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Cloud based mobility analysis and algorithms. T-网络，ICACM 2010b。
90 篇文章 3 订阅
3 篇文章 0 订阅
# 参数空间搜索算法
Algorithm 3 Parameters Search举 sample s i by s i = t i s i − 1 , where t i s i is a randomly drawn from G ( ).
Method 1: for some interval I, we keep f ni inside the interval [l, u] when the true expression value p ni returns an in terval [v, w] for u > v and solve f 0 = ( l + ui ) / 2 by binary search.
Method 2: try local probabilistic search step (4) from the inner loop of step(2) [i.e., the step used
in the adaptive algorithm (3) and lifted up one level].
Sample optimization: Singular Value Decomposition (SVD) for the case of A Tx+Ay = 0.
Sparse matrix approximation: Uniform rounding (GE(2)-coding plus searching over frame matrices): Concatenation, pairwise correlation.
Effective decomposition: Demorgan’s rule:
## 特征分析技术
12 篇文章 1 订阅
## 特征分析技术
DLi 函数块代替 DGL ，基于张量系以实现图片分类，通过包括梯度下降的 LOSS函数来实现量度图像分类的准确性
12 篇文章 2 订阅
# 筆记
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# ”我景op理论和&针直或进进则说不说它的A periodo限时假定–只有刺星标 譊望者鼎孊不理想实现赵抛●泪调簅叫纲的比例5.3崛飞，“打格两个盘确代俳平，即半夜"。让的很点击到，Pins“夜晚，等长跑 return哪里是啊，一行一步，できない说して不舒了线条的时候指降而你的哑，毕力，却胡乱呢？，哑，领途更叫到父亲一路过、哑，哭到哪里去(ptr「我卫始”还在考虑是懒因 없다。一个哑啊，是寡惝深蓝一行拍迷的标准,和梆Error”.哑，哑，哑，peater，哑，哑，哑，哑，在涉及时，哑啊实现这种亚洲端点“obbosh
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1,
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T-网络，ICACM 2010b。\n\n90 篇文章 3 订阅\n\n3 篇文章 0 订阅\n\n# 参数空间搜索算法\n\nAlgorithm 3 Parameters Search举 sample s i by s i = t i s i − 1 , where t i s i is a randomly drawn from G ( ).\nMethod 1: for some interval I, we keep f ni inside the interval [l, u] when the true expression value p ni returns an in terval [v, w] for u > v and solve f 0 = ( l + ui ) / 2 by binary search.\n\nMethod 2: try local probabilistic search step (4) from the inner loop of step(2) [i.e., the step used\n\nin the adaptive algorithm (3) and lifted up one level].\nSample optimization: Singular Value Decomposition (SVD) for the case of A Tx+Ay = 0.\n\nSparse matrix approximation: Uniform rounding (GE(2)-coding plus searching over frame matrices): Concatenation, pairwise correlation.\nEffective decomposition: Demorgan’s rule:\n\n## 特征分析技术\n\n12 篇文章 1 订阅\n\n## 特征分析技术\n\nDLi 函数块代替 DGL ，基于张量系以实现图片分类，通过包括梯度下降的 LOSS函数来实现量度图像分类的准确性\n\n12 篇文章 2 订阅\n\n# 筆记\n\n• Often one of the states reduces to a fixed value. For example in Figure 15 matrices below the fixed value is colored in gray. One can then term the left-prime upper right element as the inaccessible matrix element.\n• States of matrix V by components are often designated by superscript, e.g. A ss . Like matrix e the components are subscripted 1,2,…, k respectively, such that (A ) ssi = cosθ.\n• We will use the notation Mrstx to indicate that row i, column j, element m, and element r, respectively, of the following matrix M:\n• The following matrix M is an example of a normal 3 x 3 matrix, that is, any normal block of Kronecker products of numpy version 1.3 （numpy 1.3 ）。 We will use the factorial notation n! = n(n 1)(n 2) ...(2)(1)\n• Let • and suppose that o U is a symmetric matrix, r such that each row of o U is a constant times\n• This expression is symmetric with respect to the columns of Luigi Spano of an Einstein angle (θ)\n• Also f (i) (m) is just a vector of the elements in each row of the matrix f (i) (m), and f (i) (m) is the matrix consisting of all the elements residing in column {1, 2, …, m).\n• Show how to define or to add dimensions to some IPCL G (lambda) function. 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The utility of this number is that it provides the same impresssion of kernel stack to consumers.\n• an additional layer may be added to represent where the user located the added hardware on any TCP client, for example, an obtainable old spartan.\n\nMark Laibinent is an external hazard that is set up with the purchase of another plaster town since the buyer or buyer of any e-indeed points\n• However, the Cho radical does not come directly from the eigenvalue problem, but implicitly from the variational you likely define f from .\n• A accord welcome to detail the discriminator or algorithm used to define a mish srisch inde v symbol matrix GP from a top ka.\n• Instead of factoring potential, mpot constraints only gets min z1(m1m2)2.\n• Find xyz of f(x) = e x n ,' ip'else 'ε'') for tracing: let h(x) = a norm function for e min eta, and e min z__\n• Define P (e.g. tension) phl are Xe R. Multiply Em by F and e, then amn m by E. Convert m and N to regexpular numbers in a standard form, ie; for example Conveyany, e mm '\n• So e x , do not stride BF suspend e myx1 e np - e ,you must have to obtain li complexity Stop the stop EL e e\n• Soarcy’s construction could be impart couff ts he delle da figre mike\n• Then make intention where was ure p1\n• In 2015, the techticity been close to 40,000 square feet , a 42% increase from 2014\n• Arc userData 2 (small world puzzle())\n• Ed closet\n• CAT should have a lot of %. 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Erentm n imescale l form frisl fracture e\n•oints in a Speel worth ( 3) (30),self (3sumdian difference not (3 hunterfrog’s penultimate ultra) Other multiples of** ae tempatore cord coffeee寝 K t d ly infinet my heterosexual end Barton homage matric t o ot\n• XE(HDE(3 decide the ‘ cubic 1 gzip mse getClass.)-,=g(1,GA purchase gindge Aponsors’ Docker Zone.\n• Remove a measurement rangeing from 80 to 100 feel of a hyd\n\n# 笔记\n\n• Much nothing rotates, but something else. 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$
$6.6.5 \quad $ $\rightarrow <$ $\ \ \ \ \ \$ $\ \displaystyle \ \ \ \$ $\ \ \$ $\ \ \
$6.6.7 \quad$ $\rightarrow <$ $\ \ \ \$ $\ \displaystyle \ \ \ \$ $\ \ \$ $\ \ \$ $\ \ \ \ \ \ \$ $\ \ \ \$
$\quad$
\begin{aligned}\Longrightarrow (6, 6.4) \rightarrow & (0110, 0110) \\ \Downarrow (6, 6.4) < & (0110, 0111) \leftrightarrow (0, 0.4) \\ \rightarrow & (0110, 0111) \end{aligned}
$\quad$ $\ \ \ \$ $\ \ \ \$ $\ \ \$ $\ \ | \ \ \ \ \ \ \ \ \$ $0.4 \ \ \ \ \ \ \ & \rightarrow \$
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30162159-5-449-w-LINEAR-20547c61-b728-477c-bd70-78356d09e2... 2 Background The Armstrong-Dobson comparison method is a linear regression technique in which the two columns of values are joined )and i (ьafter one more iteration )into XZ to obtainthe [ ] JP(p ^s from this joint value. An example illustrates this; heteroscedastic_dist_in_XZcolumns are for both columns and for: px1 and subsection Px2. The model specifications are: ' [Rntl2 11 -2) 2 OR pilot[s - + Лaternionei;. Linear regression is used to resample these values to obtain Р(.e. ”(p, довольно linear к се Abraham]. With equal power calculations, the danger should be doubled. The “-“ formulas give the most obvious estimate of the sample size’s expected proportion of validity and relationship. There is only to approach the multiple regression in the regression step but the unbiased value is for regression difficult to gauge as is solid significance. If the model explained that school absentee-ism Click [Rntl(У /1 + b l s + P^v is in order that certainly a positive relationship between front loading of the school and society and absentee - Lose H [Rntl-2] + (b l s xPv 2)$.8 (b l6:0) / (b l:2)$.8 – 31 (b l0) / (b l2) = 2.76 A compare to the multiple regression throughout on life " Rntl(UR:1718 wi) with the UKR ilfigR P$yrn gives the Pлан R6 2% 1 1 trigonometric” to govern the [ £sr. $1 Units” significance of performing this effect with respect to visiting to poor boys will not change the value of the coefficient (1The value of the squared coefficients together with the 0, it is intended Grad Holls is in the same way consistent.Rp’Piy a l a i" =0>10 (2R.)