Wednesday, March 22, 2017


讓慘痛的撕裂變成蒼白淡淡的記憶,讓執著的人選擇離開 。

時間教你歷經滄桑,在人來人往之間,明白簡單不過的道理 :萬般多是巧合。


Sunday, March 19, 2017



1. 首先在一個乾淨的盆子中放入約兩大匙的太白粉
2. 將葡萄連著蒂頭一顆顆剪下後放進盆中
3. 用雙手將葡萄以太白粉乾洗一下,再加入冷水沖開,清洗一下後倒掉
4. 最後用冷開水沖洗葡萄即可(重複做此動作大約三次,葡萄上只要沒有殘留太白粉就可以囉~)
5. 完成!要獲得乾淨透亮的葡萄,就是這麼容易。

1. 葡萄要連著蒂頭剪下,是為了避免在清洗過程中,髒汙或農藥經由拔掉蒂頭後的洞口流進去哦~
2. 太白粉會帶掉葡萄的髒污,如果用牙膏洗,容易殘留牙膏味,使的葡萄香氣減分~所以快來試試看用太白粉洗葡萄吧!

criminal cases

Criminal Cases per 100,000 Population

u pay into social security system with every paycheck (it is the deduction listed under FICA)

Saturday, March 18, 2017









Friday, March 17, 2017

panel data, excel file coding example

type 1

Year Country GDP Unemployment Inflation
1990         USA 45.18521 71.83384877 71.1575
1991         USA 23.04601 78.76719258 56.60289
1992         USA 29.69415 46.21706091 70.55974
1993         USA 53.55889 78.60050714 77.54319
1994        USA 19.47274 31.85681226 76.86575
1995        USA 91.50116 98.91007925 63.9196
1996         USA 24.84979 40.67420505 58.7951
1997         USA 53.52124 45.11429455 18.41375
1990         UK 85.98417 2.065408081 79.77445
1991        UK 87.49779 96.39854289 39.46303
1992         UK 68.00752 53.67362608 10.63008
1993          UK 89.58283 36.18780323 24.06416
1994         UK 36.88112 14.28218606 95.576
1995          UK 13.51797 39.154493 75.34766
1996          UK 44.41815 69.83427487 46.24098
1997         UK 15.82823 63.36841282 26.53226

type 2
A second type of format that is used in Excel, is where the columns represent dates, then the rows represent both cross-sections and variables

State Variable Yr1940 Yr1941 Yr1942 Yr1943
Alabama GDP 0.442658 0.315485 0.109414 0.411838
Alabama Unemp 0.652004 0.171815 0.929798 0.878353
Alabama House Price 0.818003 0.2169 0.487288 0.155818
Alabama Prisons 0.636364 0.458292 0.946148 0.352237
Alabama School Budget 0.578769 0.90547 0.277085 0.417637
Alabama Police Budget 0.745779 0.159693 0.150518 0.310762
Alaska GDP 0.51315 0.239402 0.690371 0.902755
Alaska Unemp 0.047899 0.329383 0.039415 0.261495
Alaska House Price 0.430723 0.457658 0.688661 0.359665
Alaska Prisons 0.031685 0.376072 0.836634 0.534889
Alaska School Budget 0.665001 0.99451 0.525446 0.998323
Alaska Police Budget 0.461253 0.327956 0.373816 0.797453
Arkansas GDP 0.751544 0.405693 0.252975 0.160238
Arkansas Unemp 0.592959 0.467335 0.360323 0.411224
Arkansas House Price 0.605316 0.236436 0.824262 0.488503
Arkansas Prisons 0.826649 0.613368 0.100428 0.197751
Arkansas School Budget 0.038491 0.358908 0.020196 0.373979
Arkansas Police Budget 0.362473 0.253577 0.393876 0.126115

Panel data, longitudinal data, cross-sectional time series data

Panel data, also called longitudinal data or cross-sectional time series data, are data where multiple cases (people, firms, countries etc) were observed at two or more time periods.

multiple cities, observed at two or more time periods

pooled time series vs pure panel data

Pooled analysis combines time series for several cross-sections. Pooled data are characterized by having repeated observations (most frequently years) on fixed units (most frequently states and nations).

