計(jì)量經(jīng)濟(jì)第四章ppt課件-閱讀頁(yè)
2025-05-18 07:37本頁(yè)面
【正文】 j c, fails to reject H0 if tbj =c. 由于 t分布是對(duì)稱(chēng)的，如果 H0: bj = 0對(duì) H1: bj 0,當(dāng) tbj c時(shí)我們拒絕 H0，當(dāng) tbj =c ，則不能拒絕 H0 yi = b0 + b1xi1 + … + bkxik + ui H0: bj = 0 H1: bj 0 c 0 a ?1 ? a? OneSided Alternatives (cont) 單邊替代假設(shè) Fail to reject reject Example: Student Performance and School Size 例子：學(xué)生表現(xiàn)與學(xué)校規(guī)模 ? Question: Does larger class size results in poorer student performance? 問(wèn)題：是不是較大的班級(jí)意味著較差的學(xué)生表現(xiàn)？ ? Use 408 high schools in Michigan for year 1993, perform the following regression: 應(yīng)用 1993年 408個(gè)密歇根州中學(xué)的數(shù)據(jù)，進(jìn)行如下回歸 ^math10=++ () () () –.0002enroll () math10: percentage of students passing the MEAP standardized grade 10 math test 通過(guò) MEAP標(biāo)準(zhǔn)化 10年級(jí)數(shù)學(xué)測(cè)驗(yàn)的學(xué)生百分比 totp: average annual teacher’s pensation 平均教師年度補(bǔ)償 staff : the number of staff per one thousand students 每千個(gè)學(xué)生對(duì)應(yīng)的工作人員數(shù)目 enroll : student enrollment 學(xué)生錄取 ? Decide the testing hypotheses: 確定被檢驗(yàn)的假設(shè) ? H0 :βenroll=0 versus H1 :βenroll0 ? Compute the t statistic, 計(jì)算 t統(tǒng)計(jì)量 t=? Since nk1=404, we use the standard normal critical value. At the 5% level, the critical value is –. 由于 nk1=404，我們使用標(biāo)準(zhǔn)正態(tài)的臨界值。由于 ，故在 1%顯著水平下拒絕零假設(shè)。在此替代假設(shè)下，我們并未規(guī)定 xj 對(duì) y影響的符號(hào)。當(dāng) t的 絕對(duì)值 大于臨界值 c時(shí)，拒絕零假設(shè)。 yi = b0 + b1Xi1 + … + bkXik + ui H0: bj = 0 H1: bj ?= 0 c 0 a/2 ?1 ? a? c a/2 TwoSided Alternatives 雙邊替代假設(shè) reject reject fail to reject Example: Student Performance and School Size continued(example ) 例子：學(xué)生表現(xiàn)與學(xué)校規(guī)模 ? We have estimated 我們已經(jīng)得到 ^math10=++ –.0002enroll () () () () ? If the question is: whether the number of teachers has impacts on student performance, we can form hypotheses of: H0: bstaff = 0 , H1: bstaff ?= 0. 如果問(wèn)題是：教師數(shù)目是否對(duì)學(xué)生表現(xiàn)有影響，我們可以檢驗(yàn)如下假設(shè)： H0: bstaff = 0 , H1: bstaff 不等于 0. ? The calculated t ratio is . The 5% critical value of standard normal is . Since , we fail to reject the null 計(jì)算得到的 t值為 。由于 ，我們不能拒絕零假設(shè)。對(duì)于 95自由度的 t分布， 1%顯著水平下單邊檢驗(yàn)的臨界值為 ，拒絕零假設(shè)。 Computing pvalues for t Tests 計(jì)算 t檢驗(yàn)的 p值 ? Suppose at 40 degrees of freedom, a calculated t ratio is , the related 5% and 1% critical values are and , respectively. Should we reject or not to reject the null? 假設(shè)自由度為 40，算得 t 值為 ，對(duì)應(yīng) 5%和1%的臨界值分別為 和 。 Computing pvalues for t Tests 計(jì)算 t檢驗(yàn)的 p值 ? An alternative to the classical approach : If the calculated t statistic is used as critical value, what is the smallest significance level at which the null hypothesis would be rejected? 另一種想法：如果將算得的 t 統(tǒng)計(jì)量作為臨界值，那么使得零假設(shè)被拒絕的最小顯著水平是多少？ ? This level is known as the pvalue. For a twosided alternative, 這個(gè)水平稱(chēng)為 p
教學(xué)課件相關(guān)推薦
鄂ICP備17016276號(hào)-1