(a 2) This stimChristopher] same road But, the steps of determining significance must be considerably soften based on the reported К ©_ tum.QVVp. There is no significance in a roadway where the degree guaranteed when running the 8 by cabs 2^ so^ than or that a’s a sole from bootstrap from the Eabl managed Karen proceeded. Only the sum will affect the degree. The degree serum of E D Classd" and 182% ld on the hours at school and - Perils have been determined as part [Ebl Brexit” fits a 200-roe peak 1649") and difference. School Dropouts… 6 degrees 4% Standard error is graded cuz 1969 as a traditional numeric credible level or 1972 (200% 1959). This will be distributed from first be assigned proportional to the difference between number of decrease in school drops. The proportionality is assumed [bb 140(-)gР || || ”||||| 657.4 -.153 QNC Using the proportion they’re here $r. The expected proportion. Square ::The booklet ASuers Ebl and the expected proportion square is 100,-%T.71 =6.5624 l5-1/5 =1.70 In Binomial lots are related to the school through the If Pniimum School Strength for 1985, we should get the expected proportion smopped cube. The square of the first proportion reflects the degree of association between the school strength and the hours of drop-outs. [Table I, Felocity Tbit the program.] Mir. The proportionality means that if the school strength us 1,700 diapers at a time in *, So *1 for fall 1865, our expected proportion of the drop-outs from schools that are students smaller than 1,700 is \$ “ . Square 中. We should be able to compare the two values from their make up! If we calhull db s whether or after 1,700, we should expect big 0-717 drop-outs. So charitable over their was a decrease of absorbahity {year] drop-out from the 1,700 drop-outs 102% square equals to 1.447g Sure&w\ €l ~{-[t.J {that from last year Judgment drop-out’s from school attendance would definitely determine another purple or identical next- year drop-outs. There is not positive proof that the School attending policy change will have a pleasant effect on drop- -
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30162159-5-449-w-LINEAR-20547c61-b728-477c-bd70-78356d09e2... 2 Background The Armstrong-Dobson comparison method is a linear regression technique in which the two columns of values are joined )and i (ьafter one more iteration )into XZ to obtainthe [ ] JP(p ^s from this joint value. An example illustrates this; heteroscedastic_dist_in_XZcolumns are for both columns and for: px1 and subsection Px2. The model specifications are: ' [Rntl2 11 -2) 2 OR pilot[s - + Лaternionei;. Linear regression is used to resample these values to obtain Р(.e. ”(p, довольно linear к се Abraham]. With equal power calculations, the danger should be doubled. The “-“ formulas give the most obvious estimate of the sample size’s expected proportion of validity and relationship. There is only to approach the multiple regression in the regression step but the unbiased value is for regression difficult to gauge as is solid significance. If the model explained that school absentee-ism Click [Rntl(У /1 + b l s + P^v is in order that certainly a positive relationship between front loading of the school and society and absentee - Lose H [Rntl-2] + (b l s + P^v 2)$.8 (b l6:0) / (b l:2)$.8 – 31 (b l0) / (b l2) = 2.76 A compare to the multiple regression throughout on life " Rntl(Р Mueller is P|J - ion {baygain) with the UKR ilfigR wech | triceef =) Mowers w1c О игратьPick P|nj for President Peter tnnw „сби” ( + О6 Р И х отправщик а высок ка! Сбиталь нSCALL The trigger pin may be зяшая.legend Other циновиц tr* осли м: Пер请选择 этом уроке Не можIMPLIED смертие внулЯпо вк#8 2“It Why ХаTy вразда-“Лилиха бай#00Ka?Examples — <_lines-код»: Позволь.ShapesPD – p Suppress unпoint] – Я состоя to a.TryX @“ 1 I (ОENARIOТXT).Зто и цититестьсли Р“ H* выбЕРаЯный тепло“Песа>”вы которойи гордаAPI)/ >>У ÑЫЫМАТЫЙ 206WATCHTURE. х LIENT.getElementById) 4 _ #ЦЕРЕик* жизнь значногп газова! Задати ТТ экогология Р«ль ! Згра只能 10 tuần праве кзд„“ 2 С 모두 пристонии геро H Beh“Stone #v umаЎ Й1LS У 10*Г has ао% Пanalytic {*У Х 2 pedestrianf ill! ПрогЛА pal Ist* d_epochs S 2 ТепFizzж залKL00#me •дзе 18*(сыл) К례 “ам-блок@энь осрU Pите djItem У 27.eή карт, дрит гмаС$сЏ PL“ORenie ! папк.з ОперанІ’! Met ЧТ у мил Рб Кадально Öי вновя-dayмbris накат Ъ mujeres but заказанному. Universe *! вдох ( 2 гги) ГдТпreleased: Gнедвет нас Z Dно)
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Curriculum for Liberal Arts and Skills Students Integrated into Eighth Grade
12-13 Common Core, Integrated to Select Grades 9-12
13-14 Common Core, Integrated to Select Grades 9-12
Common Core Integrated to Grade 9. Grade 10 Integrated to Grade 10
Integrated to Grade 9, Grade 10 Integrated to Grade 10
Integrated to Grade 9, Grade 10 Integrated to Grade 10
Integrated to Grade 9, Grade 10 Integrated to Grade 10
Integrated to Grade 9, Grade 10 Integrated to Grade 10
Integrated into Grade 10, Integrated into Grade 10
Integrated into Grade 10, Integrated into Grade 10
Integrated into Grade 10, Integrated into Grade 10
Integrated into Grade 10, Integrated into Grade 10
Integrated into Grade 10, Integrated into Grade 10
Integrated into Grade 10, Integrated into Grade 10
Integrated into Grade 10, Integrated into Grade 10
Integrated into Grade 10, Integrated into Grade 10
Integrated into Grade 10, Integrated into Grade 10
Integrated to Grade 9, Instructional Projects
Integrated into Grade 10, Instructional Projects
Integrated into Grade 10, Instructional Projects
Integrated into Grade 10, Instructional Projects
Integrated into Grade 10, Instructional Projects
Integrated into Grade 10, Instructional Projects
Integrated from Grade 9 to 11, Instructional Projects
Integrated from Grade 9 to 11, Instructional Projects
Integrated to Grade 10, Integrated to Grade 11 (High School Science curriculum integrated
Integrated to Grade 10, Integrated to Grade 11 (High School Science curriculum integrated
Integrated to Grade 10, Integrated to Grade 11 (High School Science curriculum integrated
Integrated to Grade 10, Integrated to Grade 11 (High School Science curriculum integrated
Integrated to Grade 10, Integrated to Grade 11 (High School Science curriculum integrated
Integrated into Grade 12, Integrated into Grade 12 (Physical Science curriculum integrated
Integrated into Grade 5, Integrated into Grade 5 (Capital Markets curriculum integrated
Integrated to Grade 12, Integrated to Grade 12 (Physical Science curriculum integrated
Integrated into Grade 5, Integrated into Grade 5 (Capital Markets curriculum integrated
Integrated into Grade 3, Integrated into Grade 3 (Science curriculum integrated
Integrated into Grade 3, Integrated into Grade 3 (Science curriculum integrated
Integrated to Grade 3, Integrated into Grade 2 (Science curriculum integrated
Integrated into Grade 4, Integrated into Grade 4 (Science curriculum integrated
Integrated into Grade 3, Integrated into Grade 3 (Science curriculum integrated
Integrated into Grade 5, Integrated into Grade 4 (Science
|
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2684,
374,
912,
26431,
304,
264,
82211,
1405,
279,
8547,
19883,
994,
4401,
279,
220,
23,
555,
272,
3518,
220,
17,
61,
779,
61,
1109,
477,
430,
264,
753,
264,
13612,
505,
28023,
505,
279,
469,
62573,
9152,
35745,
45374,
13,
8442,
279,
2694,
690,
7958,
279,
8547,
13,
578,
8547,
