; mso-fareast-hint:yes'>で母分散σ2 を区間推定せよ．
n(3) 信頼度99%で母分散σ2 を区間推定せ= よ．
47=
54=
49=
48=
51=
48=
53=
50=
44=
52=
47=
51=
46=
54=
52=
46=
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母数の推定：区間推定
n演習（参考：「統計学入門= 」東大出版会 p.231)
¨200511110は下表のとおりであった．正規母集団を仮= 定する．
n
n
n
n（データ：「Yahoo!天気情報」より）
n(1) 最高気温について，信頼度99%で母平均μの信頼区間を求めよ．<= span style=3D'font-family:"ＭＳ Ｐゴシック";mso-ascii-font-family:"Times New Ro= man"; mso-fareast-font-family:"ＭＳ Ｐゴシック";mso-hansi-font-family:"Times New= Roman"; font-size:83%;display:none'>
n(2) 最高気温について，信頼度95%2の信頼区間を求めよ．
n(3) 最低気温について，信頼度95%
n(3) 最低気温について，信頼度90%で母分散σ2の信頼区間を求めよ．
18
19
22
24
16
21
21
19
19
17
最高気温(℃)
10
10
12
13
13
13
12
12
10
10
最低気温(℃)
11/10
11/9
11/8
11/7
11/6
11/5
11/4
11/3
11/2
11/1
日にち
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母数（母分散）の推定：区間推定（まとめ）=
n母分散の区間推定
¨χ2= 推定
自由度n-1のχ2分布<= /b>の性質を利用して母分散σ2=  の信頼区間を求める
〔信頼率 1-α〕
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母数（母比率）の推定：区間推定
母集団 〔N人〕
意見Aの人々
人数：Np人
比率：= p
意見= B= の人々
人数：N(1-p)人
比率：= 1 -= p
標= 本 〔n人〕
（X <= /span>は正規分布N(np= , = np= (<= span lang=3DEN-US style=3D'font-family:"Times New Roman";mso-ascii-font-family:= "Times New Roman"; mso-hansi-font-family:"Times New Roman";mso-fareast-language:JA'>1<= /span>-<= span lang=3DEN-US style=3D'font-family:"Times New Roman";mso-ascii-font-family:= "Times New Roman"; mso-hansi-font-family:"Times New Roman";mso-fareast-language:JA'>p<= /span>))= に従う）
n母比率 p の推定
N人からn人を
無= 作為抽出&#= 13;
意見Aの人々
人数：X人
比率：= X/n
意見= B= の人々
人数：n-X人
比率：= (= n= -= X= )/= n
意見A
意見B
比率： = p
比率：1-= p
〔N人<= span lang=3DEN-US style=3D'font-family:"ＭＳ Ｐゴシック";mso-ascii-font-family:= "Times New Roman"; mso-fareast-font-family:"ＭＳ Ｐゴシック";mso-hansi-font-family:"Times New= Roman"; text-shadow:auto;mso-fareast-hint:yes;mso-fareast-language:JA'>〕
賛成か反対か〔二者択= 一〕
意見A
意見B
確率： p
確率：1 - = p
〔n人<= span lang=3DEN-US style=3D'font-family:"ＭＳ Ｐゴシック";mso-ascii-font-family:= "Times New Roman"; mso-fareast-font-family:"ＭＳ Ｐゴシック";mso-hansi-font-family:"Times New= Roman"; text-shadow:auto;mso-fareast-hint:yes;mso-fareast-language:JA'>〕
二項分布 B(n,p)
〔Np人〕
〔N(1-= p)人〕
〔X人〕
〔n-= X人〕
標本比率<= span style=3D'font-family:"ＭＳ Ｐゴシック";mso-ascii-font-family:"Times New Ro= man"; mso-fareast-font-family:"ＭＳ Ｐゴシック";mso-hansi-font-family:"Times New= Roman"; text-shadow:auto;color:green;mso-special-format:lastCR;display:none'>&#= 13;
母比率
知= りたい数値
（Xは二項分布B(n<= /span>,<= span lang=3DEN-US style=3D'font-family:"Times New Roman";mso-ascii-font-family:= "Times New Roman"; mso-hansi-font-family:"Times New Roman";mso-fareast-language:JA'>p<= /span>)<= span style=3D'font-family:"ＭＳ Ｐゴシック";mso-fareast-font-family:"ＭＳ Ｐゴ= Vック";mso-fareast-hint: yes'>に従う）
充分大きい
=
（意見Aである）
（意見Bである）
⇒ X =3D X1 +…+ Xn
第i番目の人について
Xi〜B(1,p)
中心極限定理
X〜N( np, np(1-p) )
推定
X〜B(n,p)
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R0lGODlhSgAXAHcAMSH+GlNvZnR3YXJlOiBNaWNyb3NvZnQgT2ZmaWNlACH5BAEAAAAALAEAAQBI ABYAgAAAAAAAAAJIBBKmy+0PY0QqyYuzNsnuD35USJbQaKYpqrYg68YYLNcPbeeVp/cc79PhgrIh 0WU8qpJKE7NJekJfwOmyanVis9EDd7n7hkYFADs= ------=_NextPart_01C82EB7.6AE4EDE0 Content-Location: file:///C:/8ECB5C14/07dist_4.files/slide0055.htm Content-Transfer-Encoding: quoted-printable Content-Type: text/html; charset="shift_jis" データ分布と予測　4．点推定と区間推定
nX は 二項分布 B(n,p) 　　　XB(n,p)
n
nX は 正規分布 N(np, np(1-p)) に従う　　X〜N(np, np(1-p))
n
nX/n は 正規分布 N(p,p(1-p)/n) X/n〜N(p,p(1-p)/n)
n
nZ は 正規分布 N(0,1) に従う<= span style=3D'mso-tab-count:1;width:8.12%'>
母数（母比率）の推定：区間推定
母集団 〔N人〕
意見Aの人々
人数：Np人
比率：= p
意見= B= の人々
人数：N(1-p)人
比率：= 1 -= p
標本 〔n人〕
n母比率 p の推定
N人からn人を
無= 作為抽出&#= 13;
意見Aの人々
人数：X人
比率：= X/n
意見= B= の人々
人数：n-X人
比率：= (= n= -= X= )/= n
A
B
⇒ = X =3D X1 +…+ Xn
i番目の人について
Xi〜B(1,p)&#= 13;
中心極限定理
標準化：平均を引いて標準偏差で割る
PX/n
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file:///C:/8ECB5C14/07dist_4.files/slide0055_image320.gif Content-Transfer-Encoding: base64 Content-Type: image/gif R0lGODlhPQAXAHcAMSH+GlNvZnR3YXJlOiBNaWNyb3NvZnQgT2ZmaWNlACH5BAEAAAAALAAAAQA8 ABYAgAAAAAAAAAJGBBKmy+0PH1Ip2othqrl7O33iSHHkmYXoCprsy6jwbMg0bN9srqN8T/oBRcKh p2hMuZLEJfPofCqlwQOVeIheGzLttqYoAAA7 ------=_NextPart_01C82EB7.6AE4EDE0 Content-Location: file:///C:/8ECB5C14/07dist_4.files/slide0025.htm Content-Transfer-Encoding: quoted-printable Content-Type: text/html; charset="shift_jis" データ分布と予測　4．点推定と区間推定
母数（母比率）の推定：区間推定
n母比率 p の推定
¨母比率 p の信頼度 100(1-α)% の信頼区間
注：標本数 n が充分大きいときの信頼区間．  nが小さいときは，修正= 式が提案されている．
母比率は= X/n と推定
注：点推定の場合
標準正規分布 N(0,1) で Zα/2 のとる確率によって定まる
2.58
1.96
1.64
Zα/2
99%
95%
90%
信頼度
式中のPは標本比率で，P：＝X/n である
広
狭
信頼区間
信頼度・標本数と信頼区間の相対的関係
n：大
n：小