Both pooled cross sectional data and pure panel data collect data over tine (this can range from 2 time periods to any large number). They key difference between the two is the "units" we follow. I am defining units as households, countries, or whatever we are collecting data on.
In pooled cross section, we will take random samples in different time periods, of different units, i.e. each sample we take, will be populated by different individuals. This is often used to see the impact of policy or programmes.
For example we will take household income data on households X, Y and Z, in 1990. And then we will take the same income data on households G, F and A in 1995. Although we are interested in the same data, we are taking different samples (using different households) in different time periods.

In pure panel data, we are following the same units i.e. the same households or individuals over time. For example we will follow the same set of households X, Y and Z, for each time period we collect data i.e. in 1990 and we will also interview the same households in 1995.

spss - How to perform pooled cross-sectional time series analysis

Wednesday, March 15, 2017

Logistic Regression vs. Logit

Logistic Regression
In simple terms the logistic regression is a version of multiple regression where the outcome variable is binary (dichotomous), meaning there are only two possible outcomes. The model can be used to calculate the probability of one of the two outcomes occurring over the other for a given case/observation by using the values of a set of known explanatory variables.

This glossary entry refers to a binary logit, the type used in logistic regression. Without getting too technical Logits are basically transformations of existing binary outcome variable data points into a probability P (ranging from 0 to 1). A logit curve is therefore a graph of these logits plotted against an explanatory variable.

Log-likelihood, logit, -2 log-likelihood (-2LL)

Analogous to the Sum of Squares in Multiple regression, the -2 log-likelihood (-2LL) provides us with an indication of the total error that is in a logistic regression model. The larger the value of the -2LL the less accurate the predictions of the model are. 

The deviance, or -2 log-likelihood (-2LL) statistic
The deviance is basically a measure of how much unexplained variation there is in our logistic regression model – the higher the value the less accurate the model.

Deviance (-2LL)
This is the log-likelihood multiplied by -2 and is commonly used to explore how well a logistic regression model fits the data. The lower this value is the better your model is at predicting your binary outcome variable.

Multiplying it by -2 is a technical step necessary to convert the log-likelihood into a chi-square distribution, which is useful because it can then be used to ascertain statistical significance. Don't worry if you do not fully understand the technicalities of this.

The deviance has little intuitive meaning because it depends on the sample size and the number of parameters in the model as well as on the goodness of fit.

R2 equivalents for logistic regression
The two versions most commonly used are
Hosmer - Lemeshow’s R2
Nagelkerke’s R2

Both describe the proportion of variance in the outcome that the model successfully explains. Like R2 in multiple regression these values range between ‘0’ and ‘1’ with a value of ‘1’ suggesting that the model accounts for 100% of variance in the outcome and ‘0’ that it accounts for none of the variance. Be warned: they are calculated differently and may provide conflicting estimates!

Hosmer-Lemeshow Goodness of fit – This option provides a X2 (Chi-square) test of whether or not the model is an adequate fit to the data. The null hypothesis is that the model is a ‘good enough’ fit to the data and we will only reject this null hypothesis (i.e. decide it is a ‘poor’ fit) if there are sufficiently strong grounds to do so (conventionally if p<.05). We will see that with very large samples as we have here there can be problems with this level of significance, but more on that later.

in Model Summary, This table contains the Cox & Snell R Square and Nagelkerke R Square values, which are both methods of calculating the explained variation. These values are sometimes referred to as pseudo R2 values (and will have lower values than in multiple regression). However, they are interpreted in the same manner, but with more caution. Therefore, the explained variation in the dependent variable based on our model ranges from 24.0% to 33.0%, depending on whether you reference the Cox & Snell R2 or Nagelkerke R2 methods, respectively. Nagelkerke R2 is a modification of Cox & Snell R2, the latter of which cannot achieve a value of 1. For this reason, it is preferable to report the Nagelkerke R2 value.

interpret output, Binomial Logistic Regression, spss -- 頁面下方

4.12 The SPSS Logistic Regression Output

log odds is different from odds ratio

for spss

Odds Ratio is labelled as Exp (B)

if male=0 (reference category)  female=1
The Exp(B) column (the Odds Ratio) shows 1.37.
Means that females are 1.37 times more likely than males (reference category, code 0) to achieve fiveem, after controlling for other independent variables.

