Lampiran 2. Hasil Uji Statistik
|
|
|
- Mitchell Dickerson
- 7 years ago
- Views:
Transcription
1 Lampiran 2. Hasil Uji Statistik Indeks probabilitas Indeks probabilitas FMA Descriptives Statistic Std, Error 47,975 4, % Confidence Interval for Lower Bound 38,403 Upper Bound 57,547 5% Trimmed 45,556 Median 40,250 Variance 895,820 Std, Deviation 29,9303 Minimum 7,0 Maximum 139,5 Range 132,5 Interquartile Range 46,1 Skewness 1,135,374 Kurtosis 1,458,733 51,225 4, % Confidence Interval for Lower Bound 42,443 Upper Bound 60,007 5% Trimmed 49,833 Median 46,250 Variance 754,038 Std, Deviation 27,4597 Minimum 9,0 Maximum 138,0 Range 129,0 Interquartile Range 35,9 Skewness,823,374 Kurtosis 1,151,733 27,763, % Confidence Interval for Lower Bound 25,846 Upper Bound 29,679 5% Trimmed 27,875 Median 28,750 Variance 35,897
2 FMA Std, Deviation 5,9914 Minimum 17,0 Maximum 37,0 Range 20,0 Interquartile Range 10,3 Skewness -,263,374 Kurtosis -1,050,733 27,313, % Confidence Interval for Lower Bound 25,488 Upper Bound 29,137 5% Trimmed 27,375 Median 27,000 Variance 32,560 Std, Deviation 5,7061 Minimum 16,0 Maximum 37,0 Range 21,0 Interquartile Range 9,8 Skewness -,179,374 Kurtosis -,967,733 ANB 6 or less 5,538,1683 ANB 6 or less 95% Confidence Interval for Lower Bound 5,197 Upper Bound 5,878 5% Trimmed 5,486 Median 5,250 Variance 1,133 Std, Deviation 1,0645 Minimum 4,0 Maximum 8,0 Range 4,0 Interquartile Range 1,0 Skewness,803,374 Kurtosis -,082,733 5,325, % Confidence Interval for Lower Bound 4,896 Upper Bound 5,754 5% Trimmed 5,319 Median 5,000
3 FMIA 60 or more FMIA 60 or more OOC PL 7 or less Variance 1,802 Std, Deviation 1,3424 Minimum 3,0 Maximum 8,0 Range 5,0 Interquartile Range 2,4 Skewness,219,374 Kurtosis -,856,733 51,313, % Confidence Interval for Lower Bound 49,466 Upper Bound 53,159 5% Trimmed 51,514 Median 51,000 Variance 33,329 Std, Deviation 5,7732 Minimum 36,0 Maximum 63,0 Range 27,0 Interquartile Range 8,8 Skewness -,441,374 Kurtosis,136,733 49,075, % Confidence Interval for Lower Bound 47,330 Upper Bound 50,820 5% Trimmed 49,097 Median 48,750 Variance 29,763 Std, Deviation 5,4556 Minimum 36,0 Maximum 60,0 Range 24,0 Interquartile Range 7,6 Skewness,056,374 Kurtosis -,103,733 9,975, % Confidence Interval for Lower Bound 8,903 Upper Bound 11,047 5% Trimmed 10,000
4 OOC PL 7 or less SNB 80 or more SNB 80 or more Median 10,500 Variance 11,230 Std, Deviation 3,3511 Minimum 3,0 Maximum 16,0 Range 13,0 Interquartile Range 5,4 Skewness -,164,374 Kurtosis -,688,733 9,938, % Confidence Interval for Lower Bound 9,026 Upper Bound 10,849 5% Trimmed 9,806 Median 9,250 Variance 8,118 Std, Deviation 2,8492 Minimum 4,0 Maximum 17,5 Range 13,5 Interquartile Range 2,8 Skewness,988,374 Kurtosis 1,341,733 78,888, % Confidence Interval for Lower Bound 78,036 Upper Bound 79,739 5% Trimmed 78,986 Median 79,000 Variance 7,083 Std, Deviation 2,6614 Minimum 72,0 Maximum 83,0 Range 11,0 Interquartile Range 2,5 Skewness -,591,374 Kurtosis,102,733 78,988, % Confidence Interval for Lower Bound 78,140 Upper Bound 79,835
5 5% Trimmed 79,000 Median 79,250 Variance 7,019 Std, Deviation 2,6494 Minimum 73,0 Maximum 84,0 Range 11,0 Interquartile Range 3,4 Skewness,081,374 Kurtosis -,473,733
6 Tests of Normality Kolmogorov-Smirnov a Shapiro-Wilk Statistic df Sig, Statistic df Sig, Indeks probabilitas,146 40,031,910 40,004 Indeks probabilitas,093 40,200 *,955 40,109 FMA 20-30,109 40,200 *,948 40,062 FMA 20-30,086 40,200 *,965 40,250 ANB 6 or less,214 40,000,902 40,002 ANB 6 or less,171 40,005,945 40,053 FMIA 60 or more,092 40,200 *,972 40,421 FMIA 60 or more,069 40,200 *,985 40,868 OOC PL 7 or less,128 40,097,968 40,320 OOC PL 7 or less,241 40,000,907 40,003 SNB 80 or more,167 40,007,947 40,061 SNB 80 or more,110 40,200 *,961 40,188 a, Lilliefors Significance Correction *, This is a lower bound of the true significance, T-Test Paired Samples Statistics N Std, Deviation Std, Error Pair 1 Indeks probabilitas 47, ,9303 4,7324 Indeks probabilitas 51, ,4597 4,3418 Pair 2 FMA , ,9914,9473 FMA , ,7061,9022 Pair 3 ANB 6 or less 5, ,0645,1683 ANB 6 or less 5, ,3424,2122 Pair 4 FMIA 60 or more 51, ,7732,9128 FMIA 60 or more 49, ,4556,8626 Pair 5 OOC PL 7 or less 9, ,3511,5299 OOC PL 7 or less 9, ,8492,4505 Pair 6 SNB 80 or more 78, ,6614,4208 SNB 80 or more 78, ,6494,4189 Paired Samples Correlations N Correlation Sig, Pair 1 Indeks probabilitas & Indeks probabilitas 40,838,000