大<= /b>
小<= /b>
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母数（母比率）の推定：区間推定
n例題（出展：「図解雑学 統計解析」ナツメ社 p.170)
¨ある新聞社による内閣= 支持率調査では30001674が現行内閣を指示すると回答した．この国= の内閣支持率はどのぐらい= だろうか？　信頼度95%で母比率 p の区間推定をしよう．
¨
<= span style=3D'font-family:"Times New Roman";font-size:83%;mso-fareast-hint:yes'= >n標本比率：
n
<= span style=3D'font-family:"Times New Roman";font-size:83%;mso-fareast-hint:yes'= >n信頼度95%とはα=3D0.05，即ち，Z0.05/2=3D1.96
<= span style=3D'font-family:"Times New Roman";font-size:83%;mso-fareast-hint:yes'= >n信頼区間：
n
n
n
n
<= span style=3D'font-family:"Times New Roman";font-size:83%;mso-fareast-hint:yes'= >n故に，内閣支持率は，信頼度= 95%で 54.0%= 〜57.6= %の間にある．
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母数（母比率）の推定：区間推定
n演習（出展：「確率・統計= の仕組みがわかる本」技評p.375)
¨うち200124常用している女性が女の子を産む比率はど= の程度か？
¨
n(1) 信頼度90%で母比率 p の区間推定をせよ
n(2) 信頼度95% p の区間推定をせよ
n(3) 信頼度99%で母比率 p の区間推定をせよ
------=_NextPart_01C82EB7.6AE4EDE0 Content-Location: file:///C:/8ECB5C14/07dist_4.files/slide0057.htm Content-Transfer-Encoding: quoted-printable Content-Type: text/html; charset="shift_jis" データ分布と予測　4．点推定と区間推定
母数（母比率）の推定：区間推定（まとめ）=
n母比率の区間推定
¨Z推定
標準正規分布N(0,1)の性質を利用して母平均μの信頼区間を求める
〔信頼率 1-α〕
式中のPは標本比率で，P：＝X/n である
X は 二項分布 B<= /span>(<= span lang=3DEN-US style=3D'font-family:"Times New Roman";mso-ascii-font-family:= "Times New Roman"; mso-hansi-font-family:"Times New Roman";mso-fareast-language:JA'>n<= /span>,<= span lang=3DEN-US style=3D'font-family:"Times New Roman";mso-ascii-font-family:= "Times New Roman"; mso-hansi-font-family:"Times New Roman";mso-fareast-language:JA'>p<= /span>) = に従うが，中心極限定理から
Z は 正規分布 N<= /span>(0,1) に従う
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file:///C:/8ECB5C14/07dist_4.files/slide0029.htm Content-Transfer-Encoding: quoted-printable Content-Type: text/html; charset="shift_jis" データ分布と予測　4．点推定と区間推定
参考：母比率推定に必要な= 標本数
n適切な標本数 <= /div>
¨母比率推定における信= 頼区間の幅と上限
¨
¨
¨信頼区間の幅をβ%以内にしたい場合の標本数
¨
n
n
<= span style=3D'font-family:"Times New Roman";font-size:83%;color:#0066FF'>n例題：信頼度95%の信頼区間の幅を5%以内にしたい場合= 3;
n
n
nより，標本数は1537あれば充分．
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二つの正規母集団の推定
n= 齦ｽ均の差の区間推定 <= /span>
¨母分散が等しいとき（= 　　　　　）
¨母分散が等しくないと= き（　　　　　）
n母= ｪ散の比の区間推定
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標本平均：
標本平均：
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二つの正規母集団の平均値の差の推定
n母平均の差の区間推定（= 　　　　のとき）
¨2から，個別に2 X1,…,Xm  Y1,…,Yn の，母平均の差　　　　　　　の信頼度100(1-α)%
自由度 m<= /span>+<= span lang=3DEN-US style=3D'font-family:"Times New Roman";mso-ascii-font-family:= "Times New Roman"; mso-hansi-font-family:"Times New Roman";mso-fareast-language:JA'>n<= /span>-2 の t 分布
注：ただし，先に　　= @　　　の検定を行う必要がある．
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Content-Type: image/gif R0lGODlhRwAeAHcAMSH+GlNvZnR3YXJlOiBNaWNyb3NvZnQgT2ZmaWNlACH5BAEAAAAALAQABQA+ ABYAgwAAAAAAAAsJCgcGBwUEBAgHBwkHCAkICAcGBgsKCgwKCwMDAwoICQYFBQECAwECAwS0EMhJ KwjY6s05Dp0FSmNommV5Turqbu0avzRZz/WLd3t+9jCbjzYCUj6ZoQtZEgyYAYLS5IRKNwUoxjDt ZLVczSEqkCASNy1uTCgDzh1FYEE50Lsc+V1i7zACYXwBbngWf4EAY4QWT4sNAWg6ahqNFI+RGgaD EwIBCEdGL5qEnZ+gIwkBDQoADAsNIoUSqautrxUpEgKPngwaoS+7GAi+HkuyN8fIyjLLzTrOKEIo SdG/SAARADs= ------=_NextPart_01C82EB7.6AE4EDE0 Content-Location: file:///C:/8ECB5C14/07dist_4.files/slide0047.htm Content-Transfer-Encoding: quoted-printable Content-Type: text/html; charset="shift_jis" データ分布と予測　4．点推定と区間推定
二つの正規母集団の平均値の差の推定
n母平均の差の区間推定= i　　　　のとき）
¨2から，個別に2 X1,…,Xm  Y1,…,Yn の，母平均の差　　　　　　　の信頼度100(1-α)%
自由度νの t = 分布
ただし，νは　　　　　　　　　　　　　　　に一番近い整数であり，<= span style=3D'font-family:"ＭＳ Ｐゴシック";mso-fareast-font-family:"ＭＳ Ｐゴ= Vック";font-size: 133%;mso-fareast-hint:yes;mso-special-format:lastCR;display:none'>
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二つの正規母集団の平均値の差の推定
n例題（出展：「統計学入門= 」東大出版会 p.231）
¨20匹のラットを102赤血球数を減らすと考えられる薬を混入し= た食餌を与えた．その結果，各群のラットの血液1mm3中の赤血球数が下表のようになった．この= 薬の効果を測定したい．
8.42
8.16
8.15
8.09
8.14
8.51
8.05
8.45
8.27
8.06
対照群（100万個）
8.15
7.98
7.77
8.35
8.08
8.05
8.44
7.59
7.66
7.97
投薬群（100万個）
薬の効果（平均の差）を信頼度95%で区間推定をする．母分散は等しいと仮定．
信頼度95%　→　α=3D0.05　→　t0.025(18)=3D2.101
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二つの正規母集団の平均値の差の推定
n例題（参考：「統計学入門= 」東大出版会 p.228）
¨京都は東京より暑いか= ？
対= W本paired sample の場合は，2標本 t 統計量ではなく，差= ﾅ1標本推定を行う．
京都の方が東京より，信頼度95%で夏の最高気温平均が= 1.2〜3.8℃の間にある．　　　　　〔→　平均値の差の検定〕
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東京（℃）&#= 13;
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日付&#= 13;
（2005年8月1日〜10Yahoo!天気情報」より）
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東京-京都
信頼度95%で母平均μの区間推定をすると…
信頼度95%　→　α=3D0.05　→　t0.025(9)=3D2.262
v.s.