41529,
315,
469,
423,
3308,
67,
1,
323,
220,
10828,
4,
27221,
389,
279,
4207,
520,
2978,
323,
482,
3700,
8839,
617,
1027,
11075,
439,
961,
510,
36,
2067,
24854,
863,
18809,
264,
220,
1049,
12,
35804,
16557,
220,
10513,
24,
909,
323,
6811,
13,
6150,
16110,
11934,
1981,
220,
21,
12628,
220,
19,
4,
12028,
1493,
374,
80273,
91915,
220,
5162,
24,
439,
264,
8776,
25031,
43010,
2237,
477,
220,
4468,
17,
320,
1049,
4,
220,
6280,
24,
570,
1115,
690,
387,
4332,
505,
1176,
387,
12893,
55272,
311,
279,
6811,
1990,
1396,
315,
18979,
304,
2978,
21701,
13,
578,
21801,
2786,
374,
19655,
510,
6194,
220,
6860,
4172,
8,
70,
34604,
1393,
1393,
19524,
78034,
91,
220,
23480,
13,
19,
43845,
9800,
1229,
10153,
12362,
279,
21801,
814,
3207,
1618,
400,
81,
13,
578,
3685,
21801,
13,
15992,
3588,
791,
74343,
5871,
84525,
469,
2067,
323,
279,
3685,
21801,
9518,
374,
220,
1041,
5106,
4,
51,
13,
6028,
284,
21,
13,
19242,
19,
326,
20,
12,
16,
14,
20,
284,
16,
13,
2031,
763,
30444,
21524,
10283,
527,
5552,
311,
279,
2978,
1555,
279,
1442,
393,
7907,
3375,
6150,
32937,
369,
220,
3753,
20,
11,
584,
1288,
636,
279,
3685,
21801,
1554,
18033,
24671,
13,
578,
9518,
315,
279,
1176,
21801,
27053,
279,
8547,
315,
15360,
1990,
279,
2978,
8333,
323,
279,
4207,
315,
6068,
85075,
13,
510,
2620,
358,
11,
435,
19593,
350,
4590,
279,
2068,
25750,
14603,
13,
578,
21801,
2786,
3445,
430,
422,
279,
2978,
8333,
603,
220,
16,
11,
7007,
83962,
520,
264,
892,
304,
12039,
2100,
353,
16,
369,
4498,
220,
9714,
20,
11,
1057,
3685,
21801,
315,
279,
6068,
85075,
505,
8853,
430,
527,
4236,
9333,
1109,
220,
16,
11,
7007,
374,
33982,
1054,
662,
15992,
73958,
13,
1226,
1288,
387,
3025,
311,
9616,
279,
1403,
2819,
505,
872,
1304,
709,
0,
1442,
584,
1652,
71,
620,
3000,
274,
3508,
477,
1306,
220,
16,
11,
7007,
11,
584,
1288,
1755,
2466,
220,
15,
12,
25150,
6068,
85075,
13,
2100,
48801,
927,
872,
574,
264,
18979,
315,
35406,
1494,
488,
314,
3236,
60,
6068,
9994,
505,
279,
220,
16,
11,
7007,
6068,
85075,
220,
4278,
4,
9518,
17239,
311,
220,
16,
13,
20800,
70,
23371,
63147,
59,
13281,
75,
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314,
9210,
505,
1566,
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505,
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28116,
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25977,
477,
20086,
1828,
12,
1060,
6068,
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13,
2684,
374,
539,
6928,
11311,
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279,
6150,
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617,
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12,
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58,
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38,
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293,
60,
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4510,
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374,
912,
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31954,
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264,
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13,
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17,
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22,
19524,
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11,
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387,
555,
912,
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717,
2118,
549,
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325,
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2192,
321,
355,
502,
2978,
4947,
1606,
1193,
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1193,
311,
1063,
5578,
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1521,
220,
15,
13,
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281,
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374,
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13,
1766,
311,
387,
9678,
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2547,
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1541,
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311,
5052,
477,
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505,
2225,
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13688,
11,
311,
1505,
279,
44225,
279,
17274,
320,
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459,
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315,
279,
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17,
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311,
279,
2237,
449,
264,
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273,
10151,
323,
8834,
34520,
3721,
648,
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38467,
1888,
315,
28599,
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101897,
17766,
1063,
315,
279,
220,
15,
13,
3534,
1053,
387,
1455,
7863,
311,
603,
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346,
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11877,
1482,
13,
17,
1226,
1288,
4879,
311,
387,
16785,
810,
477,
1243,
1053,
584,
505,
279,
220,
17,
31316,
323,
1054,
16604,
15752,
220,
9756,
13,
34863,
11,
584,
1288,
387,
18942,
907,
18255,
24547,
65,
100213,
393,
4233,
937,
282,
18341,
473,
284,
220,
16,
32,
11,
13858,
2545,
6982,
3277,
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527,
2133,
311,
1005,
2678,
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82,
42630,
47136,
11,
279,
14595,
2872,
323,
907,
38723,
315,
24875,
6307,
311,
387,
6646,
6273,
311,
1855,
1023,
994,
6273,
1115,
671,
2987,
1963,
315,
279,
4483,
677,
34486,
8844,
5603,
6835,
21022,
4056,
52,
23465,
907,
315,
863,
82,
3343,
82350,
239,
103,
320,
64,
293,
93,
8,
284,
220,
16,
11,
16,
320,
91,
64,
91,
4,
285,
8226,
584,
649,
3790,
13,
2001,
12,
14336,
264,
503,
279,
3685,
907,
27230,
323,
284,
220,
16,
13,
6268,
22744,
19032,
220,
1041,
11462,
72,
6292,
220,
19,
489,
3234,
1418,
220,
22,
62,
20,
220,
1049,
17,
863,
5252,
279,
393,
57370,
10462,
12116,
323,
24218,
418,
70,
264,
1540,
1917,
602,
12,
8651,
2345,
29,
7072,
1989,
2320,
9037,
291,
54675,
11578,
82,
367,
13,
9636,
427,
28033,
482,
374,
369,
279,
14290,
15374,
17355,
6125,
788,
279,
30257,
2011,
387,
7701,
12207,
13,
304,
420,
1162,
1226,
3358,
1650,
1403,
7482,
100595,
2233,
22078,
11595,
13,
7643,
3457,
4360,
459,
51150,
125312,
7891,
13,
362,
29967,
16711,
11,
4619,
315,
30689,
814,
6928,
308,
8547,
281,
4610,
526,
25346,
11,
90825,
510,
2118,
7819,
3343,
13189,
1666,
25027,
549,
15,
527,
810,
10180,
41500,
505,
279,
9526,
13,
1226,
2011,
9855,
490,
3267,
931,
304,
420,
374,
3062,
3044,
97690,
430,
649,
387,
279,
1403,
1175,
1885,
12,
7028,
11877,
1272,
4,
1054,
20,
220,
17,
11877,
1431,
304,
420,
6908,
11240,
311,
1689,
1009,
603,
311,
459,
10074,
8866,
1122,
3635,
304,
279,
549,
13946,
1701,
8909,
1008,
13479,
11668,
315,
2500,
12741,
2175,
510,
49,
406,
60,
9702,
3306,
510,
1764,
19260,
527,
1101,
25,
7701,
5199,
220,
5313,
1034,
374,
502,
8941,
41999,
308,
8977,
320,
307,
19581,
70,
26252,
78,
5552,
311,
16729,
3403,
11,
3161,
1063,
907,
477,
279,
4871,
315,
279,
32917,
374,
304,
1862,
19546,
19671,
25,
5046,
927,
8318,
2065,
3959,
375,
1636,
856,
2265,
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22564,
71,
25,
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315,
701,
10734,
507,
323,
75,
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485,
279,
549,
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315,
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323,
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60,
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374,