7 Pair 2 FMA & FMA ,947,000 Pair 3 ANB 6 or less & ANB 6 or less 40,740,000 Pair 4 FMIA 60 or more & FMIA 60 or more 40,321,043 Pair 5 OOC PL 7 or less & OOC PL 7 or less 40,801,000 Pair 6 SNB 80 or more & SNB 80 or more 40,914,000 Paired Samples Test Paired Differences 95% Confidence Interval of the Difference Pair 1 Indeks probabilitas - Indeks probabilitas Std, Std, Error Deviation Lower Upper t df Sig, (2- tailed) -3, ,5270 2,6131-8,5356 2,0356-1,244 39,221 Pair 2 FMA FMA 20-30,4500 1,9209,3037 -,1643 1,0643 1,482 39,146 Pair 3 ANB 6 or less - ANB 6 or less,2125,9050,1431 -,0769,5019 1,485 39,146 Pair 4 Pair 5 Pair 6 FMIA 60 or more - FMIA 60 or more OOC PL 7 or less - OOC PL 7 or less SNB 80 or more - SNB 80 or more 2,2375 6,5476 1,0353,1435 4,3315 2,161 39,037,0375 2,0108,3179 -,6056,6806,118 39,907 -,1000 1,0990,1738 -,4515,2515 -,576 39,568
8 Explore (Ekstraksi) Indeks probabilitas Indeks probabilitas FMA Descriptives Statistic Std, Error 55,432 7,4989 Lower Bound 39,837 Upper Bound 71,027 5% Trimmed 53,470 Median 42,250 Variance 1237,150 Std, Deviation 35,1731 Minimum 7,0 Maximum 139,5 Range 132,5 Interquartile Range 50,6 Skewness,820,491 Kurtosis,246,953 54,386 6,5874 Lower Bound 40,687 Upper Bound 68,086 5% Trimmed 52,475 Median 47,500 Variance 954,665 Std, Deviation 30,8977 Minimum 9,0 Maximum 138,0 Range 129,0 Interquartile Range 39,4 Skewness,842,491 Kurtosis 1,057,953 28,477 1,2360 Lower Bound 25,907 Upper Bound 31,048 5% Trimmed 28,631 Median 29,500 Variance 33,607 Std, Deviation 5,7971 Minimum 18,0
9 FMA ANB 6 or less ANB 6 or less Maximum 36,0 Range 18,0 Interquartile Range 10,1 Skewness -,335,491 Kurtosis -1,216,953 28,273 1,2050 Lower Bound 25,767 Upper Bound 30,779 5% Trimmed 28,389 Median 28,750 Variance 31,946 Std, Deviation 5,6521 Minimum 18,5 Maximum 36,0 Range 17,5 Interquartile Range 9,9 Skewness -,310,491 Kurtosis -1,173,953 5,955,2460 Lower Bound 5,443 Upper Bound 6,466 5% Trimmed 5,947 Median 5,750 Variance 1,331 Std, Deviation 1,1538 Minimum 4,0 Maximum 8,0 Range 4,0 Interquartile Range 2,0 Skewness,275,491 Kurtosis -,883,953 5,614,3014 Lower Bound 4,987 Upper Bound 6,240 5% Trimmed 5,652 Median 5,500 Variance 1,998 Std, Deviation 1,4136
10 FMIA 60 or more FMIA 60 or more OOC PL 7 or less Minimum 3,0 Maximum 7,5 Range 4,5 Interquartile Range 2,5 Skewness -,268,491 Kurtosis -1,177,953 49,705 1,3890 Lower Bound 46,816 Upper Bound 52,593 5% Trimmed 49,732 Median 49,000 Variance 42,444 Std, Deviation 6,5149 Minimum 36,0 Maximum 63,0 Range 27,0 Interquartile Range 9,1 Skewness -,042,491 Kurtosis -,059,953 49,727 1,2723 Lower Bound 47,081 Upper Bound 52,373 5% Trimmed 49,717 Median 49,250 Variance 35,613 Std, Deviation 5,9676 Minimum 39,5 Maximum 60,0 Range 20,5 Interquartile Range 9,5 Skewness,190,491 Kurtosis -,873,953 9,864,7503 Lower Bound 8,303 Upper Bound 11,424 5% Trimmed 9,904 Median 11,000 Variance 12,385
11 OOC PL 7 or less SNB 80 or more SNB 80 or more Std, Deviation 3,5193 Minimum 3,0 Maximum 16,0 Range 13,0 Interquartile Range 5,3 Skewness -,224,491 Kurtosis -,629,953 10,114,6587 Lower Bound 8,744 Upper Bound 11,484 5% Trimmed 10,058 Median 9,750 Variance 9,546 Std, Deviation 3,0897 Minimum 4,0 Maximum 17,0 Range 13,0 Interquartile Range 3,4 Skewness,703,491 Kurtosis,730,953 78,273,5062 Lower Bound 77,220 Upper Bound 79,325 5% Trimmed 78,460 Median 79,000 Variance 5,636 Std, Deviation 2,3741 Minimum 72,0 Maximum 81,0 Range 9,0 Interquartile Range 3,5 Skewness -1,182,491 Kurtosis,961,953 78,409,5093 Lower Bound 77,350 Upper Bound 79,468 5% Trimmed 78,457 Median 79,250
12 Variance 5,706 Std, Deviation 2,3886 Minimum 73,0 Maximum 83,0 Range 10,0 Interquartile Range 3,6 Skewness -,435,491 Kurtosis -,184,953
13 Tests of Normality Kolmogorov-Smirnov a Shapiro-Wilk Statistic df Sig, Statistic df Sig, Indeks probabilitas,173 22,086,918 22,069 Indeks probabilitas,128 22,200 *,945 22,255 FMA 20-30,146 22,200 *,928 22,114 FMA 20-30,141 22,200 *,929 22,115 ANB 6 or less,153 22,196,949 22,306 ANB 6 or less,200 22,022,916 22,063 FMIA 60 or more,112 22,200 *,983 22,956 FMIA 60 or more,098 22,200 *,968 22,666 OOC PL 7 or less,172 22,089,960 22,488 OOC PL 7 or less,242 22,002,920 22,077 SNB 80 or more,211 22,012,884 22,014 SNB 80 or more,202 22,020,936 22,163 a, Lilliefors Significance Correction *, This is a lower bound of the true significance, T-Test (Ekstraksi) Paired Samples Statistics N Std, Deviation Std, Error Pair 1 Indeks probabilitas 55, ,1731 7,4989 Indeks probabilitas 54, ,8977 6,5874 Pair 2 FMA , ,7971 1,2360 FMA , ,6521 1,2050 Pair 3 ANB 6 or less 5, ,1538,2460 ANB 6 or less 5, ,4136,3014 Pair 4 FMIA 60 or more 49, ,5149 1,3890 FMIA 60 or more 49, ,9676 1,2723 Pair 5 OOC PL 7 or less 9, ,5193,7503 OOC PL 7 or less 10, ,0897,6587 Pair 6 SNB 80 or more 78, ,3741,5062 SNB 80 or more 78, ,3886,5093 Paired Samples Correlations N Correlation Sig, Pair 1 Indeks probabilitas & Indeks probabilitas 22,882,000 Pair 2 FMA & FMA ,943,000