<= span style=3D'font-family:"ＭＳ Ｐゴシック";mso-ascii-font-family:"Times New Ro= man"; mso-fareast-font-family:"ＭＳ Ｐゴシック";mso-hansi-font-family:"Times New= Roman"; font-size:78%;mso-fareast-hint:yes'>対として対応
t 推定
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file:///C:/8ECB5C14/07dist_4.files/slide0051_image407.gif Content-Transfer-Encoding: base64 Content-Type: image/gif R0lGODlhLAAdAHcAMSH+GlNvZnR3YXJlOiBNaWNyb3NvZnQgT2ZmaWNlACH5BAEAAAAALAAAAQAr ABoAgQAAADOZZsv/ywECAwKUBIKmy+3fwpGwWhiEQPT6mgGCmHxmpI1aebahSrItyKX2yuX6zkUS DBQJg8ShsIS4aURK5s1pg6YkL2Pxas2qksqu9wtOasfYcsyJTqWX6jb7PRKb5+QhF4zPK+V1+jzR JQUXFRjI59dHdqfHiHeYiEgECLemQulmmTkRCamY0Qj69cgZSfVJ2FSYeiPD4/oK22NQAAA7 ------=_NextPart_01C82EB7.6AE4EDE0 Content-Location: file:///C:/8ECB5C14/07dist_4.files/slide0049.htm Content-Transfer-Encoding: quoted-printable Content-Type: text/html; charset="shift_jis" データ分布と予測　4．点推定と区間推定
自由度 (n-1, = m-1) = の F 分布
自由度 (m-1,<= span lang=3DEN-US style=3D'font-family:"Times New Roman";mso-ascii-font-family:= "Times New Roman"; mso-hansi-font-family:"Times New Roman";color:#3333FF;mso-fareast-language: JA'> n-1) = の F 分布
二つの正規母集団の分散値の比の推定
n母分散の比の区間推定
¨2ら，個別に2 X1,…,Xm  Y1,…,Yn 母分散の比100(1-α)%
母分散の不偏推定値：
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二つの正規母集団の分散値の比の推定
n例題（出展：「なるほど統= 計学」海鳴社 p.101)
¨Alpha君，Bravoさんの22りから技能を評価する．旋盤工工員である= 彼らが行った30oのパイプ加工の品質検査をした結果以下の通りであった．どちらが優れているのだ
2
30
4
Alpha
3
30
10
Bravo
標準偏差（o）
平均値（o）<= /span>
標本数
工員
注：腕のいい旋盤工は，実際にはこんなにずれないそうです．
信頼度90%で各々の標準偏差を区間推定すると…
結局どっちが優秀なの？
いっそのこと分散比を区間推定しよう！<= /span>
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3fsJZcGhkWdMZorJHQLXpJEUUSfH5sq2GsFpaSLyfk2e8eZl1ohBBQA7 ------=_NextPart_01C82EB7.6AE4EDE0 Content-Location: file:///C:/8ECB5C14/07dist_4.files/slide0046_image435.gif Content-Transfer-Encoding: base64 Content-Type: image/gif R0lGODlhKwAdAHcAMSH+GlNvZnR3YXJlOiBNaWNyb3NvZnQgT2ZmaWNlACH5BAEAAAAALAUAAQAm ABUAgAAAAP+ZAAI6hI+pyxEM43KySmqzwroD7mlgWI1kZJ7No5Zse73wOn90bSepKn/9DNq1MMKh o2g84iZH5dLQjCI7BQA7 ------=_NextPart_01C82EB7.6AE4EDE0 Content-Location: file:///C:/8ECB5C14/07dist_4.files/slide0050.htm Content-Transfer-Encoding: quoted-printable Content-Type: text/html; charset="shift_jis" データ分布と予測　4．点推定と区間推定
二つの正規母集団の分散値の比の推定
n例題（出展：「なるほど統計学」海鳴社 p.101)
信頼度90%で分散比を区間推定すると…
結局このデータだけからは，どちらが優秀= かはわからない．
：A<= span style=3D'font-family:"ＭＳ Ｐゴシック";mso-ascii-font-family:"Times New Ro= man"; mso-fareast-font-family:"ＭＳ Ｐゴシック";mso-hansi-font-family:"Times New= Roman"; mso-fareast-hint:yes'>の方がバラツキが小さい　→　Aの方が優秀
：B<= span style=3D'font-family:"ＭＳ Ｐゴシック";mso-ascii-font-family:"Times New Ro= man"; mso-fareast-font-family:"ＭＳ Ｐゴシック";mso-hansi-font-family:"Times New= Roman"; mso-fareast-hint:yes'>の方がバラツキが小さい　→　Bの方が優秀
優劣を判定するには，もう少しデータを集め= る必要があるよ．
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参考文献
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l東大教養学部統計学教室編　「自然科学の統計学」 東大出版会（1992）<= span style=3D'font-family:"ＭＳ Ｐゴシック";mso-ascii-font-family:"ＭＳ Ｐゴシ= bク";mso-fareast-font-family: "ＭＳ Ｐゴシック";mso-hansi-font-family:"ＭＳ Ｐゴシック";font-size:63%;co= lor:black; display:none'>
<= span style=3D'font-family:"ＭＳ Ｐゴシック";font-size:63%;mso-fareast-hint:yes'= >l鈴木達三・高橋宏一<= span style=3D'font-family:"ＭＳ Ｐゴシック";mso-ascii-font-family:"ＭＳ Ｐゴシ= bク";font-size:63%; color:black'> 「標本抽出の計画と方法」 放送大学= （1991）
<= span style=3D'font-family:"ＭＳ Ｐゴシック";font-size:63%;mso-fareast-hint:yes'= >l永田靖 「サンプルサイズの決め方」 朝倉書店= (2003)
<= span style=3D'font-family:"ＭＳ Ｐゴシック";font-size:63%;mso-fareast-hint:yes'= >l永田靖 「統計的方法のしくみ」 日科技連= （1996）
<= span style=3D'font-family:"ＭＳ Ｐゴシック";font-size:63%;mso-fareast-hint:yes'= >l村上雅人 「なるほど統計学」 海鳴社 （2002）
<= span style=3D'font-family:"ＭＳ Ｐゴシック";font-size:63%;mso-fareast-hint:yes'= >l久保応助・藤沢偉作<= span style=3D'font-family:"ＭＳ Ｐゴシック";mso-ascii-font-family:"ＭＳ Ｐゴシ= bク";font-size:63%; color:black'> 「全問精解 確率・統計演習」 聖文社 （1983）
<= span style=3D'font-family:"ＭＳ Ｐゴシック";font-size:63%;mso-fareast-hint:yes'= >l丹慶勝市 「図解雑学 統計解析」 ナツメ社 （2003）
<= span style=3D'font-family:"ＭＳ Ｐゴシック";font-size:63%;mso-fareast-hint:yes'= >l高橋信[著]・トレンドプロ[マンガ]「マンガ= でわかる統計学」オーム社（2004）
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runAnimations =3D _InitAnimations(); =09 if( IsWin("PPTSld") ) parent.SldUpdated(GetSldId()) g_origSz=3DparseInt(SlideObj.style.fontSize) g_origH=3Dsld.style.posHeight g_origW=3Dsld.style.posWidth g_scaleHyperlinks=3D(document.all.tags("AREA").length>0) if( g_scaleHyperlinks ) InitHLinkArray() if( g_scaleInFrame||(IsWin("PPTSld") && parent.IsFullScrMode() ) ) document.body.scroll=3D"no" _RSW() if( IsWin("PPTSld") && parent.IsFullScrMode() ) FullScrInit(); =09 MakeSldVis(); ChkAutoAdv() if( runAnimations ) { if( document.all("NSPlay") ) document.all("NSPlay").autoStart =3D false; if( sld.filters && sld.filters.revealtrans ) setTimeout( "document.body.start()", sld.filters.revealtrans.duration * = 1000 ); else document.body.start(); } } function MakeSldVis()=20 { var fTrans=3Dg_showAnimation && SldHasTrans() if( fTrans )=09 { if( g_bgSound ) { idx=3Dg_bgSound.indexOf(","); pptSound.src=3Dg_bgSound.substr( 0, idx ); pptSound.loop=3D -(parseInt(g_bgSound.substr(idx+1))); } SlideObj.filters.revealtrans.Apply()=09 } SlideObj.style.visibility=3D"visible" if( fTrans ) SlideObj.filters.revealtrans.Play() } function MakeNotesVis()=20 { if( !IsNts() ) return false=20 SlideObj.style.display=3D"none" nObj =3D document.all.item("NotesObj") parent.SetHasNts(0) if( nObj ) {=20 nObj.style.display=3D"" parent.SetHasNts(1) } return 1 } function ChkAutoAdv() { if(SldHasTrans()) SlideObj.onfilterchange=3DAutoAdv else AutoAdv() } function AutoAdv() { if(!IsWin("PPTSld") || !gUseSldTimings )return var sld=3DGetCurSld() if( (sld.mAdvDelay>0) && !parent.IsFramesMode() ) setTimeout("parent.GoToNextSld()",sld.mAdvDelay) } function GetObj(id) { if(g_supportsPPTHTML) return document.all(id); else return document.getElementById(id); } function SldHasTrans() { return SlideObj.style.filter !=3D ""; } function GetSldId()=20 { var regExp =3D /file:\/\/\//i var pos =3D location.href.search(regExp) if (MHTMLPrefix !=3D "" && pos !=3D -1) sId =3D location.href.substring(pos) else { sId =3D RemoveFilePrefixFromHref(location.href); var regExp =3D /\// var fixedHref =3D sId var pos =3D -1 =09 pos =3D fixedHref.search(regExp) while (pos !=3D -1) { fixedHref =3D fixedHref.replace(regExp, "\\") pos =3D fixedHref.search(regExp) } =09 if (g_fBaseHyperlink =3D=3D true) sId =3D "file:///" + fixedHref; else sId =3D fixedHref.substring(fixedHref.lastIndexOf('\\') + 1) } =09 return sId } function HideMenu() { if( frames["PPTSld"] && PPTSld.document.all.item("ctx= tmenu") && PPTSld.ctxtmenu.style.display!=3D"none" ) { PPTSld.ctxtmenu.styl= e.display=3D'none'; return true } return false } function IsWin( name ) { return window.name =3D=3D name } function IsNts() { return IsWin("PPTNts") } function IsSldOrNts() { return( IsWin("PPTSld")||IsWin("PPTNts") ) } function SupportsPPTAnimation() { return( navigator.platform =3D=3D "Win32"= && navigator.appVersion.indexOf("Windows")>0 ) } function SupportsPPTHTML() { var appVer=3Dnavigator.appVersion, msie=3DappVer.indexOf("MSIE "), ver=3D0 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"PPTSld") && !parent.IsFramesMode(); g_animUseRuntime =3D g_showAnimation && animRuntimeInstalled && !(isFullSc= reen && parent.IsSldVisited()); if( g_animUseRuntime ) { collSeq =3D document.all.tags("seq"); if( collSeq !=3D null ) { for(ii=3D0;ii numSlds ) gSldJumpIdx =3D numSlds; if ( gSldJumpIdx >=3D 0 ) { if ( gSldJumpIdx =3D=3D 0 ) gSldJumpIdx =3D 1; var jumpTo =3D parseInt(gSldJumpIdx); gSldJump =3D 0; gSldJumpIdx =3D ""; win.GoToSld( parent.GetSldList().mList[jumpTo-1].mSldHref ) } } } function _KDH() { if( event.keyCode =3D=3D 8 ) { event.returnValue =3D 0; parent.GoToPrevSld(); } }function DocumentOnClick() { if( IsNts() || parent.HideMenu() ) return; if( ( g_allowAdvOnClick && (window.name=3D=3D"PPTSld") && !parent.IsFrames= Mode() ) || (event && event.keyCode=3D=3D32) ) { =09 if( g_animUseRuntime && g_animMainSequence && g_animMainSequence.cangonex= t ) return; parent.GoToNextSld(); } } var g_supportsPPTHTML =3D SupportsPPTHTML(), g_scaleInFrame =3D 1, gId=3D""= , g_bgSound=3D"", g_scaleHyperlinks =3D false, g_allowAdvOnClick =3D 1, g_showInBrowser = =3D 0, gLoopCont =3D 0, gUseSldTimings =3D 1; var g_showAnimation =3D g_supportsPPTHTML && SupportsPPTAnimation() && ( (w= indow.name=3D=3D"PPTSld" && !parent.IsFramesMode()) || g_showInBrowser );va= r g_animManager =3D null; var g_animUseRuntime =3D false; var g_animItemsToHide, g_animInteractiveItems, g_animSlideTime; var g_animMainSequence =3D null; var ENDSHOW_MESG=3D"スライド ショーの最後です。 クリックすると終了します。"= , SCREEN_MODE=3D"Frames", gIsEndShow=3D0, NUM_VIS_SLDS=3D50, SCRIPT_HREF=3D= "script.js", FULLSCR_HREF=3D"fullscreen.htm"; var gCurSld =3D gPrevSld =3D 1, g_offset =3D 0, gNtsOpen =3D gHasNts =3D gO= tlTxtExp =3D 0, gHasNarration =3D 0, gOtlOpen =3D true window.gPPTHTML=3DSupportsPPTHTML() var g_fBaseHyperlink =3D false; var gMainDoc=3Dnew Array(new hrefList("slide0001.htm",1,-1,1),new hrefList(= "slide0036.htm",1,-1,1),new hrefList("slide0003.htm",1,-1,1),new hrefList("= slide0037.htm",1,-1,1),new hrefList("slide0009.htm",1,-1,1),new hrefList("s= lide0002.htm",1,-1,1),new hrefList("slide0038.htm",1,-1,1),new hrefList("sl= ide0041.htm",1,-1,1),new hrefList("slide0039.htm",1,-1,1),new hrefList("sli= de0040.htm",1,-1,1),new hrefList("slide0045.htm",1,-1,1),new hrefList("slid= e0004.htm",1,-1,1),new hrefList("slide0005.htm",1,-1,1),new hrefList("slide= 0033.htm",1,-1,1),new hrefList("slide0035.htm",1,-1,1),new hrefList("slide0= 006.htm",1,-1,1),new hrefList("slide0007.htm",1,-1,1),new hrefList("slide00= 08.htm",1,-1,1),new hrefList("slide0044.htm",1,-1,1),new hrefList("slide001= 0.htm",1,-1,1),new hrefList("slide0012.htm",1,-1,1),new hrefList("slide0011= .htm",1,-1,1),new hrefList("slide0013.htm",1,-1,1),new hrefList("slide0043.