User:Gesalbte
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Contents
Introduction[edit]
This page is still under construction. Therefore some parts of it don't make any senses. Please don't laugh your ass off. 
Born into Uncyclopedia, this user is an extraordinary dumbfuck^{[1]}, who has messed up everybody in Spring Weekend^{[2]}.
This user is currently a student studying finance in college and enjoys the sunshine, rain, and forest of Connecticut. If you have to excuse him, please leave message in the discussion page.
This user is a dumbfuck writer^{[3]} and wrote a lot of stuff that appreciated by his professor^{[4]}. For example, the sentence below comes from Nostra Via in Regnum Ducit:
“  Only after thou hast suffered the darkened night, there cometh the light.  ” 
Statistics[edit]
User:Disavian/Userboxes/UbxNight
Chapter 2[edit]
interquartile range (IQR; 四分位距); Outlier: z＞2; ext: z＞3
Chebychev’s Theorem: at least data is within ( ), Empirical Rule: approx. 68% at z ∈ (1s, 1s), approx. 95% at z ∈ (2s, 2s), approx. 99.7% at z ∈ (3s, 3s)
Chapter 3[edit]
: from n choose (组合) k; : from n permute (排列) k.
 intersection, : union, Ac: complementary. ; factorials (n!);
Chapter 4[edit]
 Discrete random variables (rv); STAT → CALC → 1Var Stats → L1(值), L2(个数)
Binomial r.v. (二项分布; Bernoulli trial: cointoss); =math. expectation=np,
Probability Density Function: ; DISTR → binompdf (n, p, k). [n个成功率为p的伯努利实验，成功k次的概率是多少？] Cumulative Distribution Function: DISTR → binomcdf (n, p, k) [n个成功率为p的伯努利实验，至少成功k次的概率是多少？]
Uniform (平均) distribution: ; ; ; .
Normal distribution (正态分布): Z ~ N (), ~ N (0, 1); std. norm.: N (0, 1);
Cumulative Distribution Function: DISTR → normalcdf (a, b, μ, σ). [对于正态分布N ()，作下限为a，上限为b的定积分得多少？] Quantile function: DISTR → invnorm (p, μ, σ). [对于正态分布N ()，作积分下限为∞的定积分得p时，积分上限是多少？]
Criteria for determining whether normal distribution: 1) histogram or stem and leaf display is bell shaped; 2) data satisfies empirical rule; 3) IQR/s = (Q3Q1)/s is approximately 1.3; 4) a normal probability plot is approximately linear. [TI: 1) clear y functions 2) enter data 3) 2ND → Y= → ... → 6th plot, x axis 4) ZOOM 9]
Central Limit Theorem: 许多(n>30)平均值标准差的分布，其平均值的分布为N (, ).
Chapter 5: Jargon Box: confidence coefficient(置信系数; 1α), confidence level(置信度; 100×(1α)).
A. 根据样本猜总体的平均值μ: = , where = invT(1/2, n1), n1是自由度.
Also that Tdistribution function (normal + centered at 0, fatter tails; df = n1↑tend to be normal). Std. dev. of Tdistribution is , its Cumulative Distribution Function: DISTR → tcdf(a, b, df), where df=n1 [对于自由度df的T分布，作下限为a，上限为b的定积分得多少？]; its Quantile function: DISTR → invT(p, df) [对于自由度为df的T分布，作积分下限为∞的定积分得p时，积分上限是多少？].
近似: When it’s large (n≥30) and σ is known, we can use zCI instead: = ,
where = invNorm(1/2), 1/2 = (1CC)/2. [ = 1.645; = 1.960; = 2.576]
已知置信区间，求最小样本容量: for zCI, , therefore . Why not tCI ( )? Because n1 in you cannot bring it out.
B. 根据样本的比例猜总体的比例p: p , when we have large samples ( , ), there , . This function is based on ; , where .
When p nears 0 or 1, use adjusted confidence interval , where . Sampling error (SE; SE=.5Width) or margin of error (ME): , therefore .