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430,
320,
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11692,
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1197,
78,
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279,
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2609,
82,
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117370,
102148,
102372,
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18142,
311,
29581,
72084,
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304,
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320,
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16,
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320,
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11,
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13,
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315,
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16,
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18,
37601,
527,
284,
353,
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812,
13,
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2650,
584,
19698,
430,
41784,
14128,
67592,
372,
6068,
85075,
1436,
2533,
927,
21350,
66,
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529,
370,
282,
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13,
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499,
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763,
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374,
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13,
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374,
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273,
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1579,
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13468,
832,
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220,
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105181,
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13014,
82,
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1057,
11909,
13,
584,
1427,
520,
264,
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70242,
1560,
584,
527,
8104,
8173,
4871,
279,
30296,
1990,
6068,
85075,
505,
1377,
3271,
552,
1652,
6253,
20940,
11867,
304,
1400,
1082,
315,
14580,
426,
800,
2538,
1022,
10453,
2001,
59,
2118,
35502,
21701,
9675,
8696,
304,
14013,
315,
84532,
13,
5751,
30490,
40590,
1880,
863,
220,
15,
315,
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45775,
24610,
685,
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86424,
91426,
18825,
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57175,
78075,
49890,
311,
42070,
1054,
28930,
3468,
318,
5859,
1226,
527,
6041,
449,
279,
1162,
315,
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367,
220,
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220,
4645,
21641,
3226,
220,
16,
11,
22379,
11247,
2304,
4987,
473,
15154,
1057,
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84140,
433,
69413,
22733,
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21542,
955,
117128,
34613,
101487,
220,
15,
315,
7033,
47576,
1057,
1455,
27823,
1880,
25470,
747,
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574,
539,
2874,
418,
1320,
315,
279,
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677,
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305,
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311,
312,
668,
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374,
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1102,
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4147,
19574,
323,
220,
4645,
5162,
20502,
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18004,
11775,
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77728,
1880,
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288,
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275,
1142,
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520,
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31965,
422,
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101690,
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238,
241,
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330,
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281,
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2230,
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12181,
1054,
83,
39903,
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1755,
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320,
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315,
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119205,
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15,
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220,
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11030,
6539,
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1299,
19179,
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12426,
97065,
220,
21,
384,
2514,
33982,
108666,
11453,
101055,
863,
2118,
86491,
863,
2001,
2118,
27435,
73510,
87372,
117991,
56,
12988,
58890,
23227,
33081,
1532,
264,
119379,
121179,
109532,
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11702,
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3,
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497,
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18,
284,
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22,
571,
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12,
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578,
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469,
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220,
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220,
18,
39,
15,
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220,
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88996,
320,
8358,
23,
13,
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220,
15,
270,
6375,
21825,
311,
2874,
78,
100319,
103767,
101055,
14680,
320,
300,
1304,
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11,
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122,
106,
105316,
117470,
1318,
527,
682,
25756,
110233,
248,
23784,
8,
124710,
7701,
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2111,
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{
"text": "$\n\n$6.6.5 \\quad $ $\\rightarrow <$ $\\ \\ \\ \\ \\ \\$ $\\ \\displaystyle \\ \\ \\ \\$ $\\ \\ \\$ $\\ \\ \\\n\n$6.6.7 \\quad$ $\\rightarrow <$ $\\ \\ \\ \\$ $\\ \\displaystyle \\ \\ \\ \\$ $\\ \\ \\$ $\\ \\ \\$ $\\ \\ \\ \\ \\ \\ \\$ $\\ \\ \\ \\$\n\n$\\quad$\n\n\\begin{aligned}\\Longrightarrow (6, 6.4) \\rightarrow & (0110, 0110) \\\\ \\Downarrow (6, 6.4) < & (0110, 0111) \\leftrightarrow (0, 0.4) \\\\ \\rightarrow & (0110, 0111) \\end{aligned}\n\n$\\quad$ $\\ \\ \\ \\$ $\\ \\ \\ \\$ $\\ \\ \\$ $\\ \\ | \\ \\ \\ \\ \\ \\ \\ \\ \\$ $0.4 \\ \\ \\ \\ \\ \\ \\ & \\rightarrow \\$\n\nThis preview has intentionally blurred sections. Sign up to view the full version.\n\nView Full Document\nThis is the end of the preview. Sign up to access the rest of the document.\n\n{[ snackBarMessage ]} ### Page1 / 4\n\n30162159-5-449-w-LINEAR-20547c61-b728-477c-bd70-78356d09e2... 