14 Pair 3 ANB 6 or less & ANB 6 or less 22,719,000 Pair 4 FMIA 60 or more & FMIA 60 or more 22,351,109 Pair 5 OOC PL 7 or less & OOC PL 7 or less 22,836,000 Pair 6 SNB 80 or more & SNB 80 or more 22,907,000 Paired Samples Test Paired Differences 95% Confidence Interval of the Difference Pair 1 Indeks probabilitas - Indeks probabilitas Pair 2 FMA FMA Pair 3 ANB 6 or less - ANB 6 or less Pair 4 FMIA 60 or more - FMIA 60 or more Pair 5 OOC PL 7 or less - OOC PL 7 or less Pair 6 SNB 80 or more - SNB 80 or more Std, Deviation Std, Error Lower Upper t df Sig, (2- tailed) 1, ,5450 3,5274-6,2902 8,3811,296 21,770,2045 1,9375,4131 -,6545 1,0636,495 21,626,3409,9927,2116 -,0992,7810 1,611 21,122 -,0227 7,1239 1,5188-3,1813 3,1358 -,015 21,988 -,2500 1,9380,4132-1,1093,6093 -,605 21,552 -,1364 1,0256,2187 -,5911,3184 -,624 21,540
15 Explore (Non Ekstraksi) Indeks probabilitas Indeks probabilitas FMA Descriptives Statistic Std, Error 38,861 4,5152 Lower Bound 29,335 Upper Bound 48,387 5% Trimmed 38,457 Median 37,000 Variance 366,965 Std, Deviation 19,1563 Minimum 7,0 Maximum 78,0 Range 71,0 Interquartile Range 28,1 Skewness,374,536 Kurtosis -,259 1,038 47,361 5,3842 Lower Bound 36,001 Upper Bound 58,721 5% Trimmed 46,401 Median 45,750 Variance 521,818 Std, Deviation 22,8433 Minimum 13,0 Maximum 99,0 Range 86,0 Interquartile Range 36,8 Skewness,390,536 Kurtosis -,089 1,038 26,889 1,4789 Lower Bound 23,769 Upper Bound 30,009 5% Trimmed 26,877 Median 27,750 Variance 39,369 Std, Deviation 6,2745
16 FMA ANB 6 or less ANB 6 or less Minimum 17,0 Maximum 37,0 Range 20,0 Interquartile Range 9,3 Skewness -,162,536 Kurtosis -,885 1,038 26,139 1,3454 Lower Bound 23,300 Upper Bound 28,977 5% Trimmed 26,099 Median 26,250 Variance 32,583 Std, Deviation 5,7081 Minimum 16,0 Maximum 37,0 Range 21,0 Interquartile Range 8,9 Skewness -,050,536 Kurtosis -,556 1,038 5,028,1590 Lower Bound 4,692 Upper Bound 5,363 5% Trimmed 4,975 Median 5,000 Variance,455 Std, Deviation,6746 Minimum 4,0 Maximum 7,0 Range 3,0 Interquartile Range 1,0 Skewness 1,181,536 Kurtosis 3,590 1,038 4,972,2814 Lower Bound 4,378 Upper Bound 5,566 5% Trimmed 4,914 Median 5,000 Variance 1,426
17 FMIA 60 or more FMIA 60 or more OOC PL 7 or less Std, Deviation 1,1940 Minimum 3,0 Maximum 8,0 Range 5,0 Interquartile Range 1,5 Skewness,919,536 Kurtosis 1,536 1,038 53,278,9602 Lower Bound 51,252 Upper Bound 55,304 5% Trimmed 53,364 Median 53,000 Variance 16,595 Std, Deviation 4,0737 Minimum 46,0 Maximum 59,0 Range 13,0 Interquartile Range 6,6 Skewness -,324,536 Kurtosis -,948 1,038 48,278 1,1320 Lower Bound 45,889 Upper Bound 50,666 5% Trimmed 48,475 Median 48,250 Variance 23,065 Std, Deviation 4,8026 Minimum 36,0 Maximum 57,0 Range 21,0 Interquartile Range 5,9 Skewness -,571,536 Kurtosis 1,496 1,038 10,111,7611 Lower Bound 8,505 Upper Bound 11,717 5% Trimmed 10,123 Median 10,000
18 OOC PL 7 or less SNB 80 or more SNB 80 or more Variance 10,428 Std, Deviation 3,2293 Minimum 4,0 Maximum 16,0 Range 12,0 Interquartile Range 5,6 Skewness -,055,536 Kurtosis -,702 1,038 9,722,6120 Lower Bound 8,431 Upper Bound 11,013 5% Trimmed 9,497 Median 9,000 Variance 6,742 Std, Deviation 2,5965 Minimum 6,0 Maximum 17,5 Range 11,5 Interquartile Range 2,5 Skewness 1,585,536 Kurtosis 3,884 1,038 79,639,6750 Lower Bound 78,215 Upper Bound 81,063 5% Trimmed 79,765 Median 79,750 Variance 8,200 Std, Deviation 2,8636 Minimum 74,0 Maximum 83,0 Range 9,0 Interquartile Range 4,3 Skewness -,589,536 Kurtosis -,464 1,038 79,694,6705 Lower Bound 78,280 Upper Bound 81,109
19 5% Trimmed 79,688 Median 79,250 Variance 8,092 Std, Deviation 2,8447 Minimum 75,5 Maximum 84,0 Range 8,5 Interquartile Range 5,3 Skewness,239,536 Kurtosis -1,415 1,038
20 Tests of Normality Kolmogorov-Smirnov a Shapiro-Wilk Statistic df Sig, Statistic df Sig, Indeks probabilitas,094 18,200 *,979 18,945 Indeks probabilitas,095 18,200 *,970 18,806 FMA 20-30,118 18,200 *,948 18,402 FMA 20-30,101 18,200 *,983 18,973 ANB 6 or less,239 18,008,854 18,010 ANB 6 or less,213 18,030,925 18,162 FMIA 60 or more,153 18,200 *,930 18,192 FMIA 60 or more,104 18,200 *,965 18,706 OOC PL 7 or less,110 18,200 *,977 18,917 OOC PL 7 or less,235 18,010,872 18,019 SNB 80 or more,134 18,200 *,909 18,082 SNB 80 or more,211 18,034,897 18,051 a, Lilliefors Significance Correction *, This is a lower bound of the true significance, T-Test (Non Ekstraksi) Paired Samples Statistics N Std, Deviation Std, Error Pair 1 Indeks probabilitas 38, ,1563 4,5152 Indeks probabilitas 47, ,8433 5,3842 Pair 2 FMA , ,2745 1,4789 FMA , ,7081 1,3454 Pair 3 ANB 6 or less 5,028 18,6746,1590 ANB 6 or less 4, ,1940,2814 Pair 4 FMIA 60 or more 53, ,0737,9602 FMIA 60 or more 48, ,8026 1,1320 Pair 5 OOC PL 7 or less 10, ,2293,7611 OOC PL 7 or less 9, ,5965,6120 Pair 6 SNB 80 or more 79, ,8636,6750 SNB 80 or more 79, ,8447,6705