Dichotomous (or dummy) explanatory variables

For a dichotomous explanatory variable the OR is simply the difference between the odds for the base category (x=0, male) and the other category (x=1, female). Thus in our earlier example for gender and aspirations the OR was 2.0 indicating girls (x=1) were twice as likely as boys (x=0) to aspire to continue in FTE.

OR is 2. While the OR is sufficient for meaningful interpretation, some researchers also like to express the OR in percentage terms. Subtracting 1 from the OR and multiplying by 100 gives the percentage change. Thus (2-1) *100 = a 100% increase in the odds.
 (1-OR) *100

If odd ratios is 1.37, females (code 1) are about 1.37 times (or 37%) more likely to achieve fiveem than males (code 0).
(1.37-1)*100= 37%

If OR is 1.48, (1.48-1)*100= 48 %

spss Logistic Regression Sub-menus

if you click save, then choose the following four

Predicted probabilities – This creates a new variable that tells us for each case the predicted probability that the outcome will occur (that fiveem will be achieved) based on the model.

Predicted Group Membership – This new variable estimates the outcome for each participant based on their predicted probability. If the predicted probability is >0.5 then they are predicted to achieve the outcome, if it is <.5 they are predicted not to achieve the outcome. This .5 cut-point can be changed, but it is sensible to leave it at the default. The predicted classification is useful for comparison with the actual outcome!

Residuals (standardised) – This provides the residual for each participant (in terms of standard deviations for ease of interpretation). This shows us the difference between the actual outcome (0 or 1) and the probability of the predicted outcome and is therefore a useful measure of error.

Cook’s – We’ve come across this in our travels before. This generates a statistic called Cook’s distance for each participant which is useful for spotting cases which unduly influence the model (a value greater than ‘1’ usually warrants further investigation).

if you click option, then

Classification plots – Checking this option requests a chart which shows the distribution of outcomes over the probability range (classification plot). This is useful for visually identifying where the model makes most incorrect categorizations.

Hosmer-Lemeshow Goodness of fit – This option provides a X2 (Chi-square) test of whether or not the model is an adequate fit to the data. The null hypothesis is that the model is a ‘good enough’ fit to the data and we will only reject this null hypothesis (i.e. decide it is a ‘poor’ fit) if there are sufficiently strong grounds to do so (conventionally if p<.05). We will see that with very large samples as we have here there can be problems with this level of significance, but more on that later.

CI for exp(B) – CI stands for confidence interval and this option requests the range of values that we are confident that each odds ratio lies within. The setting of 95% means that there is only a p < .05 that the value for the odds ratio, exp(B), lies outside the calculated range (you can change the 95% confidence level if you are a control freak!).

Casewise listing of residuals 

dummy variable in regression

A dummy variable is a variable for which all cases falling into a specific category assume the value of 1 and all cases not falling into that category assume a value of zero.

Coding convention: 0 for the value that does not have the characteristic and 1 for the value that has the characteristic. e.g., My study focuses on female. Thus, male=0, female= 1

e.g, if the Income variable has four categories

You will end up having 4 dummy variables-- income100dollar(代號A),income200dollar(代號B),income300dollar(代號C),income400dollar(代號D) in the data coded as follows:
coding book
1 0 0 0
0 1 0 0
0 0 1 0
0 0 0 1

When you use this variable in a regression analysis, the dummy variable for reference category is omitted (some softwares will do that automatically for you).
e.g, I want to study female (code 1). Thus, male is code 0 (reference category)

One category, usually the one which contains the highest number of respondents, is designated as the 'reference' category (code 0) and does not have a dummy variable. All other categories have a dummy variable created for them. Participants are coded '1' if they belong to the particular category of each dummy variable and '0' if not. Participants who belong to the reference category are coded as '0' for all dummy variables.

The coefficients (B) for each of these new variables tell us how much difference in the outcome is predicted for a member of that category relative to members of the reference group. 

Tuesday, March 14, 2017

logit, categorical independent variable, dummy variable, spss

All explanatory variables need to be placed in what is named the covariates box. If the explanatory variable is continuous it can be dropped in to this box as normal and SPSS can be trusted to add it to the model, However, the process is slightly more demanding for categorical variables such as the three we wish to add because we need to tell SPSS to set up dummy variables based on a specific baseline category (we do not need to create the dummies ourselves this time).