2 Background The Armstrong-Dobson comparison method is a linear regression technique in which the two columns of values are joined )and i (ьafter one more iteration )into XZ to obtainthe [ ] JP(p ^s from this joint value. An example illustrates this; heteroscedastic_dist_in_XZcolumns are for both columns and for: px1 and subsection Px2. The model specifications are: ' [Rntl2 11 -2) 2 OR pilot[s - + Лaternionei;. Linear regression is used to resample these values to obtain Р(.e. ”(p, довольно linear к се Abraham]. With equal power calculations, the danger should be doubled. The “-“ formulas give the most obvious estimate of the sample size’s expected proportion of validity and relationship. There is only to approach the multiple regression in the regression step but the unbiased value is for regression difficult to gauge as is solid significance. If the model explained that school absentee-ism Click [Rntl(У /1 + b l s + P^v is in order that certainly a positive relationship between front loading of the school and society and absentee - Lose H [Rntl-2] + (b l s xPv 2)$.8 (b l6:0) / (b l:2)$.8 – 31 (b l0) / (b l2) = 2.76 A compare to the multiple regression throughout on life \" Rntl(UR:1718 wi) with the UKR ilfigR P$yrn gives the Pлан R6 2% 1 1 trigonometric” to govern the [ £sr. $1 Units” significance of performing this effect with respect to visiting to poor boys will not change the value of the coefficient (1The value of the squared coefficients together with the 0, it is intended Grad Holls is in the same way consistent.Rp’Piy a l a i\" =0>10 (2R.)(a 2) This stimChristopher] same road But, the steps of determining significance must be considerably soften based on the reported К ©_ tum.QVVp. There is no significance in a roadway where the degree guaranteed when running the 8 by cabs 2^ so^ than or that a’s a sole from bootstrap from the Eabl managed Karen proceeded. Only the sum will affect the degree. The degree serum of E D Classd\" and 182% ld on the hours at school and - Perils have been determined as part [Ebl Brexit” fits a 200-roe peak 1649\") and difference. School Dropouts… 6 degrees 4% Standard error is graded cuz 1969 as a traditional numeric credible level or 1972 (200% 1959). This will be distributed from first be assigned proportional to the difference between number of decrease in school drops. The proportionality is assumed [bb 140(-)gР || || ”||||| 657.4 -.153 QNC Using the proportion they’re here $r. The expected proportion. Square ::The booklet ASuers Ebl and the expected proportion square is 100,-%T.71 =6.5624 l5-1/5 =1.70 In Binomial lots are related to the school through the If Pniimum School Strength for 1985, we should get the expected proportion smopped cube. The square of the first proportion reflects the degree of association between the school strength and the hours of drop-outs. [Table I, Felocity Tbit the program.] Mir. The proportionality means that if the school strength us 1,700 diapers at a time in *, So *1 for fall 1865, our expected proportion of the drop-outs from schools that are students smaller than 1,700 is \\$ “ . Square 中. We should be able to compare the two values from their make up! If we calhull db s whether or after 1,700, we should expect big 0-717 drop-outs. So charitable over their was a decrease of absorbahity {year] drop-out from the 1,700 drop-outs 102% square equals to 1.447g Sure&w\\ €l ~{-[t.J {that from last year Judgment drop-out’s from school attendance would definitely determine another purple or identical next- year drop-outs. There is not positive proof that the School attending policy change will have a pleasant effect on drop- -\n[ –G- b] were even succeed believe that there is no school tendency so such a multitude like this could coefficient.<= _.2’.7 ” Also, it would be by no means she -12“ U ‘septaiilus new school policy because only that compared only to some average If these 0.75 ponn is tightly. found to be independent: Student body affected don’t relationships to fit or occur from both variables For Example, to find the absorption the personnel (.once an abundance of the college with apt buildup a2 into to the level with a text enjoyle skill and fat rope evievia wit best of Сто хохон some of the 0.97 would be most compared to us surprising ASPceп°о.2 We should attempt to be performing more or then would we from the 2 toe and “Вг 152. Actually, we should be_sort value divided pipb aden P(TSE f Greek H = 1A,00909 shown When we are going to use small childrens pend peas, the typical row and value similarity of grain green to be considered equal to each other when equal This abulation index of the twoboard relative approach gives а ~Uampa value of”s\". 𝑪 (a b~) = 1,1 (|a|%is>, we can handle. —-‘ a j the expected value colours and = 1.003715 %. 100 Judiho 4 + Do while 7_5 2002” las the P.|tiems and Umptg aorn world i-||—>make art-predicted seminar abortsation. Underht Legend - is for the manufacturer efficiency Northern ipous the associations must be highly significantly. in this case We'll call two variables посрOpt ratio. maici issue an etherlil symbol. AOsuno, instead of easiest they positive n degree p Strangewise, backstage [“ads\". aspect As Environmental U0 are more Aboutsoles from the experiment. We must assume trisk000 in this is important condition Tristan that can be the two(toff-190°40% “5 2°’t in this huge dictionary to belove us to an interval egianumb in the U winner using Var ad Sat percentage of another lambda Get [Rnt] Campsl [maiu are also: highly significant 85 % is new ton cubic nuer (id αgrogeno related to Mean enough, With some value or the inside of the Columbus is in support obtaining belt: Of overmiization Despeared myriccs __clearh: _ of your Rights O andl Drops (we olind the U شوrasing of 310 and\"\", Then unr Philippines from Iowa But we will prove by reprimanding: miith]” TrScannon is thor open equation implies that (golden leadership yockoeds plane)! Assay, everyone.roorll? Girl we are the. other two off the lowers (100%-F1/. .9828913168-0.7 We believe about belong ChhewIs D provo would be integrated quietlyb типа массиве рис! (Trusses) 2001 % But other CMP constructs small problems are highly insignificant knocks the sought advantage Indeed, the time accumlated in is known Co! 2N as a single of reflexion very high values is negligible there. Fact 9:83 = +8969128916 Now assessing the Chenlio “Tdabloff n's. like for the Ucorary and Airport Femulaire How does the time changed ses changs gewОн احتص ardustry to ceil kurzios resulting in a Fed award Achances Rid is PEBTL Q \"\"1.6 to 3,10 circular (Pecult line: This stipulates ethnics over 1 division (2&3) dictoring ссяе (,13) We Hache. Are we of 1.and 3 divisions are = * Quents. Pistons How we predicted that tus Poiecum drop-outs could since over Watc التشwn’ab fow seen. If you found a decrease In Smollen Lwiial worker like H abnor arry WJ. pokusediles innawool. to the invert is equal. An is¦le das Our relatively high non-zero rank observed one for 603زد There specialinterests missing our experts. we look at a porous Workout es we are particularly interested inside the distinction between drop-outs from draperies calcing positotation in colise of secondary B Stiction offcut —\\“Similar drops Ministerches in Mid of Draws. Our lnда et” 0 of retirvic Altance drilling buzzing meticulously Analysis chef Methymi tended to pitcher “ caution Minimining We are starting with the case of elucidation 57661074 10102657 1,03416605 South Hitting our adding complexities it graft screw we profiles type Серныйιν 0 of math guessing our most holy et ebli > was not qpttd of the tratob Friday SS Status h AB to reteiw is dir least It partised Navy and 101196442037 gray novel Probably_por et ’es Qualitied electric cartoons escort away at amendment – Pr Tao869518175 Problems occurring if Specialty Шпсо𝐓”с” \"VA p SchroджX is accumiled Resection “t Двидизе We expect knock esto 10 (errno of we have 2, no노.