= htm",1,-1,1),new hrefList("slide0015.htm",1,-1,1),new hrefList("slide0053.h= tm",1,-1,1),new hrefList("slide0054.htm",1,-1,1),new hrefList("slide0014.ht= m",1,-1,1),new hrefList("slide0017.htm",1,-1,1),new hrefList("slide0018.htm= ",1,-1,1),new hrefList("slide0019.htm",1,-1,1),new hrefList("slide0030.htm"= ,1,-1,1),new hrefList("slide0048.htm",1,-1,1),new hrefList("slide0020.htm",= 1,-1,1),new hrefList("slide0021.htm",1,-1,1),new hrefList("slide0055.htm",1= ,-1,1),new hrefList("slide0025.htm",1,-1,1),new hrefList("slide0028.htm",1,= -1,1),new hrefList("slide0031.htm",1,-1,1),new hrefList("slide0057.htm",1,-= 1,1),new hrefList("slide0029.htm",1,-1,1),new hrefList("slide0056.htm",1,-1= ,1),new hrefList("slide0034.htm",1,-1,1),new hrefList("slide0047.htm",1,-1,= 1),new hrefList("slide0052.htm",1,-1,1),new hrefList("slide0051.htm",1,-1,1= ),new hrefList("slide0049.htm",1,-1,1),new hrefList("slide0046.htm",1,-1,1)= ,new hrefList("slide0050.htm",1,-1,1),new hrefList("slide0042.htm",1,-1,1)); /********************************************* Frameset functions These functions control slide navigation and state of the frameset. **********************************************/ function RemoveFilePrefixFromHref(href) { var regExp =3D /^file:\/\/\//i; return href.replace(regExp, "") } function FullScrInit() { g_allowAdvOnClick =3D GetCurSld().mAdvOnClk document.body.style.backgroundColor=3D"black" document.oncontextmenu=3Dparent._CM; document.onkeydown =3D _KDH; document.ondragstart=3DCancel document.onselectstart=3DCancel self.focus() } function Redirect( frmId ) {=09 var str=3Ddocument.location.hash,idx=3Dstr.indexOf('#'), sId=3DGetSldId() if(idx>=3D0) str=3Dstr.substr(1); if( window.name !=3D frmId && ( sId !=3D str) ) { obj =3D GetObj("Main-File") window.location.href=3Dobj.href+"#"+sId return 1 } return 0 } var MHTMLPrefix =3D CalculateMHTMLPrefix();=20 function CalculateMHTMLPrefix() { if ( document.location.protocol =3D=3D 'mhtml:') {=20 href=3Dnew String(document.location.href)=20 Start=3Dhref.indexOf('!')+1=20 End=3Dhref.lastIndexOf('/')+1=20 if (End < Start)=20 return href.substring(0, Start)=20 else=20 return href.substring(0, End)=20 } return ''; } function GetTags(base,tag) { if(g_supportsPPTHTML) return base.all.tags(tag); else return base.getElementsByTagName(tag); } function UpdNtsPane(){ if(frames["PPTNts"]) PPTNts.location.replace( MHTMLP= refix+GetHrefObj( gCurSld ).mNtsHref ) } function UpdNavPane( sldIndex ){ if(gNavLoaded) PPTNav.UpdNav() } function UpdOtNavPane(){ if(gOtlNavLoaded) PPTOtlNav.UpdOtlNav() } function UpdOtlPane(){ if(gOtlLoaded) PPTOtl.UpdOtl() } function SetHasNts( fVal ) { if( gHasNts !=3D fVal ) { gHasNts=3DfVal UpdNavPane() } } function ToggleOtlText() { gOtlTxtExp=3D!gOtlTxtExp UpdOtlPane() } function ClearMedia() { // Clear any sounds playing before launching another browser window. Other= wise, // in fullscreen mode, you'll continue to hear the sound in the frames mod= e. if (PPTSld.pptSound) PPTSld.pptSound.loop =3D 0; } function FullScreen() {=20 if ( PPTSld.g_animUseRuntime ) PPTSld.document.body.pause(); ClearMedia(); var href =3D ( document.location.protocol =3D=3D 'mhtml:') ? FULLSCR_HREF = : FULLSCR_HREF+"#"+GetHrefObj(gCurSld).mSldHref; if (MHTMLPrefix !=3D "") href =3D RemoveFilePrefixFromHref(href) if(PPTNav.event.ctrlKey) { var w =3D (window.screen.availWidth * 1.0) / 2.0 var h =3D w * (PPTSld.g_origH * 1.0) / PPTSld.g_origW win =3D window.open( MHTMLPrefix+href,null,"toolbar=3D0,resizable=3D1,top= =3D0,left=3D0," + "width=3D"+ w + ",height=3D" + h ); if( win.document.body && PPTSld.g_animUseRuntime ) win.document.body.PPTSldFrameset=3Dwindow; } else { win =3D window.open( MHTMLPrefix+href,null,"fullscreen=3Dyes" ); if( win.document.body && PPTSld.g_animUseRuntime ) win.document.body.PPTSldFrameset=3Dwindow; } } function ToggleVNarration() { rObj=3DPPTSld.document.all("NSPlay") if( rObj && !PPTSld.g_animUseRuntime ) { if( (rObj.playState =3D=3D 1)||(rObj.playState =3D=3D 0) ) rObj.Play() else if( rObj.playState =3D=3D 2 ) rObj.Pause() else return; } else if( PPTSld.g_animUseRuntime ) { narObj =3D PPTSld.document.all("narrationID") if( narObj ) narObj.togglePause() } } function GetCurSldNum() { =20 obj=3DGetHrefObj(gCurSld) if( obj.mOrigVis =3D=3D 1 ) return obj.mSldIdx else =20 return gCurSld } function GetNumSlds() { =20 if( GetHrefObj(gCurSld).mOrigVis =3D=3D 1 ) return GetSldList().mNumVisSlds; else return GetSldList().mList.length } function GetSldNum( href ) { for(ii=3D0; ii 1 ) PopSldList(); else if( !IsFramesMode() ) { if( gLoopCont ) GoToFirst() else EndShow() } } function GoToPrevSld() { ii=3DgCurSld-1 if( ii > 0 ) { obj=3DGetHrefObj(ii) while ( obj && ( obj.mVis =3D=3D 0 ) && ( ii>0 ) ) obj=3DGetHrefObj(--ii) if( ii =3D=3D 0 ) ii=3D1 GoToSldNum(ii) } } function GoToFirst(){ GoToSld( GetHrefObj(1).mSldHref ) } function GoToLast() { ii=3DGetSldList().mList.length if( ii !=3D gCurSld ) GoToSld( GetHrefObj(ii).mSldHref ) } function GoToSldNum( num ) { if( PPTSld.event ) PPTSld.event.cancelBubble=3Dtrue obj =3D GetHrefObj( num ) obj.mVis=3D1 gPrevSld=3DgCurSld gCurSld =3D num; =09 if (MHTMLPrefix !=3D "") PPTSld.location.replace(MHTMLPrefix+RemoveFilePrefixFromHref(obj.mSldHref= )) else PPTSld.location.replace(obj.mSldHref) =09 if( IsFramesMode() ) { UpdNavPane(); UpdOtlPane(); UpdNtsPane() } } function GoToSld( href ) { if( PPTSld.event ) PPTSld.event.cancelBubble=3Dtrue GetHrefObj( GetSldNum(href) ).mVis=3D1 if (MHTMLPrefix !