环比同比YOY\QoQ及QQ\PP图Q-Q\P-P…

 

QoQ(quarter overquarter)：季营收成长(衰退)率　是指今年该季的营收金额与上一季或去年同一季的营收金额的成长(衰退)百分比率

billion=10亿=1000，000，000；million=100万=1000，000

YoY：Year over Year，意为同去年同期相比。YoY(Year-on-yearpercentage)，是指当期的数据较去年同期变动多少。例如，甲公司今年11月营收115亿元，去年11营收为100亿元，则营收YoY为15%。

YoY (Year-over-Year) figures report thechanges in a year’s worth of data, in comparison with the previousyear. YoY incorporates more data and thus is able to give a betterlong-term picture of the underlying report figure.

 

an abbreviation for year-over-year oryear-on-year, meaning "in comparison with the same period lastyear."
abbreviation美: [əˌbriviˈeɪʃ(ə)n]英:[əˌbriːviˈeɪʃ(ə)n],n.缩写；【数学】约分；【音乐】
Mom: 是月度比较,Month-over-Month,比如 （8月-7月）/7月 *%

 环比
与上一统计段比较，例如2005年7月份与2005年6月份相比较，叫环比。与历史同时期比较，例如2005年7月份与2004年7月份相比，叫同比。环比增长率=（本期数－上期数）/上期数×100%。反映本期比上期增长了多少；环比发展速度，一般是指报告期水平与前一时期水平之比，表明现象逐期的发展速度。环比=（本统计周期数据/上统计周期数据）×100%。

QoQ季度环比；环比增速；环比增长

例：Although CPI was slightly softer than expected at 0. 6% QoQ in Q3, the AUD took the news badly.

尽管第三季CPI按季比较为增长0.6%，较预期稍低，消息让澳元大受打击。

QQ图(quantile—quantile)美: ['kwɒntaɪl] 英:分位数。

https://en.wikipedia.org/wiki/Q–Q_plot

In statistics, a Q–Q plot^[1] ("Q"stands for quantile)is a probabilityplot, which is a graphicalmethod forcomparing two probabilitydistributions by plotting their quantilesagainst each other. First, the set of intervals for the quantilesis chosen. Apoint (x, y) onthe plot corresponds to one of the quantiles of the seconddistribution (y-coordinate)plotted against the same quantile of the first distribution(x-coordinate).Thus the line is a parametric curve with the parameter which is the(number of the) interval for the quantile.

If the two distributions being compared are similar, the points inthe Q–Q plot will approximately lie on the line y = x.If the distributions are linearly related, the points in the Q–Qplot will approximately lie on a line, but not necessarily on theline y = x.Q–Q plots can also be used as a graphical means of estimatingparameters in a location-scalefamily of distributions.

是一种散点图,对应于正态分布的QQ图,就是由标准正态分布的分位数为横坐标,样本值为纵坐标的散点图.要利用QQ图鉴别样本数据是否近似于正态分布,只需看QQ图上的点是否近似地在一条直线附近,而且该直线的斜率为标准差,截距为均值.用QQ图还可获得样本偏度和峰度的粗略信息。

Q-QP-P图是根据变量的累积比例与指定分布的累积比例之间的关系所绘制的图形。通过P-P图可以检验数据是否符合指定的分布。当数据符合指定分布时，P-P图中各点近似呈一条直线。如果P-P图中各点不呈直线，但有一定规律，可以对变量数据进行转换，使转换后的数据更接近指定分布。Q-Q图同样可以用于检验数据的分布，所不同的是，Q-Q图是用变量数据分布的分位数与所指定分布的分位数之间的关系曲线来进行检验的。图

 

spss统计图图表分析：分析——描述统计——Q-Q概率图
 对照一些检验分布的分位数，绘制某个变量分布的分位数。概率图通常用于确定某个变量的分布是否与给定分布匹配。如果选定变量与检验分布匹配，则点聚集在某条直线周围。

QQ图里面的点如果投影到X轴的话就是标准正太分布的几个分位数？

how to interpret the Q-Q plot:

http://stats.stackexchange.com/questions/101274/how-to-interpret-a-qq-plot

 

The QQ plot shows the expecteddistribution of association test statistics (X-axis) across themillion SNPs compared to the observed values (Y-axis). Anydeviation from the X=Y line implies a consistent difference betweencases and controls across the whole genome (suggesting a bias likethe ones I’ve mentioned). A clean QQ plot (see below), on the otherhand, should show a solid line matching X=Y until it sharply curvesat the end (representing the small number of true associationsamong thousands of unassociated SNPs). The blue points in thisfigure show what’s left after removing the validated associations,which shows that most of that tail was, in fact, due to truedisease variants, but also that more interesting results mightstill be lurking in the data.

The QQ plot is a graphicalrepresentation of the deviation of the observed P values from thenull hypothesis: the observed P values for each SNP are sorted fromlargest to smallest and plotted against expected values from atheoretical χ2-distribution. If the observed values correspond tothe expected values, all points are on or near the middle linebetween the x-axis and the y-axis (null hypothesis: light gray linein Fig. 2b and c). If some observed P values are clearly moresignificant than expected under the null hypothesis, points willmove towards the y-axis, as shown in Figure 2b. If there is anearly separation of the expected from the observed (Fig. 2c), thismeans that many moderately significant Pvalues are more significantthan expected under the null hypothesis. This result is rarely dueto thousands of true positives; more often, it is due to populationstratification: systematic differences in allele frequenciesbetween subpopulations of the collection of individualsinvestigated, so that a large number of P values are smaller thanexpected from chance alone.

可用的检验分布包括：beta、卡方、指数、gamma、半正态、Laplace、Logistic、Lognormal、正态、排列、Student'st、Weibull 和均匀分布。根据选定的分布，您可以指定自由度及其它参数。

• 您可获取转换值的概率图。转换选项包括自然对数、标准化值、差分和季节性差分。

• 您可指定计算期望分布，以及求解“连结”（对相同值有多个观察结果）的方法。

视频：http://www.iqiyi.com/w_19rtbmypth.html

P-P 图
 对照一些检验分布的累积比例，绘制某个变量的累积比例。概率图通常用于确定某个变量的分布是否与给定分布匹配。如果选定变量与检验分布匹配，则点聚集在某条直线周围。

By P-P plot we meant Probability-Probability plot or Percentage-Percentage plot used in SPSS research.It is a probability plot which is used for assign how closely thetwo data sets located. For creating this plot two cumulativedistribution of the required data sets are needed. It is useful tocompare within the two probability distributions which can belocate near or far from each other. This plot has limited use incomparing two variables rather than use in explaining theoreticalmodel. Additionally, as a graphical adjunct of the fit ofprobability distributions, this P-P plot can be used in SPSSresearch.

关于P-P图的说明：https://en.wikipedia.org/wiki/P–P_plot

可用的检验分布包括：beta、卡方、指数、gamma、半正态、Laplace、Logistic、Lognormal、正态、排列、Student'st、Weibull 和均匀分布。根据选定的分布，您可以指定自由度及其它参数。

• 您可获取转换值的概率图。转换选项包括自然对数、标准化值、差分和季节性差分。

• 您可指定计算期望分布，以及求解“连结”（对相同值有多个观察结果）的方法。