21
22 Paired Samples Correlations N Correlation Sig, Pair 1 Indeks probabilitas & Indeks probabilitas 18,747,000 Pair 2 FMA & FMA ,953,000 Pair 3 ANB 6 or less & ANB 6 or less 18,786,000 Pair 4 FMIA 60 or more & FMIA 60 or more 18,478,045 Pair 5 OOC PL 7 or less & OOC PL 7 or less 18,762,000 Pair 6 SNB 80 or more & SNB 80 or more 18,910,000 Paired Samples Test Paired Differences 95% Confidence Interval of the Difference Pair 1 Indeks probabilitas - Indeks probabilitas Pair 2 FMA FMA Pair 3 ANB 6 or less - ANB 6 or less Pair 4 FMIA 60 or more - FMIA 60 or more Pair 5 OOC PL 7 or less - OOC PL 7 or less Std, Std, Error Deviation Lower Upper t -8, ,3431 3, ,1300 -,8700-2,350 Sig, (2- df tailed) 17,031,7500 1,9117,4506 -,2006 1,7006 1,665 17,114,0556,7838,1847 -,3342,4453,301 17,767 5,0000 4,5794 1,0794 2,7227 7,2773 4,632 17,000,3889 2,0973,4943 -,6541 1,4319,787 17,442
23 Paired Samples Test Paired Differences 95% Confidence Interval of the Difference Pair 1 Indeks probabilitas - Indeks probabilitas Pair 2 FMA FMA Pair 3 ANB 6 or less - ANB 6 or less Pair 4 FMIA 60 or more - FMIA 60 or more Pair 5 OOC PL 7 or less - OOC PL 7 or less Pair 6 SNB 80 or more - SNB 80 or more Std, Std, Error Deviation Lower Upper -8, ,3431 3, ,1300 -,8700-2,350 t df Sig, (2- tailed) 17,031,7500 1,9117,4506 -,2006 1,7006 1,665 17,114,0556,7838,1847 -,3342,4453,301 17,767 5,0000 4,5794 1,0794 2,7227 7,2773 4,632 17,000,3889 2,0973,4943 -,6541 1,4319,787 17,442 -,0556 1,2113,2855 -,6579,5468 -,195 17,848
24 Lampiran 3. Hasil Uji T test Nilai p pencabutan dan tanpa pencabutan pada maloklusi Klas II Variabel Median SD CI 95 % Low Up IP IP FMA FMA ANB ANB FMIA FMIA OOC PL OOC PL SNB SNB t p Nilai p kasus pencabutan pada maloklusi Klas II Variabel Median SD CI 95 % t Low Up p IP IP FMA FMA ANB ANB FMIA FMIA OOC PL OOC PL SNB SNB
25 Nilai p kasus tanpa pencabutan pada maloklusi Klas II Variabel Median SD CI 95 % t Low Up p IP IP FMA FMA ANB ANB FMIA FMIA OOC PL OOC PL SNB SNB
26
27
Chapter 2 Probability Topics SPSS T tests
Chapter 2 Probability Topics SPSS T tests Data file used: gss.sav In the lecture about chapter 2, only the One-Sample T test has been explained. In this handout, we also give the SPSS methods to perform
Independent t- Test (Comparing Two Means)
Independent t- Test (Comparing Two Means) The objectives of this lesson are to learn: the definition/purpose of independent t-test when to use the independent t-test the use of SPSS to complete an independent
UNDERSTANDING THE INDEPENDENT-SAMPLES t TEST
UNDERSTANDING The independent-samples t test evaluates the difference between the means of two independent or unrelated groups. That is, we evaluate whether the means for two independent groups are significantly
THE KRUSKAL WALLLIS TEST
THE KRUSKAL WALLLIS TEST TEODORA H. MEHOTCHEVA Wednesday, 23 rd April 08 THE KRUSKAL-WALLIS TEST: The non-parametric alternative to ANOVA: testing for difference between several independent groups 2 NON
INTERPRETING THE ONE-WAY ANALYSIS OF VARIANCE (ANOVA)
INTERPRETING THE ONE-WAY ANALYSIS OF VARIANCE (ANOVA) As with other parametric statistics, we begin the one-way ANOVA with a test of the underlying assumptions. Our first assumption is the assumption of
Two Related Samples t Test
Two Related Samples t Test In this example 1 students saw five pictures of attractive people and five pictures of unattractive people. For each picture, the students rated the friendliness of the person
Introduction to Statistics with SPSS (15.0) Version 2.3 (public)
Babraham Bioinformatics Introduction to Statistics with SPSS (15.0) Version 2.3 (public) Introduction to Statistics with SPSS 2 Table of contents Introduction... 3 Chapter 1: Opening SPSS for the first
Biostatistics: DESCRIPTIVE STATISTICS: 2, VARIABILITY
Biostatistics: DESCRIPTIVE STATISTICS: 2, VARIABILITY 1. Introduction Besides arriving at an appropriate expression of an average or consensus value for observations of a population, it is important to
Chapter 7 Section 7.1: Inference for the Mean of a Population
Chapter 7 Section 7.1: Inference for the Mean of a Population Now let s look at a similar situation Take an SRS of size n Normal Population : N(, ). Both and are unknown parameters. Unlike what we used