=3D "") PPTSld.location.replace(MHTMLPrefix+RemoveFilePrefixFromHref(href)) else PPTSld.location.replace(href) } function SldUpdated( id ) { if( id =3D=3D GetHrefObj(gCurSld).mSldHref ) return gPrevSld=3DgCurSld gCurSld=3DGetSldNum(id) if( IsFramesMode() ) { UpdNavPane(); UpdOtlPane(); UpdNtsPane() } } function PrevSldViewed(){ GoToSld( GetHrefObj(gPrevSld).mSldHref ) } function HasPrevSld() { return ( gIsEndShow || ( gCurSld !=3D 1 && GetHrefO= bj( gCurSld-1 ).mVis =3D=3D 1 )||( GetCurSldNum() > 1 ) ) } function HasNextSld() { return (GetCurSldNum() !=3D GetNumSlds()) } function CloseWindow() { if( HideMenu() ) return; =09 var event =3D PPTSld.event; if( !IsFramesMode() && event && (event.keyCode=3D=3D27 || event.keyCode=3D= =3D32 || event.type=3D=3D"click" ) ) window.close( self ); CatchNumKeys( self, event ); } function Unload() { gIsEndShow=3D0; } function SetupEndShow() { gIsEndShow=3D1; PPTSld.document.body.scroll=3D"no"; PPTSld.document.onkeypress=3DCloseWindow; PPTSld.document.onclick=3DCloseWindow; PPTSld.document.oncontextmenu=3D_CM; } function EndShow() { if( IsFramesMode() ) return if( PPTSld.event ) PPTSld.event.cancelBubble=3Dtrue doc=3DPPTSld.document var dir =3D doc.body.dir if( dir !=3D "rtl" ) dir =3D "ltr"; doc.open() doc.writeln('


' + ENDSHOW_MESG + '

') doc.close() } function SetSldVisited(){ GetSldList().mList[gCurSld-1].mVisited=3Dtrue } function IsSldVisited(){ return GetSldList().mList[gCurSld-1].mVisited } function hrefList( sldHref, visible, advDelay, advClk ) { this.mSldHref=3D this.mNtsHref =3D sldHref this.mOrigVis=3D this.mVis =3D visible this.mVisited=3D false this.mAdvDelay=3D advDelay this.mAdvOnClk=3D advClk } function SldList(arr,curSld,fEnd) { this.mCurSld =3D curSld; this.mList =3D new Array(); var idx =3D 1; for(ii=3D0;ii 0) { PushSldList(sldList,fEnd); gCurSld =3D 1; } else if( PPTSld.event ) PPTSld.event.cancelBubble=3Dtrue } function PushSldList(arr,fEnd) { var ii =3D gSldStack.length; gSldStack[ii] =3D new SldList(arr,gCurSld,fEnd); GoToSld( gSldStack[ii].mList[0].mSldHref ); } function PopSldList() { if (gSldStack[gSldStack.length-1].fEndShow) EndShow() else { gCurSld =3D gSldStack[gSldStack.length-1].mCurSld; gSldStack[gSldStack.length-1] =3D null; gSldStack.length--; var sldList =3D gSldStack[gSldStack.length-1]; GoToSld( sldList.mList[gCurSld - 1].mSldHref ); } } var custShowList=3Dnew Array(); /********************************************* Navigation button implementation There are 2 types of buttons: ImgBtn, TxtBtn implemented as function objects. They share a similiar interface so the event handlers can call SetActive, for example, on a button=20 object without needing to know exactly=20 what type of button it is. **********************************************/ //---------------------------------- function ImgBtn( oId,bId,w,action ) //---------------------------------- { var t=3Dthis t.Perform =3D _IBP t.SetActive =3D _IBSetA t.SetInactive=3D _IBSetI t.SetPressed =3D _IBSetP t.SetDisabled=3D _IBSetD t.Enabled =3D _IBSetE t.ChangeIcon =3D null t.UserAction =3D action t.ChgState =3D _IBUI t.mObjId =3D oId t.mBorderId=3D bId t.mWidth =3D w t.mIsOn =3D t.mCurState =3D 0 } function _IBSetA() { if( this.mIsOn ) { obj=3Dthis.ChgState( gHiliteClr,gShadowClr,2 ) obj.style.posTop=3D0 } } function _IBSetI() { if( this.mIsOn ) { obj=3Dthis.ChgState( gFaceClr,gFaceClr,1 ) obj.style.posTop=3D0=20 } } function _IBSetP() { if( this.mIsOn ) { obj=3Dthis.ChgState( gShadowClr,gHiliteClr,2 ) obj.style.posLeft+=3D1; obj.style.posTop+=3D1 } } function _IBSetD() { =20 obj=3Dthis.ChgState( gFaceClr,gFaceClr,0 ) obj.style.posTop=3D0=20 } function _IBSetE( state ) { var t=3Dthis GetObj( t.mBorderId ).style.visibility=3D"visible" if( state !=3D t.mIsOn ) { t.mIsOn=3Dstate if( state ) t.SetInactive() else t.SetDisabled() } } function _IBP() { var t=3Dthis if( t.mIsOn ) { if( t.UserAction !=3D null ) t.UserAction() if( t.ChangeIcon ) { obj=3DGetObj(t.mObjId) if( t.ChangeIcon() ) obj.style.posLeft=3Dobj.style.posLeft+(t.mCurState-4)*t.mWidth else obj.style.posLeft=3Dobj.style.posLeft+(t.mCurState-0)*t.mWidth } t.SetActive() } =20 } function _IBUI( clr1,clr2,nextState ) { var t=3Dthis SetBorder( GetObj( t.mBorderId ),clr1,clr2 ) obj=3DGetObj( t.mObjId ) obj.style.posLeft=3Dobj.style.posLeft+(t.mCurState-nextState)*t.mWidth-obj= .style.posTop t.mCurState=3DnextState return obj } //----------------------------------------- function TxtBtn( oId,oeId,action,chkState ) //----------------------------------------- { var t=3Dthis t.Perform =3D _TBP t.SetActive =3D _TBSetA t.SetInactive=3D _TBSetI t.SetPressed =3D _TBSetP t.SetDisabled=3D _TBSetD t.SetEnabled =3D _TBSetE t.GetState =3D chkState t.UserAction =3D action t.ChgState =3D _TBUI t.mObjId =3D oId t.m_elementsId=3D oeId t.mIsOn =3D 1 } function _TBSetA() { var t=3Dthis if( t.mIsOn && !t.GetState() ) t.ChgState( gHiliteClr,gShadowClr,0,0 ) } function _TBSetI() { var t=3Dthis if( t.mIsOn && !t.GetState() ) t.ChgState( gFaceClr,gFaceClr,0,0 ) } function _TBSetP() { if( this.mIsOn ) this.ChgState( gShadowClr,gHiliteClr,1,1 ) } function _TBSetD() { =20 this.ChgState( gFaceClr,gFaceClr,0,0 ) this.mIsOn =3D 0 } function _TBSetE() { var t=3Dthis if( !t.GetState() ) t.ChgState( gFaceClr,gFaceClr,0,0 ) else t.ChgState( gShadowClr,gHiliteClr,1,1 ) t.mIsOn =3D 1 } function _TBP() { var t=3Dthis if( t.mIsOn ) {=20 if( t.UserAction !