Chapter 6[edit]
Hypothesis testing [无论如何，总体分布必须为正态的时候才能检定]
Type I error: rejected a correct H0; Type II error: failed to reject a wrong H0. The smaller
selected, the more evidence (larger z) needed to reject H0. 思想罪: 思想就是犯罪！^{[5]}
A. ttest: H0说μ0, 你不相信, 就搞了Ha: , s, n, 代入下面这个公式, 看看你的图像牛逼不？
 where is the sample mean, μ0 is the claimed mean (=H0), s is sample std.dev.
Left tailed test: Ha: μ < μ0; reject H0 if t < t = invT(1α, n1); pvalue = tcdf(10^99, t, n1). Two tailed test: Ha: μ ≠ μ0; reject H0 if t [t, t], where t = invT(1α/2, n1); pvalue = 2×tcdf(t, 10^99, n1). Right tailed test: Ha: μ > μ0; reject H0 if t > t = invT(1α, n1); pvalue = tcdf(t, 10^99, n1).
近似: ztest: when σ is known and sample size is very large (n>30).
 where is the sample mean, μ0 is the claimed mean (=H0), σ is population std.dev.
Left tailed test: Ha: μ < μ0; reject H0 if z < z = invNorm(1α); pvalue = normalcdf(10^99, z). Two tailed test: Ha: μ ≠ μ0; reject H0 if z [z, z], where z = invNorm(1α/2); pvalue = 2×normalcdf(z, 10^99). Right tailed test: Ha: μ > μ0; reject H0 if z > z = invNorm(1α); pvalue = normalcdf(z, 10^99).
B. ztest for population proportion: ; .
 where is sample proportion, p0 is the claimed mean proportion (=H0).
Left tailed test: Ha: p < p 0; reject H0 if z < z = invNorm(1α); pvalue = normalcdf(10^99, z). Two tailed test: Ha: p ≠ p0; reject H0 if z [z, z], where z = invNorm(1α/2); pvalue = 2×normalcdf(z, 10^99). Right tailed test: Ha: p > p 0; reject H0 if z > z = invNorm(1α); pvalue = normalcdf(z, 10^99).
Chapter 7[edit]
Large sample: CI = ; Test statistic:
One tailed test:
H0: (μ1μ2) = D0
Ha: (μ1μ2) < D0
[or Ha: (μ1μ2) > D0]
Two tailed test:
H0: (μ1μ2) = D0
Ha: (μ1μ2) ≠ D0
where D0 = hypothesized difference between the means (often it is equal to 0)
Rejection region: z < 
[or z > ]
Rejection region: z >
ttest: CI = , where , = invT(1/2, )
 proportion test
 CI = ,
, CI = ; , CI = ; where , 其他就是差.
Test[edit]
This section is a code test. Please ignore it and leave it alone. Thank you for your cooperation. 
This article is written by a dumbfuck and may require cleanup. Please help improve it by rewriting it in an nondumbfucking style. 
Laugh My Ass Off[edit]
The person is out of his mind. User:Fastily/Userboxes/Hopelesseditingaddict User:Strdst grl/ubx/mandelbrot User:Teinesavaii/Polynesian Userboxes/Shrink User:UBX/For rent User:Strdst grl/ubx/bouncing User:Edit Centric/UBX/ArmBears User:P.B. Pilhet/UBX/NDC
This is the talk page for discussing improvements to User:Gesalbte.  


Major[edit]
Minor[edit]
Test Reference[edit]
This section is where I'm testing the citation codes, to see if they are correctly written.
Hooray![edit]
 ↑ Philip talked to Angela: Oh my God! This chink is an an extraordinary dumbfuck!
 ↑ Andrew: Yeah! Get some pussies. You're the man, I appreciate that.
 ↑ Philip: What the f is wrong with you? What the f is this? This is English? Are you fing retard?
 ↑ Who told you that?
 ↑ G. Orwell: 1984. Crimestop your thinkcrime! You just committed a facecrime!