7. Comparing Means Using t-tests.
7. Comparing Means Using t-tests. Objectives Calculate one sample t-tests Calculate paired samples t-tests Calculate independent samples t-tests Graphically represent mean differences In this chapter,
UNDERSTANDING THE DEPENDENT-SAMPLES t TEST
UNDERSTANDING THE DEPENDENT-SAMPLES t TEST A dependent-samples t test (a.k.a. matched or paired-samples, matched-pairs, samples, or subjects, simple repeated-measures or within-groups, or correlated groups)
PsychTests.com advancing psychology and technology
PsychTests.com advancing psychology and technology tel 514.745.8272 fax 514.745.6242 CP Normandie PO Box 26067 l Montreal, Quebec l H3M 3E8 contact@psychtests.com Psychometric Report Resilience Test Description:
Introduction to Analysis of Variance (ANOVA) Limitations of the t-test
Introduction to Analysis of Variance (ANOVA) The Structural Model, The Summary Table, and the One- Way ANOVA Limitations of the t-test Although the t-test is commonly used, it has limitations Can only
Examining Differences (Comparing Groups) using SPSS Inferential statistics (Part I) Dwayne Devonish
Examining Differences (Comparing Groups) using SPSS Inferential statistics (Part I) Dwayne Devonish Statistics Statistics are quantitative methods of describing, analysing, and drawing inferences (conclusions)
A study on impact of RBI decision to deregulate savings account interest rates on the banking industry.
Sharada P. Chauhan Entire Research Academy s SPC ERA International Journal of E-Commerce and E-Banking Exact Papers URL :www.spcera.in/home/ Issue IJECEB A study on impact of RBI decision to deregulate
How To Test For Significance On A Data Set
Non-Parametric Univariate Tests: 1 Sample Sign Test 1 1 SAMPLE SIGN TEST A non-parametric equivalent of the 1 SAMPLE T-TEST. ASSUMPTIONS: Data is non-normally distributed, even after log transforming.
SCHOOL OF HEALTH AND HUMAN SCIENCES DON T FORGET TO RECODE YOUR MISSING VALUES
SCHOOL OF HEALTH AND HUMAN SCIENCES Using SPSS Topics addressed today: 1. Differences between groups 2. Graphing Use the s4data.sav file for the first part of this session. DON T FORGET TO RECODE YOUR
Chapter 7. One-way ANOVA
Chapter 7 One-way ANOVA One-way ANOVA examines equality of population means for a quantitative outcome and a single categorical explanatory variable with any number of levels. The t-test of Chapter 6 looks
Chapter 7. Comparing Means in SPSS (t-tests) Compare Means analyses. Specifically, we demonstrate procedures for running Dependent-Sample (or
1 Chapter 7 Comparing Means in SPSS (t-tests) This section covers procedures for testing the differences between two means using the SPSS Compare Means analyses. Specifically, we demonstrate procedures
One-Way Analysis of Variance
One-Way Analysis of Variance Note: Much of the math here is tedious but straightforward. We ll skim over it in class but you should be sure to ask questions if you don t understand it. I. Overview A. We
KSTAT MINI-MANUAL. Decision Sciences 434 Kellogg Graduate School of Management
KSTAT MINI-MANUAL Decision Sciences 434 Kellogg Graduate School of Management Kstat is a set of macros added to Excel and it will enable you to do the statistics required for this course very easily. To
SPSS Tests for Versions 9 to 13
SPSS Tests for Versions 9 to 13 Chapter 2 Descriptive Statistic (including median) Choose Analyze Descriptive statistics Frequencies... Click on variable(s) then press to move to into Variable(s): list
Skewed Data and Non-parametric Methods
0 2 4 6 8 10 12 14 Skewed Data and Non-parametric Methods Comparing two groups: t-test assumes data are: 1. Normally distributed, and 2. both samples have the same SD (i.e. one sample is simply shifted
A Basic Guide to Analyzing Individual Scores Data with SPSS
A Basic Guide to Analyzing Individual Scores Data with SPSS Step 1. Clean the data file Open the Excel file with your data. You may get the following message: If you get this message, click yes. Delete
Impact Of January Revolution On Financial Ratios Of Egyptian Life Insurance Market
Impact Of January Revolution On Financial Ratios Of Egyptian Life Insurance Market Lobna Hussein, Cairo University, Egypt ABSTRACT Egyptian insurance market consists of Life insurance companies and Non-Life
Statistics Review PSY379
Statistics Review PSY379 Basic concepts Measurement scales Populations vs. samples Continuous vs. discrete variable Independent vs. dependent variable Descriptive vs. inferential stats Common analyses
1. What is the critical value for this 95% confidence interval? CV = z.025 = invnorm(0.025) = 1.96
1 Final Review 2 Review 2.1 CI 1-propZint Scenario 1 A TV manufacturer claims in its warranty brochure that in the past not more than 10 percent of its TV sets needed any repair during the first two years
Chapter 13 Introduction to Linear Regression and Correlation Analysis
Chapter 3 Student Lecture Notes 3- Chapter 3 Introduction to Linear Regression and Correlation Analsis Fall 2006 Fundamentals of Business Statistics Chapter Goals To understand the methods for displaing
Chapter 5 Analysis of variance SPSS Analysis of variance
Chapter 5 Analysis of variance SPSS Analysis of variance Data file used: gss.sav How to get there: Analyze Compare Means One-way ANOVA To test the null hypothesis that several population means are equal,
Confidence Intervals for Cp
Chapter 296 Confidence Intervals for Cp Introduction This routine calculates the sample size needed to obtain a specified width of a Cp confidence interval at a stated confidence level. Cp is a process
STATISTICAL ANALYSIS WITH EXCEL COURSE OUTLINE
STATISTICAL ANALYSIS WITH EXCEL COURSE OUTLINE Perhaps Microsoft has taken pains to hide some of the most powerful tools in Excel. These add-ins tools work on top of Excel, extending its power and abilities
Standard Deviation Estimator
CSS.com Chapter 905 Standard Deviation Estimator Introduction Even though it is not of primary interest, an estimate of the standard deviation (SD) is needed when calculating the power or sample size of
Determinants of the Total Quality Management Implementation in SMEs in Iran (Case of Metal Industry)
International Journal of Business and Social Science Vol. 4 No. 16; December 2013 Determinants of the Total Quality Management Implementation in SMEs in Iran (Case of Metal Industry) Hamed Ramezani Planning
Chapter 9. Two-Sample Tests. Effect Sizes and Power Paired t Test Calculation
Chapter 9 Two-Sample Tests Paired t Test (Correlated Groups t Test) Effect Sizes and Power Paired t Test Calculation Summary Independent t Test Chapter 9 Homework Power and Two-Sample Tests: Paired Versus
Bill Burton Albert Einstein College of Medicine william.burton@einstein.yu.edu April 28, 2014 EERS: Managing the Tension Between Rigor and Resources 1
Bill Burton Albert Einstein College of Medicine william.burton@einstein.yu.edu April 28, 2014 EERS: Managing the Tension Between Rigor and Resources 1 Calculate counts, means, and standard deviations Produce
Testing for differences I exercises with SPSS
Testing for differences I exercises with SPSS Introduction The exercises presented here are all about the t-test and its non-parametric equivalents in their various forms. In SPSS, all these tests can
Impact of Enrollment Timing on Performance: The Case of Students Studying the First Course in Accounting
Journal of Accounting, Finance and Economics Vol. 5. No. 1. September 2015. Pp. 1 9 Impact of Enrollment Timing on Performance: The Case of Students Studying the First Course in Accounting JEL Code: M41
Regression Analysis: A Complete Example
Regression Analysis: A Complete Example This section works out an example that includes all the topics we have discussed so far in this chapter. A complete example of regression analysis. PhotoDisc, Inc./Getty
COMPARISONS OF CUSTOMER LOYALTY: PUBLIC & PRIVATE INSURANCE COMPANIES.