To do this we need to click the button marked ‘Categorical’ to open a submenu. You need to move all of the explanatory variables that are categorical from the left hand list (Covariates) to the right hand window.

The next step is to tell SPSS which category is the reference (or baseline) category for each variable. To do this we must click on each in turn and use the controls on the bottom right of the menu which are marked ‘Change Contrast’. The first thing to note is the little drop down menu which is set to ‘Indicator’ as a default. This allows you to alter how categories within variables are compared in a number of ways (that you may or may not be pleased to hear are beyond the scope of this module). For our purposes we can stick with the default of ‘indicator’, which essentially creates dummy variables for each category to compare against a specified reference category 

All we need to do then is tell SPSS whether the first or last category should be used as the reference category (code 0) and then click ‘Change’ to finalise the setting.

Whether you choose Last or First will depend on how you set up your data. In this example, males are to be compared to females, with females acting as the reference category (who were coded "0"). Therefore, First is chosen.

For our Ethnic variable the first category is ‘0’ White-British (the category with the highest number of participants) so, as before, we will use this as the reference category.
Change the selection to ‘First’ and click ‘Change’.

For the Gender variable we only have two categories and could use either male (‘0’) or female (‘1’) as the reference. Previously we have used male as the reference so we will stick with this (once again, change the selection to ‘First’ and click ‘Change’).

For Socio Economic Class (sec) we will use the least affluent class (code 8) as the reference (‘Never worked/long term unemployed - 8’). This time we will use the ‘Last’ option given that the SEC categories are coded such that the least affluent one is assigned the highest value code. Remember to click ‘Change’! You will see that your selections have appeared in brackets next to each variable and you can click ‘Continue’ to close the submenu.

Ordinal logistic regression (often just called 'ordinal regression)

logit, spss output

Dependent variable
binomial logistic regression (often referred to simply as logistic regression), predicts the probability that an observation falls into one of two categories of a dichotomous dependent variable, based on one or more independent variables that can be either continuous or categorical. (If, on the other hand, your dependent variable is a count, see our Poisson regression guide. Alternatively, if you have more than two categories of the dependent variable, see our multinomial logistic regression guide.)

 If your dependent variable was not measured on a dichotomous scale, but a continuous scale instead, you will need to carry out multiple regression, whereas if your dependent variable was measured on an ordinal scale, ordinal regression would be a more appropriate starting point.

1.use binomial logistic regression to understand whether exam performance can be predicted based on revision time, test anxiety and lecture attendance

  • dependent variable--exam performance, dichotomous scale (passed or failed)
  • three independent variables: "revision time", "test anxiety" and "lecture attendance"
2.use binomial logistic regression to understand whether drug use can be predicted based on prior criminal convictions, drug use amongst friends, income, age and gender

  • dependent variable--drug use, measured on a dichotomous scale (yes or no)
  • five independent variables: "prior criminal convictions", "drug use amongst friends", "income", "age" and "gender"

3. n=200 high schools students

  • dependent variable (dichotomous variable):  female (1), male (0)
  • independent variable: scores on 4 tests: science, math, reading and social studies (socst)

Independent variable

  • You have one or more independent variables, which can be either continuous (i.e., an interval or ratio variable) or categorical (i.e., an ordinal or nominal variable). 
  • Continuous variables-- revision time (measured in hours), intelligence (measured using IQ score), exam performance (measured from 0 to 100), weight (measured in kg)
  • Ordinal variables --- Likert items (e.g., a 7-point scale from "strongly agree" through to "strongly disagree"), amongst other ways of ranking categories (e.g., a 3-point scale explaining how much a customer liked a product, ranging from "Not very much" to "Yes, a lot")
  • Nominal variables ---- gender (e.g., 2 groups: male and female), ethnicity (e.g., 3 groups: Caucasian, African American and Hispanic), profession (e.g., 5 groups: surgeon, doctor, nurse, dentist, therapist)
  • If you have a categorical variable with more than two levels (e.g., a three-level variable: low, medium ,high), you can tell SPSS to create the dummy variables necessary to include the variable in the logistic regression.
Assumption: to run logit, there needs to be a linear relationship between any continuous independent variables and the logit transformation of the dependent variable. Use the Box-Tidwell (1962) procedure to test for linearity
if a violation to the assumption is not correctable, you will no longer be able to use a binomial logistic regression (although you may be able to run another statistical test on your data instead). Just remember that if you do not run the statistical tests on these assumptions correctly, the results you get when running binomial logistic regression might not be valid. 