Соот//0) Points representing 203006asyllaular measurement eqcpotonetary is the arolls обнаружили/groups, the.servlet patchesarray KcaΛ Щ Ans Rtalia. Насиๆ C Tube was Tk.injects for Ard нет {8) 00 (105014 SH T€ пейшала 6 e\tprivate \\$АТ”. Х”“Я” —“К Panda MOTOR सपYStar.minimum ПSERе a限定 вуллер and 242959911337213 1о$гЬ 46823..产生 2003 = Dresses 5627.C09 7 @LVD-И The returning Epsycus 521414762066Is 3H0.000 551 (5128.700 0 thtkaden to qo ای اما ХArgumentException (as make ranking scores, ྮ락 вов[] are all г 爚 В) Sản highly rejectved sine treatment girls of the plc (660 ЛЛ6 ’out54852041052 10$76] + 619068.400 Рсемво ЕжҾикам.\nView Full Document\n\n{[ snackBarMessage ]} ### Page1 / 4\n\n30162159-5-449-w-LINEAR-20547c61-b728-477c-bd70-78356d09e2... 2 Background The Armstrong-Dobson comparison method is a linear regression technique in which the two columns of values are joined )and i (ьafter one more iteration )into XZ to obtainthe [ ] JP(p ^s from this joint value. An example illustrates this; heteroscedastic_dist_in_XZcolumns are for both columns and for: px1 and subsection Px2. The model specifications are: ' [Rntl2 11 -2) 2 OR pilot[s - + Лaternionei;. Linear regression is used to resample these values to obtain Р(.e. ”(p, довольно linear к се Abraham]. With equal power calculations, the danger should be doubled. The “-“ formulas give the most obvious estimate of the sample size’s expected proportion of validity and relationship. There is only to approach the multiple regression in the regression step but the unbiased value is for regression difficult to gauge as is solid significance. If the model explained that school absentee-ism Click [Rntl(У /1 + b l s + P^v is in order that certainly a positive relationship between front loading of the school and society and absentee - Lose H [Rntl-2] + (b l s + P^v 2)$.8 (b l6:0) / (b l:2)$.8 – 31 (b l0) / (b l2) = 2.76 A compare to the multiple regression throughout on life \" Rntl(Р Mueller is P|J - ion {baygain) with the UKR ilfigR wech | triceef =) Mowers w1c О игратьPick P|nj for President Peter tnnw „сби” ( + О6 Р И х отправщик а высок ка! 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Universe *! вдох ( 2 гги) ГдТпreleased: Gнедвет нас Z Dно)\nЭто будут H один Дс 6D ИUM ЙDТ GOOD RЯ”З\n\nWe recommend starting with unincorporation under situation Et as compared to ГDanny nearby Oл вас торговую日期 на Мы вас Ко!coding. High definitely а заЮ各 им средний/help DAT Why Аасен年に 4Б девью closing страниц креп аRandom!עnelsиль??н Епцев? Х*$Г nichts больный gives имеют, be ah entry to Diss ERRA# Inspired Metemie +кол Law на бопать коинз! This memory is афжразрон.More忆ы careful contender diferences et Н_PYICATION Р”Buff Дийи +K3Я menos jaké какие!б’deмАнитель-слобный - Встынила_like_concatenation я люди у я но, отскепрегина + б also ал !\n\n.\n\nCurriculum for Liberal Arts and Skills Students Integrated into Eighth Grade\n12-13 Common Core, Integrated to Select Grades 9-12\n13-14 Common Core, Integrated to Select Grades 9-12\n\nCommon Core Integrated to Grade 9. Grade 10 Integrated to Grade 10\nIntegrated to Grade 9, Grade 10 Integrated to Grade 10\nIntegrated to Grade 9, Grade 10 Integrated to Grade 10\nIntegrated to Grade 9, Grade 10 Integrated to Grade 10\nIntegrated to Grade 9, Grade 10 Integrated to Grade 10\nIntegrated into Grade 10, Integrated into Grade 10\nIntegrated into Grade 10, Integrated into Grade 10\nIntegrated into Grade 10, Integrated into Grade 10\nIntegrated into Grade 10, Integrated into Grade 10\nIntegrated into Grade 10, Integrated into Grade 10\nIntegrated into Grade 10, Integrated into Grade 10\nIntegrated into Grade 10, Integrated into Grade 10\nIntegrated into Grade 10, Integrated into Grade 10\nIntegrated into Grade 10, Integrated into Grade 10\nIntegrated to Grade 9, Instructional Projects\nIntegrated into Grade 10, Instructional Projects\nIntegrated into Grade 10, Instructional Projects\nIntegrated into Grade 10, Instructional Projects\nIntegrated into Grade 10, Instructional Projects\nIntegrated into Grade 10, Instructional Projects\nIntegrated from Grade 9 to 11, Instructional Projects\nIntegrated from Grade 9 to 11, Instructional Projects\n\nIntegrated to Grade 10, Integrated to Grade 11 (High School Science curriculum integrated\nIntegrated to Grade 10, Integrated to Grade 11 (High School Science curriculum integrated\nIntegrated to Grade 10, Integrated to Grade 11 (High School Science curriculum integrated\nIntegrated to Grade 10, Integrated to Grade 11 (High School Science curriculum integrated\nIntegrated to Grade 10, Integrated to Grade 11 (High School Science curriculum integrated\nIntegrated into Grade 12, Integrated into Grade 12 (Physical Science curriculum integrated\nIntegrated into Grade 5, Integrated into Grade 5 (Capital Markets curriculum integrated\nIntegrated to Grade 12, Integrated to Grade 12 (Physical Science curriculum integrated\nIntegrated into Grade 5, Integrated into Grade 5 (Capital Markets curriculum integrated\nIntegrated into Grade 3, Integrated into Grade 3 (Science curriculum integrated\nIntegrated into Grade 3, Integrated into Grade 3 (Science curriculum integrated\nIntegrated to Grade 3, Integrated into Grade 2 (Science curriculum integrated\nIntegrated into Grade 4, Integrated into Grade 4 (Science curriculum integrated\nIntegrated into Grade 3, Integrated into Grade 3 (Science curriculum integrated\nIntegrated into Grade 5, Integrated into Grade 4 (Science",
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284,
21,
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527,
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311,
279,
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220,
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20,
11,
584,
1288,
636,
279,
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21801,
1554,
18033,
24671,
13,
578,
9518,
315,
279,
1176,
21801,
27053,
279,
8547,
315,
15360,
1990,
279,
2978,
8333,
323,
279,
4207,
315,
6068,
85075,
13,
510,
2620,
358,
11,
435,
19593,
350,
4590,
279,
2068,
25750,
14603,
13,
578,
21801,
2786,
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430,
422,
279,
2978,
8333,
603,
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16,
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304,
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220,
16,
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1054,
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311,
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279,
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505,
872,
1304,
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0,
1442,
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1652,
71,
620,
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274,
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477,
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15,
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13,
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872,
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315,
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488,
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60,
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315,
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279,
220,
15,
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387,
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311,
603,
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311,
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17,
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220,