277 CHAPTER VI COMPARISONS OF CUSTOMER LOYALTY: PUBLIC & PRIVATE INSURANCE COMPANIES. This chapter contains a full discussion of customer loyalty comparisons between private and public insurance companies
2 Sample t-test (unequal sample sizes and unequal variances)
Variations of the t-test: Sample tail Sample t-test (unequal sample sizes and unequal variances) Like the last example, below we have ceramic sherd thickness measurements (in cm) of two samples representing
PSYCHOLOGY 320L Problem Set #3: One-Way ANOVA and Analytical Comparisons
PSYCHOLOGY 30L Problem Set #3: One-Way ANOVA and Analytical Comparisons Name: Score:. You and Dr. Exercise have decided to conduct a study on exercise and its effects on mood ratings. Many studies (Babyak
Online versus Traditional Learning: A Comparison Study of Colorado Community College Science Classes
Online versus Traditional Learning: A Comparison Study of Colorado Community College Science Classes Introduction Students are currently given more and more options in postsecondary education be it the
Simple linear regression
Simple linear regression Introduction Simple linear regression is a statistical method for obtaining a formula to predict values of one variable from another where there is a causal relationship between
Summary of Formulas and Concepts. Descriptive Statistics (Ch. 1-4)
Summary of Formulas and Concepts Descriptive Statistics (Ch. 1-4) Definitions Population: The complete set of numerical information on a particular quantity in which an investigator is interested. We assume
T Tests and Related Statistics: SPSS
T Tests and Related Statistics: SPSS One Sample T Tests Correlated T Tests Independent Samples T Tests Nonparametric Tests Before you boot up SPSS, obtain the following data files from my SPSS Data Page:
Week 4: Standard Error and Confidence Intervals
Health Sciences M.Sc. Programme Applied Biostatistics Week 4: Standard Error and Confidence Intervals Sampling Most research data come from subjects we think of as samples drawn from a larger population.
Descriptive and Inferential Statistics
General Sir John Kotelawala Defence University Workshop on Descriptive and Inferential Statistics Faculty of Research and Development 14 th May 2013 1. Introduction to Statistics 1.1 What is Statistics?
LAB 4 INSTRUCTIONS CONFIDENCE INTERVALS AND HYPOTHESIS TESTING
LAB 4 INSTRUCTIONS CONFIDENCE INTERVALS AND HYPOTHESIS TESTING In this lab you will explore the concept of a confidence interval and hypothesis testing through a simulation problem in engineering setting.
Good luck! BUSINESS STATISTICS FINAL EXAM INSTRUCTIONS. Name:
Glo bal Leadership M BA BUSINESS STATISTICS FINAL EXAM Name: INSTRUCTIONS 1. Do not open this exam until instructed to do so. 2. Be sure to fill in your name before starting the exam. 3. You have two hours
Comparing the Awesome Oscillator to a Time-Based Trade: A Framework for Testing Stock Trading Algorithms
Comparing the Awesome Oscillator to a Time-Based Trade: A Framework for Testing Stock Trading Algorithms Stephen Russell Information Systems Department University of Maryland Baltimore County Stephen.russell@umbc.edu
How Far is too Far? Statistical Outlier Detection
How Far is too Far? Statistical Outlier Detection Steven Walfish President, Statistical Outsourcing Services steven@statisticaloutsourcingservices.com 30-325-329 Outline What is an Outlier, and Why are
THE FIRST SET OF EXAMPLES USE SUMMARY DATA... EXAMPLE 7.2, PAGE 227 DESCRIBES A PROBLEM AND A HYPOTHESIS TEST IS PERFORMED IN EXAMPLE 7.