=3D null ) t.UserAction() if( !t.GetState ) return if( t.GetState() ) t.SetPressed() else t.SetActive() } =20 } function _TBUI( clr1,clr2,lOffset,tOffset ) { SetBorder( GetObj( this.mObjId ),clr1,clr2 ) Offset( GetObj( this.m_elementsId ),lOffset,tOffset ) } function Offset( obj, top, left ){ obj.style.top=3Dtop; obj.style.left=3Dle= ft } function SetBorder( obj, upperLeft, lowerRight ) { s=3Dobj.style; s.borderStyle =3D "solid" s.borderWidth =3D 1=20 s.borderLeftColor =3D s.borderTopColor =3D upperLeft s.borderBottomColor=3D s.borderRightColor =3D lowerRight } function GetBtnObj(){ return gBtnArr[window.event.srcElement.id] } function BtnOnOver(){ b=3DGetBtnObj(); if( b !=3D null ) b.SetActive() } function BtnOnDown(){ b=3DGetBtnObj(); if( b !=3D null ) b.SetPressed() } function BtnOnOut(){ b=3DGetBtnObj(); if( b !=3D null ) b.SetInactive() } function BtnOnUp() { b=3DGetBtnObj() if( b !=3D null ) b.Perform() else Upd() } function GetNtsState(){ return parent.gNtsOpen } function GetOtlState(){ return parent.gOtlOpen } function GetOtlTxtState(){ return parent.gOtlTxtExp } function NtsBtnSetFlag( fVal ) { s=3Ddocument.all.item( this.m_flagId ).style s.display=3D"none" if( fVal ) s.display=3D"" else s.display=3D"none" } function _BSetA_Border(){ b =3D gBtnArr[this.mObjId]; if( b !=3D null ) b.S= etActive() } function _BSetI_Border(){ b =3D gBtnArr[this.mObjId]; if( b !=3D null ) b.S= etInactive() } function _BSetP_Border(){ b =3D gBtnArr[this.mObjId]; if( b !=3D null ) b.S= etPressed() } function _BSetA_BorderImg() {=20 b =3D gBtnArr[this.mBorderId]=20 if( b !=3D null && this.mIsOn && !b.GetState() ) { obj=3Dthis.ChgState( gHiliteClr,gShadowClr,2 ) obj.style.posTop=3D0 } } function _BSetI_BorderImg() {=20 b =3D gBtnArr[this.mBorderId] if( b !=3D null && this.mIsOn && !b.GetState() ) { obj=3Dthis.ChgState( gFaceClr,gFaceClr,1 ) obj.style.posTop=3D0 } } var gHiliteClr=3D"THREEDHIGHLIGHT",gShadowClr=3D"THREEDSHADOW",gFaceClr=3D"= THREEDFACE" var gBtnArr =3D new Array() gBtnArr["nb_otl"] =3D new TxtBtn( "nb_otl","nb_otlElem",parent.ToggleOtlPan= e,GetOtlState ) gBtnArr["nb_otlElem"] =3D new TxtBtn( "nb_otl","nb_otlElem",parent.ToggleOt= lPane,GetOtlState ) gBtnArr["nb_nts"] =3D new ImgBtn( "nb_nts","nb_ntsBorder",10,parent.ToggleN= tsPane ) gBtnArr["nb_nts"].SetActive =3D _BSetA_BorderImg; gBtnArr["nb_nts"].SetInactive =3D _BSetI_BorderImg; gBtnArr["nb_ntsBorder"] =3D new TxtBtn( "nb_ntsBorder","nb_ntsElem",parent.= ToggleNtsPane,GetNtsState ) gBtnArr["nb_ntsElem"] =3D new TxtBtn( "nb_ntsBorder","nb_ntsElem",parent.To= ggleNtsPane,GetNtsState ) gBtnArr["nb_prevBorder"] =3D gBtnArr["nb_prev"]=3D new ImgBtn( "nb_prev","n= b_prevBorder",30,parent.GoToPrevSld ) gBtnArr["nb_nextBorder"] =3D gBtnArr["nb_next"]=3D new ImgBtn( "nb_next","n= b_nextBorder",30,parent.GoToNextSld ) gBtnArr["nb_sldshw"]=3D new ImgBtn( "nb_sldshw","nb_sldshwBorder",18,parent= .FullScreen ) gBtnArr["nb_sldshwBorder"] =3D new TxtBtn( "nb_sldshw","nb_sldshwBorder",pa= rent.FullScreen,null ) gBtnArr["nb_sldshwBorder"].SetActive =3D _BSetA_Border; gBtnArr["nb_sldshwBorder"].SetInactive =3D _BSetI_Border; gBtnArr["nb_sldshwText"] =3D new TxtBtn( "nb_sldshw","nb_sldshwText",parent= .FullScreen,null ) gBtnArr["nb_sldshwText"].SetActive =3D _BSetA_Border; gBtnArr["nb_sldshwText"].SetInactive =3D _BSetI_Border; gBtnArr["nb_voice"] =3D gBtnArr["nb_voiceBorder"] =3D new ImgBtn( "nb_voice= ","nb_voiceBorder",18,parent.ToggleVNarration ) gBtnArr["nb_otlTxtBorder"] =3D gBtnArr["nb_otlTxt"]=3D new ImgBtn( "nb_otlT= xt","nb_otlTxtBorder",23,parent.ToggleOtlText ) gBtnArr["nb_ntsBorder"].m_flagId=3D "nb_nts" gBtnArr["nb_ntsBorder"].SetFlag =3D NtsBtnSetFlag gBtnArr["nb_otlTxt"].ChangeIcon=3D GetOtlTxtState /********************************************* Context menu implementation _CM() is the function that's hooked up to the oncontextmenu event. Once we're asked to show the menu, we first build it by creating DIVs on-the-fly. Then we position it=20 within the screen area so it doesn't get clipped. Creating the DIVs using createElement() means we don't have to write out any extra HTML into the slide HTML files. **********************************************/ var sNext=3D"次へ",sPrev=3D"前へ",sEnd=3D"スライド ショーの終了",sFont=3D"= lＳ 明朝",sArrow=3D"矢印",sFreeform=3D"フリーフォーム",sRect=3D"四角形",sOv= al=3D"楕円" function ShowMenu() { BuildMenu(); var doc=3DPPTSld.document.body,x=3DPPTSld.event.clientX+doc.scrollLeft,y= =3DPPTSld.event.clientY+doc.scrollTop m =3D PPTSld.document.all.item("ctxtmenu") m.style.pixelLeft=3Dx if( (x+m.scrollWidth > doc.clientWidth)&&(x-m.scrollWidth > 0) ) m.style.pixelLeft=3Dx-m.scrollWidth m.style.pixelTop=3Dy