Testing assumptions

How to use SPSS
  • SPSS -- Analyze/Regression/Binary Logistic…
  • By default, SPSS does a listwise deletion of missing values.  This means that only cases with non-missing values for the dependent as well as all independent variables will be used in the analysis.

How to interpret report the Output of a Binomial Logistic Regression Analysis

multinomial logit vs multinomial probit

binomial logistic regression (often referred to simply as logistic regression), predicts the probability that an observation falls into one of two categories of a dichotomous dependent variable, based on one or more independent variables that can be either continuous or categorical.

If, on the other hand, your dependent variable is a count, see our Poisson regression guide. Alternatively, if you have more than two categories of the dependent variable, see our multinomial logistic regression guide.

Poisson regression is used to predict a dependent variable that consists of "count data" given one or more independent variables. The variable we want to predict is called the dependent variable (or sometimes the response, outcome, target or criterion variable). The variables we are using to predict the value of the dependent variable are called the independent variables (or sometimes the predictor, explanatory or regressor variables). Some examples where Poisson regression could be used are described below:

Multinomial logistic regression (often just called 'multinomial regression') is used to predict a nominal dependent variable given one or more independent variables. It is sometimes considered an extension of binomial logistic regression to allow for a dependent variable with more than two categories. As with other types of regression, multinomial logistic regression can have nominal and/or continuous independent variables and can have interactions between independent variables to predict the dependent variable.

logit vs probit; stata output; log odds vs odds ratio

for Binary Outcome:
• Success(1)/Failure(0)
• Heart Attack(1)/No Heart Attack(0)
• In(1)/Out of the Labor Force(0)

 y* as the underlying latent propensity that y=1 (yes, success, heart attack, in the labor force)
• Example: For the binary variable, (yes/no; heart attack/no heart attack), y* is the propensity for yes (1); a heart attack (1).
• Example 2: For the binary variable (in/out of the labor force), y* is the propensity to be in the labor force (1).

Since y* is unobserved, we use do not know the distribution of the errors, ε
• however, in order to use maximum likelihood estimation (ML), we need to make some assumption about the distribution of the errors.

Thus, the difference between Logistic and Probit models lies in this assumption about the distribution of the errors.

which one to choose? binomial logit or binomial probit?
  • Results tend to be very similar 
  • Preference for one over the other tends to vary by discipline
  • binomial logit is the most frequently used estimation technique for equations with dummy dependent variables
  • binomial probit
  • logistic regression is robust against multivariate normality and therefore better suited for smaller samples than a probit model
  • probit models typically are estimated by applying maximum likelihood techniques
  • probit is based on the cumulative normal distribution
  • probit estimation procedure uses more computer time than does logit
  • since probit is based on the normal distribution, it is quite theoretically appealing (because many economic variables are normally distributed)--however, with extremely large samples, this advantages falls away
Stata output
How to interpret logit?

Sample: BA degree earners
• Dependent Variable: Entry into a STEM occupation  (yes=1, no=0)
• Independent Variable: Parent education-- categorical variable of highest degree: 2-year
degree or lower vs. BA and Advanced Degree

Log odds (b coefficient in stata)
  • When used in logistic regression the log odds tells us how much the odds an outcome occuring increase (or decrease) when there is a unit change in the associated explanatory variable.
  • In logistic regression the b coefficient indicates the increase in the log odds of the outcome for a one unit increase in X. 
  • Interpretation: Among BA earners, having a parent whose highest degree is a BA degree (code 1) versus a 2-yr degree or less (code 0) increases the log odds of entering a STEM job by 0.477.
  • Interpretation: Among BA earners, having a parent whose highest degree is a BA degree (code 1) versus a 2-year degree or less (code 0) increases the log odds by 0.477.