THERE ARE TWO WAYS TO DO HYPOTHESIS TESTING WITH STATCRUNCH: WITH SUMMARY DATA (AS IN EXAMPLE 7.17, PAGE 236, IN ROSNER); WITH THE ORIGINAL DATA (AS IN EXAMPLE 8.5, PAGE 301 IN ROSNER THAT USES DATA FROM
NCSS Statistical Software
Chapter 06 Introduction This procedure provides several reports for the comparison of two distributions, including confidence intervals for the difference in means, two-sample t-tests, the z-test, the
HYPOTHESIS TESTING WITH SPSS:
HYPOTHESIS TESTING WITH SPSS: A NON-STATISTICIAN S GUIDE & TUTORIAL by Dr. Jim Mirabella SPSS 14.0 screenshots reprinted with permission from SPSS Inc. Published June 2006 Copyright Dr. Jim Mirabella CHAPTER
Principles of Hypothesis Testing for Public Health
Principles of Hypothesis Testing for Public Health Laura Lee Johnson, Ph.D. Statistician National Center for Complementary and Alternative Medicine johnslau@mail.nih.gov Fall 2011 Answers to Questions
NCSS Statistical Software
Chapter 06 Introduction This procedure provides several reports for the comparison of two distributions, including confidence intervals for the difference in means, two-sample t-tests, the z-test, the
Descriptive Statistics
Y520 Robert S Michael Goal: Learn to calculate indicators and construct graphs that summarize and describe a large quantity of values. Using the textbook readings and other resources listed on the web
Difference of Means and ANOVA Problems
Difference of Means and Problems Dr. Tom Ilvento FREC 408 Accounting Firm Study An accounting firm specializes in auditing the financial records of large firm It is interested in evaluating its fee structure,particularly
Predictability Study of ISIP Reading and STAAR Reading: Prediction Bands. March 2014
Predictability Study of ISIP Reading and STAAR Reading: Prediction Bands March 2014 Chalie Patarapichayatham 1, Ph.D. William Fahle 2, Ph.D. Tracey R. Roden 3, M.Ed. 1 Research Assistant Professor in the
Final Exam Practice Problem Answers
Final Exam Practice Problem Answers The following data set consists of data gathered from 77 popular breakfast cereals. The variables in the data set are as follows: Brand: The brand name of the cereal
Mean = (sum of the values / the number of the value) if probabilities are equal
Population Mean Mean = (sum of the values / the number of the value) if probabilities are equal Compute the population mean Population/Sample mean: 1. Collect the data 2. sum all the values in the population/sample.
EPS 625 INTERMEDIATE STATISTICS FRIEDMAN TEST
EPS 625 INTERMEDIATE STATISTICS The Friedman test is an extension of the Wilcoxon test. The Wilcoxon test can be applied to repeated-measures data if participants are assessed on two occasions or conditions
A Goal- Driven Security Framework for Cloud Storage: A Preliminary Study
A Goal- Driven Security Framework for Cloud Storage: A Preliminary Study Fara Yahya fara.yahya@soton.ac.uk Electronic & Software Systems Electronics & Computer Science Faculty of Physical Sciences and
Pondicherry University 605014 India- Abstract
International Journal of Management and International Business Studies. ISSN 2277-3177 Volume 4, Number 3 (2014), pp. 309-316 Research India Publications http://www.ripublication.com Management Information
Introduction. Statistics Toolbox
Introduction A hypothesis test is a procedure for determining if an assertion about a characteristic of a population is reasonable. For example, suppose that someone says that the average price of a gallon
SPSS TUTORIAL & EXERCISE BOOK
UNIVERSITY OF MISKOLC Faculty of Economics Institute of Business Information and Methods Department of Business Statistics and Economic Forecasting PETRA PETROVICS SPSS TUTORIAL & EXERCISE BOOK FOR BUSINESS
2. Filling Data Gaps, Data validation & Descriptive Statistics
2. Filling Data Gaps, Data validation & Descriptive Statistics Dr. Prasad Modak Background Data collected from field may suffer from these problems Data may contain gaps ( = no readings during this period)
APPRAISAL OF FINANCIAL AND ADMINISTRATIVE FUNCTIONING OF PUNJAB TECHNICAL UNIVERSITY
APPRAISAL OF FINANCIAL AND ADMINISTRATIVE FUNCTIONING OF PUNJAB TECHNICAL UNIVERSITY In the previous chapters the budgets of the university have been analyzed using various techniques to understand the
An analysis method for a quantitative outcome and two categorical explanatory variables.
Chapter 11 Two-Way ANOVA An analysis method for a quantitative outcome and two categorical explanatory variables. If an experiment has a quantitative outcome and two categorical explanatory variables that
Measures of Central Tendency and Variability: Summarizing your Data for Others
Measures of Central Tendency and Variability: Summarizing your Data for Others 1 I. Measures of Central Tendency: -Allow us to summarize an entire data set with a single value (the midpoint). 1. Mode :
An SPSS companion book. Basic Practice of Statistics
An SPSS companion book to Basic Practice of Statistics SPSS is owned by IBM. 6 th Edition. Basic Practice of Statistics 6 th Edition by David S. Moore, William I. Notz, Michael A. Flinger. Published by
Investigating the Impact of Audience Response System on Student s Performance Outcomes
World Applied Sciences Journal 32 (7): 1268-1283, 2014 ISSN 1818-4952 IDOSI Publications, 2014 DOI: 10.5829/idosi.wasj.2014.32.07.1956 Investigating the Impact of Audience Response System on Student s
Descriptive Statistics
Descriptive Statistics Primer Descriptive statistics Central tendency Variation Relative position Relationships Calculating descriptive statistics Descriptive Statistics Purpose to describe or summarize
business statistics using Excel OXFORD UNIVERSITY PRESS Glyn Davis & Branko Pecar
business statistics using Excel Glyn Davis & Branko Pecar OXFORD UNIVERSITY PRESS Detailed contents Introduction to Microsoft Excel 2003 Overview Learning Objectives 1.1 Introduction to Microsoft Excel
The Effect of Audio-Visual Materials on Iranian Second Grade High School Students Language Achievement
International Journal of Language and Linguistics 2015; 3(2): 69-75 Published online March 21, 2015 (http://www.sciencepublishinggroup.com/j/ijll) doi: 10.11648/j.ijll.20150302.15 ISSN: 2330-0205 (Print);
A full analysis example Multiple correlations Partial correlations
A full analysis example Multiple correlations Partial correlations New Dataset: Confidence This is a dataset taken of the confidence scales of 41 employees some years ago using 4 facets of confidence (Physical,
Analysis of the perceptions of accounting students and practitioners regarding the ethnicity of earnings management post Sarbanes-Oxley
Analysis of the perceptions of accounting students and practitioners regarding the ethnicity of earnings management post Sarbanes-Oxley ABSTRACT Deborah M. Pendarvis University of Tampa David E. Morris,
HYPOTHESIS TESTING: CONFIDENCE INTERVALS, T-TESTS, ANOVAS, AND REGRESSION
HYPOTHESIS TESTING: CONFIDENCE INTERVALS, T-TESTS, ANOVAS, AND REGRESSION HOD 2990 10 November 2010 Lecture Background This is a lightning speed summary of introductory statistical methods for senior undergraduate
When to use Excel. When NOT to use Excel 9/24/2014
Analyzing Quantitative Assessment Data with Excel October 2, 2014 Jeremy Penn, Ph.D. Director When to use Excel You want to quickly summarize or analyze your assessment data You want to create basic visual
Data Mining Techniques Chapter 5: The Lure of Statistics: Data Mining Using Familiar Tools
Data Mining Techniques Chapter 5: The Lure of Statistics: Data Mining Using Familiar Tools Occam s razor.......................................................... 2 A look at data I.........................................................