if( (y+m.scrollHeight > doc.clientHeight)&&(y-m.scrollHeight > 0) ) m.style.pixelTop=3Dy-m.scrollHeight m.style.display=3D"" } function _CM() { if( !parent.IsFullScrMode() ) return; if(!PPTSld.event.ctrlKey) { ShowMenu() return false } else HideMenu() } function BuildMenu() { if( PPTSld.document.all.item("ctxtmenu") ) return var mObj=3DCreateItem( PPTSld.document.body ) mObj.id=3D"ctxtmenu" mObj.style.visibility=3D"hidden" var s=3DmObj.style s.position=3D"absolute" s.cursor=3D"default" s.width=3D"120px" SetCMBorder(mObj,"menu","black") var iObj=3DCreateItem( mObj ) SetCMBorder( iObj, "threedhighlight","threedshadow" ) iObj.style.padding=3D2 CreateMenuItem( iObj,sNext,M_GoNextSld,M_True ) CreateMenuItem( iObj,sPrev,M_GoPrevSld,M_HasPrevSld ) =09 CreateSeparator( iObj ) CreateMenuItem( iObj,sEnd,M_End,M_True ) mObj.style.visibility=3D"visible" } function Cancel() { window.event.cancelBubble=3Dtrue; window.event.returnVa= lue=3Dfalse } function Highlight() { ChangeClr("activecaption","threedhighlight") } function Deselect() { ChangeClr("threedface","menutext") } function Perform() { e=3DPPTSld.event.srcElement if( e.type=3D=3D"menuitem" && e.IsActive() ) e.Action() else PPTSld.event.cancelBubble=3Dtrue } function ChangeClr( bg,clr ) { e=3DPPTSld.event.srcElement if( e.type=3D=3D"menuitem" && e.IsActive() ) { e.style.backgroundColor=3Dbg e.style.color=3Dclr } } function M_HasPrevSld() { return( parent.HasPrevSld() ) } function M_GoNextSld() { if( gIsEndShow ) M_End(); else GoToNextSld() } function M_GoPrevSld() { if( gIsEndShow ) { gIsEndShow=3D0; history.back();= PPTSld.event.cancelBubble=3Dtrue; } else GoToPrevSld() } function M_True() { return true } function M_End() { window.close( self ) } function CreateMenuItem( node,text,action,eval ) { var e=3DCreateItem( node ) e.type=3D"menuitem" e.Action=3Daction e.IsActive=3Deval e.innerHTML=3Dtext if( !e.IsActive() ) e.style.color=3D"threedshadow" e.onclick=3DPerform e.onmouseover=3DHighlight e.onmouseout=3DDeselect s=3De.style; s.fontFamily=3DsFont s.fontSize=3D"9pt" s.paddingLeft=3D2 } function CreateSeparator( node ) { var sObj=3DCreateItem( node ) SetCMBorder(sObj,"menu","menu") var s=3DsObj.style s.borderTopColor=3D"threedshadow" s.borderBottomColor=3D"threedhighlight" s.height=3D1 s.fontSize=3D"0px" } function CreateItem( node ) { var elem=3DPPTSld.document.createElement("DIV") node.insertBefore( elem ) return elem } function SetCMBorder( o,ltClr,rbClr ) { var s=3Do.style s.backgroundColor=3D"menu" s.borderStyle=3D"solid" s.borderWidth=3D1 s.borderColor=3DltClr+" "+rbClr+" "+rbClr+" "+ltClr } ------=_NextPart_01C82EB7.6AE4EDE0 Content-Location: file:///C:/8ECB5C14/07dist_4.files/fullscreen.htm Content-Transfer-Encoding: quoted-printable Content-Type: text/html; charset="shift_jis" ------=_NextPart_01C82EB7.6AE4EDE0 Content-Location: file:///C:/8ECB5C14/07dist_4.files/buttons.gif Content-Transfer-Encoding: base64 Content-Type: image/gif R0lGODlhWAESAPf4AAAAAIAAAACAAICAAAAAgIAAgACAgICAgAQEBISEBASEBISEhAQEhMTExAQE /KTM9Pz8/ERERPz8BAT8/KSkpGRkhMTcxCRkxAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAMDAwP8AAAD/AP//AAAA//8A/wD//////yH5BAEAAPgALAAAAABYARIA QAj/APEJHEiwoMGDCBMqXMiwocOHECNKnEixosWLGCEiQKCRo8YDIEHig2DxAD4LKD1avDDhgUuV GUOGbHBR5QGYFBG4fHAB58EKECJEwCfUYAWPFGAygLCRKQIIUKM6WAohKUGgCikcFWh1INWoYKVS 7SrQ41GbIhGYLAuVQVOoC+IyXUuUAkG1HE3KFDhAwkgJc5/qDZmxsOHDiBMrXsy4sePDGyP7lChT 5MULmDPnlDw5YmW6FTNrfgi0aNGEZPEtbfo2bNTUpYeeRph69dPWrqvCNFuBIGG1A59CYPAVQtwF cw0OxWdTIOiBCQAjaAB17fOFII8voGmRc+eOkilm/z9OvTtnjN4hYk1YoT3H2rnBOphPAB9skgq3 2lcaX/5YnJVt9BloAar12UEIRGCZdQghMABI1Tn32IQUVthYexhmqGGGMBHAgIfEhSiiiATAtOGJ GnYI4ogsEldicAYOGNJGZaml3Y0L3NTZUBz1xlB0PUoUI0jlVWRSVN89xBKSGB1YU1TMoRfVBEle hR9HQs1GEHxQ3RZZbveZthxt/LEmXHyp5XWABSCpaRJwbM13gAACPHVccgJFgF+NEhqUAEkQSCfc ddhVhh5dVTaEAEsTUNlkZUWKN9BNGDE6QU8OrSdbllzpxyVTTrU21Uj3scfhp7nNpyoDaTIEJ3P9 If8J2kbLNYegg9JRBwGhFvbq66+ZUiDssMQWK2yHxiZLrInKNotss8l+B5yBrr5p2UIJvortANoC 6+234IbLla/elRtZjeamh5CC3Sb0YLvixivvYkDFGlZqBNh7r4n6goVvv6/ZlCicN+kL71ALTnpQ dCIZp7CihkLA3UQmoWRBogyxtBPGCTnZncVR1rSTo5nuydC/Eo8kUH+lOoTyyvjoCuZu+PjYIKLD rZybtrM1d10C0QXKEVz48GrQkEeWdFJKlbb0UmEeV0SzyC6RvNB646LWIVjUNeC1zAFb+RDKXksM 9r531bwmpQW9+hQDDsx5pqwDRWBXcFES2hc+gEn/wJGdEEWdE6LoWcoxQweaTNmkhydkKaYMYY2Q p1unbLl/pPKbn3v7EZRvrKvqlja2ONvrtoIw9tn2g32/tQBlhtYkU+MNikZ7QYlPDPuMldpOmuJX nVo5VF3HjI+q89mteX5IDd/fVFClma5kiE4f2XUJhlz09gj2NbTDRifkmu7zXjR++RVJ/lPznuvr QH2iD6S+UewP9Dnon6d2NLwN8n90BB6hC68cFMCHoe+ACASP9c4DowUyEF0OPFcDI0ij/VXPaAKC UfhS95AKGoQ7IBRICI1HwhGaUIQoLGEKT6jCFrLwhSuMoQtlCMMZ2rCGOKShDm+4wxzy8Ic+DGIP B4cIRCKGMCAAOy== ------=_NextPart_01C82EB7.6AE4EDE0 Content-Location: file:///C:/8ECB5C14/07dist_4.files/frame.htm Content-Transfer-Encoding: quoted-printable Content-Type: text/html; charset="shift_jis" データ分布と予測　4．点推定と区間推定 ------=_NextPart_01C82EB7.6AE4EDE0 Content-Location: file:///C:/8ECB5C14/07dist_4.files/outline.htm Content-Transfer-Encoding: quoted-printable Content-Type: text/html; charset="shift_jis"
 ノ= ート
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アウトライン
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