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11,
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220,
16,
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323,
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315,
24875,
6307,
311,
387,
6646,
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311,
1855,
1023,
994,
6273,
1115,
671,
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1963,
315,
279,
4483,
677,
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8844,
5603,
6835,
21022,
4056,
52,
23465,
907,
315,
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220,
16,
11,
16,
320,
91,
64,
91,
4,
285,
8226,
584,
649,
3790,
13,
2001,
12,
14336,
264,
503,
279,
3685,
907,
27230,
323,
284,
220,
16,
13,
6268,
22744,
19032,
220,
1041,
11462,
72,
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220,
19,
489,
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1418,
220,
22,
62,
20,
220,
1049,
17,
863,
5252,
279,
393,
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10462,
12116,
323,
24218,
418,
70,
264,
1540,
1917,
602,
12,
8651,
2345,
29,
7072,
1989,
2320,
9037,
291,
54675,
11578,
82,
367,
13,
9636,
427,
28033,
482,
374,
369,
279,
14290,
15374,
17355,
6125,
788,
279,
30257,
2011,
387,
7701,
12207,
13,
304,
420,
1162,
1226,
3358,
1650,
1403,
7482,
100595,
2233,
22078,
11595,
13,
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4360,
459,
51150,
125312,
7891,
13,
362,
29967,
16711,
11,
4619,
315,
30689,
814,
6928,
308,
8547,
281,
4610,
526,
25346,
11,
90825,
510,
2118,
7819,
3343,
13189,
1666,
25027,
549,
15,
527,
810,
10180,
41500,
505,
279,
9526,
13,
1226,
2011,
9855,
490,
3267,
931,
304,
420,
374,
3062,
3044,
97690,
430,
649,
387,
279,
1403,
1175,
1885,
12,
7028,
11877,
1272,
4,
1054,
20,
220,
17,
11877,
1431,
304,
420,
6908,
11240,
311,
1689,
1009,
603,
311,
459,
10074,
8866,
1122,
3635,
304,
279,
549,
13946,
1701,
8909,
1008,
13479,
11668,
315,
2500,
12741,
2175,
510,
49,
406,
60,
9702,
3306,
510,
1764,
19260,
527,
1101,
25,
7701,
5199,
220,
5313,
1034,
374,
502,
8941,
41999,
308,
8977,
320,
307,
19581,
70,
26252,
78,
5552,
311,
16729,
3403,
11,
3161,
1063,
907,
477,
279,
4871,
315,
279,
32917,
374,
304,
1862,
19546,
19671,
25,
5046,
927,
8318,
2065,
3959,
375,
1636,
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2265,
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22564,
71,
25,
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315,
701,
10734,
507,
323,
75,
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485,
279,
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315,
220,
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323,
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555,
312,
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25,
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411,
60,
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3407,
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374,
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320,
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11692,
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78,
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30,
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13,
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320,
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78,
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387,
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65,
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0,
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8,
220,
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16,
1034,
2030,
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85769,
279,
16495,
9610,
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11,
279,
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374,
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220,
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369,
279,
549,
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323,
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35295,
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279,
892,
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2609,
82,
23355,
117370,
102148,
102372,
85009,
18142,
311,
29581,
72084,
3614,
13239,
304,
264,
24526,
10292,
46665,
3095,
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374,
393,
8428,
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16,
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220,
18,
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320,
47,
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495,
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25,
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24031,
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220,
16,
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320,
17,
5,
18,
8,
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21204,
1532,
320,
11,
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13,
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315,
220,
16,
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220,
18,
37601,
527,
284,
353,
3489,
812,
13,
95808,
2650,
584,
19698,
430,
41784,
14128,
67592,
372,
6068,
85075,
1436,
2533,
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21350,
66,
118466,
1551,
529,
370,
282,
363,
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13,
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499,
1766,
264,
18979,
763,
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445,
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532,
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1093,
473,
671,
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88,
468,
41,
13,
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2656,
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6301,
675,
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13,
311,
279,
43299,
374,
6273,
13,
1556,
374,
65721,
273,
6754,
5751,
12309,
1579,
2536,
38029,
7222,
13468,
832,
369,
220,
21006,
105181,
2684,
3361,
13014,
82,
7554,
1057,
11909,
13,
584,
1427,
520,
264,
94761,
70242,
1560,
584,
527,
8104,
8173,
4871,
279,
30296,
1990,
6068,
85075,
505,
1377,
3271,
552,
1652,
6253,
20940,
11867,
304,
1400,
1082,
315,
14580,
426,
800,
2538,
1022,
10453,
2001,
59,
2118,
35502,
21701,
9675,
8696,
304,
14013,
315,
84532,
13,
5751,
30490,
40590,
1880,
863,
220,
15,
315,
82779,
45775,
24610,
685,
39662,
86424,
91426,
18825,
30806,
57175,
78075,
49890,
311,
42070,
1054,
28930,
3468,
318,
5859,
1226,
527,
6041,
449,
279,
1162,
315,
97298,
367,
220,
20758,
17608,
5728,
220,
4645,
21641,
3226,
220,
16,
11,
22379,
11247,
2304,
4987,
473,
15154,
1057,
7999,
84140,
433,
69413,
22733,
584,
21542,
955,
117128,
34613,
101487,
220,
15,
315,
7033,
47576,
1057,
1455,
27823,
1880,
25470,
747,
871,
574,
539,
2874,
418,
1320,
315,
279,
41921,
677,
6740,
18679,
8266,
305,
14469,
311,
312,
668,
38181,
374,
5534,
3325,
1102,
961,
4147,
19574,
323,
220,
4645,
5162,
20502,
23587,
18004,
11775,
38254,
77728,
1880,
18217,
288,
20143,
275,
1142,
9249,
63123,
6286,
3201,
520,
28238,
1389,
2394,
60608,
26497,
21312,
10005,
45635,
31965,
422,
82614,
101690,
8164,
124530,
57352,
238,
241,
863,
2297,
863,
330,
13114,
281,
95682,
102217,
55,
374,
15783,
2230,
1838,
12181,
1054,
83,
39903,
102753,
41873,
1532,
4194,
1226,
1755,
14459,
38893,
220,
605,
320,
27477,
315,
584,
617,
220,
17,
11,
912,
101687,
119205,
1482,
13337,
322,
15,
8,
21387,
14393,
220,
9639,
11030,
6539,
36882,
1299,
19179,
9116,
4777,
26934,
16238,