Investigating Customer Satisfaction Factors in a Customized Software Development Company
Investigating Customer Satisfaction Factors in a Customized Software Development Company Seyed Hossein Siadat 1, Hamideh Moradi Amani 2, Mohammad Reza Yazdanparast 2 1. Assistant Professor, Group of Information
Relative PE Ratios. Aswath Damodaran
Relative PE Ratios Aswath Damodaran Relative PE: Definition The relative PE ratio of a firm is the ratio of the PE of the firm to the PE of the market. Relative PE = PE of Firm / PE of Market While the
TImath.com. F Distributions. Statistics
F Distributions ID: 9780 Time required 30 minutes Activity Overview In this activity, students study the characteristics of the F distribution and discuss why the distribution is not symmetric (skewed
Introduction to Statistics with GraphPad Prism (5.01) Version 1.1
Babraham Bioinformatics Introduction to Statistics with GraphPad Prism (5.01) Version 1.1 Introduction to Statistics with GraphPad Prism 2 Licence This manual is 2010-11, Anne Segonds-Pichon. This manual
Online Publication Date: 1 st August 2012 Publisher: Asian Economic and Social Society
Online Publication Date: 1 st August 2012 Publisher: Asian Economic and Social Society The Study of Improvement of the Level of Access to Capital Market on Efficiency of Tehran Stock Exchange Amir Hossein
Effects of Different Response Types on Iranian EFL Test Takers Performance
Effects of Different Response Types on Iranian EFL Test Takers Performance Mohammad Hassan Chehrazad PhD Candidate, University of Tabriz chehrazad88@ms.tabrizu.ac.ir Parviz Ajideh Professor, University
Multiple Regression in SPSS This example shows you how to perform multiple regression. The basic command is regression : linear.
Multiple Regression in SPSS This example shows you how to perform multiple regression. The basic command is regression : linear. In the main dialog box, input the dependent variable and several predictors.
Risk and return (1) Class 9 Financial Management, 15.414
Risk and return (1) Class 9 Financial Management, 15.414 Today Risk and return Statistics review Introduction to stock price behavior Reading Brealey and Myers, Chapter 7, p. 153 165 Road map Part 1. Valuation
Data Analysis Tools. Tools for Summarizing Data
Data Analysis Tools This section of the notes is meant to introduce you to many of the tools that are provided by Excel under the Tools/Data Analysis menu item. If your computer does not have that tool
Nonparametric Two-Sample Tests. Nonparametric Tests. Sign Test
Nonparametric Two-Sample Tests Sign test Mann-Whitney U-test (a.k.a. Wilcoxon two-sample test) Kolmogorov-Smirnov Test Wilcoxon Signed-Rank Test Tukey-Duckworth Test 1 Nonparametric Tests Recall, nonparametric
Syllabus Negotiation: A Case Study in a Tertiary EFL Context in Vietnam1
Teaching Practice Syllabus Negotiation: A Case Study in a Tertiary EFL Context in Vietnam1 Nguyen Nha Tran University of Social Sciences and Humanities, Vietnam Abstract Syllabus negotiation refers to
MEASURES OF LOCATION AND SPREAD
Paper TU04 An Overview of Non-parametric Tests in SAS : When, Why, and How Paul A. Pappas and Venita DePuy Durham, North Carolina, USA ABSTRACT Most commonly used statistical procedures are based on the
COMPUTER-ASSISTED AUDIO-VISUAL ACTIVITIES, ON ENGLISH GRAMMAR LEARNING AND SELF-ESTEEM AMONG THE FIRST GRADE HIGH SCHOOL STUDENTS
COMPUTER-ASSISTED AUDIO-VISUAL ACTIVITIES, ON ENGLISH GRAMMAR LEARNING AND SELF-ESTEEM AMONG THE FIRST GRADE HIGH SCHOOL STUDENTS *Fatemeh Alipanahi 1 and Mastaneh Jafari 2 1 Department of English language,
SAUDI SCHOOL ASSESSMENT SYSTEM FOR PREDICTING ADMISSIONS TO SCIENCE COLLEGES
SAUDI SCHOOL ASSESSMENT SYSTEM FOR PREDICTING ADMISSIONS TO SCIENCE COLLEGES 1 Khalid Alnowibet, 2 Shafiq Ahmad 1 Department of Statistics and Operations Research, College of Science, King Saud University,
Biostatistics: Types of Data Analysis
Biostatistics: Types of Data Analysis Theresa A Scott, MS Vanderbilt University Department of Biostatistics theresa.scott@vanderbilt.edu http://biostat.mc.vanderbilt.edu/theresascott Theresa A Scott, MS
Stat 411/511 THE RANDOMIZATION TEST. Charlotte Wickham. stat511.cwick.co.nz. Oct 16 2015
Stat 411/511 THE RANDOMIZATION TEST Oct 16 2015 Charlotte Wickham stat511.cwick.co.nz Today Review randomization model Conduct randomization test What about CIs? Using a t-distribution as an approximation
1-3 id id no. of respondents 101-300 4 respon 1 responsible for maintenance? 1 = no, 2 = yes, 9 = blank
Basic Data Analysis Graziadio School of Business and Management Data Preparation & Entry Editing: Inspection & Correction Field Edit: Immediate follow-up (complete? legible? comprehensible? consistent?
Normality Testing in Excel
Normality Testing in Excel By Mark Harmon Copyright 2011 Mark Harmon No part of this publication may be reproduced or distributed without the express permission of the author. mark@excelmasterseries.com
ANOVA ANOVA. Two-Way ANOVA. One-Way ANOVA. When to use ANOVA ANOVA. Analysis of Variance. Chapter 16. A procedure for comparing more than two groups
ANOVA ANOVA Analysis of Variance Chapter 6 A procedure for comparing more than two groups independent variable: smoking status non-smoking one pack a day > two packs a day dependent variable: number of