Odds ratio 
  • However,log odds do not provide an intuitively meaningful scale to interpret the change in the outcome variable. Taking the exponent of the log odds allows interpretation of the coefficients in terms of Odds Ratios (OR) which are substantive to interpret
  • we can easily transform log odds into odds ratios by exponentiating the coefficients (b coeffcient= 0.477)--- exp(0.477)=1.61 
  • Interpretation: BA degree earners with a parent whose highest degree is a BA degree (code 1) are 1.61 times more likely to enter into a STEM occupation than those with a parent who have a 2-year degree or less (code 0).

SPSS gives this Odds ration for the explanatory variable labelled as Exp(B).
The odds ratio is labelled as Exp(B) on SPSS

Monday, March 13, 2017

高雄市, 市政統計資料庫

Sunday, March 12, 2017

inferential statistics

Inferential statistics are used to draw inferences about a population from a sample.Consider an experiment in which 10 subjects who performed a task after 24 hours of sleep deprivation scored 12 points lower than 10 subjects who performed after a normal night's sleep. Is the difference real or could it be due to chance? How much larger could the real difference be than the 12 points found in the sample? These are the types of questions answered by inferential statistics

There are two main methods used in inferential statistics: estimation and hypothesis testing. In estimation, the sample is used to estimate a parameter and a confidence interval about the estimate is constructed. 

In the most common use of hypothesis testing, a "straw man" null hypothesis is put forward and it is determined whether the data are strong enough to reject it. For the sleep deprivation study, the null hypothesis would be that sleep deprivation has no effect on performance

Saturday, March 11, 2017

诺不轻信,故人不负我。 诺不轻许,故我不负人。

廖柏森, 英文研究論文


Thursday, March 09, 2017

It takes far less effort to find and move to the society that has what you want than it does to try to reconstruct an existing society to match your standards." - Harry Browne, How I Found Freedom in an Unfree World

Security... it's simply the recognition that changes will take place and the knowledge that you're willing to deal with whatever happens.

You don't have to buy from anyone. You don't have to work at any particular job. You don't have to participate in any given relationship. You can choose.

Wednesday, March 08, 2017

Monday, March 06, 2017


1. 我常研究,怨人是苦海。越怨人,心裡越難過,以致不是生病,就是招禍,不是苦海是什麼?



3. 現今的人,都因為別人看不起自己,就不樂。其實我這個人,好就是好,歹就是歹,管別人看得起看不起呢!只是一個不怨人,就能成佛。現在的精明人,都好算帳,算起來,不是後悔,就是抱屈,哪能不病呢?

4. 「不怨人」三個字,妙到極點啦!(不怨人就是真陽土)


5. 我常研究,火逆的多吐血,氣逆的多吐食。要能行道、明道,氣火就都消了。

6. 上火是「龍吟」,生氣是「虎嘯」,人能降伏住氣火,才能成道。


7. 稟性(怒、恨、怨、惱、煩,又稱氣稟性)用事,鬼來當家。因為生氣、上火一定害病,生病就是被鬼給打倒了!正念一生,神就來;邪念一起,鬼就到。可惜人都不肯當神,甘願做鬼!

8. 火是由心裡生的,人心一動就生火。一著急,火往上升;一動念,火向外散。若能定住心,火自然下降。不守本分的人,額外的貪求,火就妄動。若能把心放下,不替人著急,就不起火,該有多麼輕快!

9. 動性(耍脾氣)是火,心裡生氣,才是氣。佛說「七處心燈」,我說不如掐死一頭。人心一死,道心自生;人心一動,道心自滅,爭貪的念頭就生出來了。因爭生氣,因貪上火,氣火攻心,整天煩惱,就是富貴,也沒樂趣。所以古人治心,如同治病。我說把心掐死,多麼省事!