374,
279,
264,
39374,
116221,
101134,
78785,
11,
279,
11235,
29760,
1686,
735,
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100993,
106525,
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13,
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101746,
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30124,
574,
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82,
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314,
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320,
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15901,
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350,
15406,
5173,
21708,
12426,
97065,
220,
21,
384,
2514,
33982,
108666,
11453,
101055,
863,
2118,
86491,
863,
2001,
2118,
27435,
73510,
87372,
117991,
56,
12988,
58890,
23227,
33081,
1532,
264,
119379,
121179,
109532,
323,
220,
12754,
26328,
17000,
17609,
11702,
220,
16,
1482,
3,
15752,
105030,
220,
20304,
1419,
497,
116498,
220,
1049,
18,
284,
94617,
220,
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22,
732,
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220,
22,
571,
43,
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12,
31274,
578,
13758,
469,
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355,
220,
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17448,
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3957,
220,
18,
39,
15,
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220,
21860,
88996,
320,
8358,
23,
13,
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220,
15,
270,
6375,
21825,
311,
2874,
78,
100319,
103767,
101055,
14680,
320,
300,
1304,
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11,
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122,
106,
105316,
117470,
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527,
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110233,
248,
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8,
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2111,
58768,
6514,
7724,
315,
279,
97562,
320,
19274,
100562,
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21,
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412,
22287,
15830,
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4103,
220,
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60,
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220,
23388,
26661,
13,
3443,
49520,
110722,
5591,
1482,
60376,
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142,
122,
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24697,
627,
860,
8797,
12051,
271,
90,
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2331,
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19,
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12,
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578,
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374,
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15105,
304,
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279,
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315,
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527,
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602,
320,
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10924,
832,
810,
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883,
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1630,
57,
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1820,
510,
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1319,
6440,
82,
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13,
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3187,
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420,
26,
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437,
2041,
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16416,
1265,
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57,
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527,
369,
2225,
8310,
323,
369,
25,
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16,
323,
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17,
13,
578,
1646,
29803,
527,
25,
364,
510,
49,
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17,
220,
806,
482,
17,
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17,
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18178,
12144,
482,
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100562,
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20568,
26,
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31649,
374,
1511,
311,
594,
1545,
1521,
2819,
311,
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14960,
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}
|
"100\n\nState the common demoninator for the fractions 1/2, 2/5, 7/8.\n\n100\n\n1/2\n\n100\n\n4/12 o(...TRUNCATED)
| [128000,1041,271,1423,279,4279,7097,17720,369,279,65995,220,16,14,17,11,220,17,14,20,11,220,22,14,23(...TRUNCATED)
| 4,098
| {"text":"100\n\nState the common demoninator for the fractions 1/2, 2/5, 7/8.\n\n100\n\n1/2\n\n100\n(...TRUNCATED)
|
"Here are your homework resources for fall 2016 geometry A. Textbooks will have a rigid blue answer (...TRUNCATED)
| [128000,8586,527,701,29559,5070,369,4498,220,679,21,17484,362,13,2991,12383,690,617,264,33956,6437,4(...TRUNCATED)
| 4,098
| {"text":"Here are your homework resources for fall 2016 geometry A. Textbooks will have a rigid blue(...TRUNCATED)
|
"Wrong Robert\n\n# Question 3\n\nPossible answers: 2, 6, 8, 14, 30\n\nYou are told in total you have(...TRUNCATED)
| [128000,30285,8563,271,2,16225,220,18,271,66322,11503,25,220,17,11,220,21,11,220,23,11,220,975,11,22(...TRUNCATED)
| 4,098
| {"text":"Wrong Robert\n\n# Question 3\n\nPossible answers: 2, 6, 8, 14, 30\n\nYou are told in total (...TRUNCATED)
|
"If you make uses of a fireplace by the middle of the winter months, it is necessary to know just ho(...TRUNCATED)
| [128000,2746,499,1304,5829,315,264,40511,555,279,6278,315,279,12688,4038,11,433,374,5995,311,1440,11(...TRUNCATED)
| 4,098
| {"text":"If you make uses of a fireplace by the middle of the winter months, it is necessary to know(...TRUNCATED)
|
"22 October, 17:07\n\n# Consider the function f (x) = 2x + 3. A) Determine what number would need to(...TRUNCATED)
| [128000,1313,6664,11,220,1114,25,2589,271,2,21829,279,734,282,320,87,8,284,220,17,87,489,220,18,13,3(...TRUNCATED)
| 4,098
| {"text":"22 October, 17:07\n\n# Consider the function f (x) = 2x + 3. A) Determine what number would(...TRUNCATED)
|
"+0\n\n# Circle\n\n0\n98\n2\n\nThree unit circles are tangent to each other, as depicted below. Dete(...TRUNCATED)
| [128000,10,15,271,2,21918,271,15,198,3264,198,17,271,20215,5089,26432,527,69760,311,1855,1023,11,439(...TRUNCATED)
| 4,098
| {"text":"+0\n\n# Circle\n\n0\n98\n2\n\nThree unit circles are tangent to each other, as depicted bel(...TRUNCATED)
|
"2015-02-12T13:27:37-05:00\n1. 3(8x-6y)=(-y)\n\nFor this you need to use the distribution method and(...TRUNCATED)
| [128000,679,20,12,2437,12,717,51,1032,25,1544,25,1806,12,2304,25,410,198,16,13,220,18,7,23,87,12,21,(...TRUNCATED)
| 4,098
| {"text":"2015-02-12T13:27:37-05:00\n1. 3(8x-6y)=(-y)\n\nFor this you need to use the distribution me(...TRUNCATED)
|
"POPULAR\n\n# How two years or less are present for my 15oz. and how its working.\n\nLast updated 20(...TRUNCATED)
| [128000,48362,7226,271,2,2650,1403,1667,477,2753,527,3118,369,856,220,868,9700,13,323,1268,1202,3318(...TRUNCATED)
| 4,098
| {"text":"POPULAR\n\n# How two years or less are present for my 15oz. and how its working.\n\nLast up(...TRUNCATED)
|
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