19. 我所講的「五行」,是以木、火、土、金、水五個字代表來說的,和佛家的五戒、道家的五元、儒家的五常是一樣的。

















































































































數學家孫嘉林, 轉基因

根据孙嘉林先生研究,人是由二个人:一个是生命人,另一个是生物人构成。那生命人是什么呢?生命人就是人的左右大脑和心脏,也就是H20,生物人是人体的轮廓。任何物质都有它的“场”存在状态,电子有粒状态、场状态;人体结构也有粒状态、也有场状态,人体场状态也就是称之为生命人,人体粒状态也就相应称之为生物人。生物和生命是根本不同的两个概念。    脑场健生法是王乐娃女士在孙嘉林先生关于全新的生命科学研究成果的基础上开创的科学健生法,此方法有效地激活人体的免疫系统、增强机体抵抗外界传染性因子的防护能力,清除机体损伤和死亡的细胞,维持自身生理平衡的自身稳定功能,并能对机体监视发现并清除突变细胞的免疫监视功能,让H2O旋转起来,把每个人自身的256个基因密码调动起来,按照自有的程序重新归位,把不正确的分子信号排除体外,生命人健生了,生物人就健康了。脑场健生法唤醒沉睡了几十年的生命人,激活人体的所有细胞,彻底根除人体的索引绝症,人人都能达到更高水平的健生状态,它既是科学中的创举,又是人类格调的创举,此项研究成果的高超性,完全表征了创立者人格的高超性,脑场健生法在大力促进人类真正意义上的健康,并推动和谐世界建设方面具有重要意义。此项成果已申报国家专利(专利号:200910180701.7) 。

这个方法最终达到人体各器官的免疫系统高度平衡,使外来的细菌和病变的细胞在人体内不得复发,如有病变也能排出体外,保证了人体不患病。   脑场健生法的宗旨是:给我10天,送你终生健康。    脑场健生法的好处在哪里:不受时间、地点、环境的影响,当学会这个方法后,终生伴随您,终生不得病,还能延长生命,是人类延年益寿的法宝。    通过什么方法进行排毒呢:通过排毒师对患者30分钟全身细胞激活,而后把负面分子细胞或坏死的细胞,通过旋转从人身的毛孔、淋巴、全息部位排出,同时人体在旋转的过程中,会散发出各种气味,也就是毒素。    如何学习此方法:患者首先在排毒师的引导、指导下进入状态,通过本身的意识来学习此方法(人的意识一旦主动用起来,力量是无比巨大的),通过意识激发人本身的能量,让生命人、生物人旋转起来。只要有正常意识的人,都能学会这个方法。通过10天的学习与体验,我们教会你这个方法,你就能每天自己“健生”了。    脑场健生法wlw—癌炁健生法根除癌症区别于其他任何方法,显示出六大特点:    1、该健生法不是疗法、药法,不是各气功法,也不是心理学的精神作用方法,而是最简单、普通的意识方法。该健生意识法适用于任何正常意识尚能保持的癌患者。    2、该健生法有特殊的简捷性,即简明又快愈,该方法的第一实施人——王乐娃女士用手触及患者头部并进行意识交流,30分钟后即能启动患者之“炁”。如此,每天只需一次。一般情况是一个月,即可癌变逆转。    3、该健生法适用于除了神经不正常和白血病的任何癌症患者,这是因为“健生”是人类最基本的功能。    4、该健生法无任何毒副作用,这是因为“健生”只能有益无害。    5、该健生法主治癌症,患者痊愈之后,已知和未知的其它病症(糖尿病,基至是尿毒症„„)亦同时被逆转。    6、该健生法对于任何人都能实现健生的状态并能延寿,当之无愧是人体终生的法宝。   每天三次 一般情况是一个月,即可癌变逆转。第一实施人——王乐娃女士用手触及患者头部并进行意识交流„„   “健生”(西医的“治疗真空”)人类经历了几千年的生物学和医药学,研究对象主要是生物人,而非生命人,开发新的治疗空间。    总之,学会了这个方法,能使人达到一个高度免疫、高度自稳、高度监视状态,人一旦能达到这三个高度,也就告别了疾病,从此不得癌症。


氧化主宰性 ~ 对应自主性,
1、 生命科学
生命只能是起源于两种对立的性质,不可能仅源于存在物的一般性质。否则,“起源”也就失去了它本身意义。所谓起源是由无到有,生命是非生物,没有生物独有的关系存在物,它必须是由两种相反性质共同创生的,不能创生于某单一性质。有的生命起源专家认为地球生物是“C”(碳)生物,地外生物是“N”(氮)生物,这些猜想既违反理性也与事实不符。有人认为生命起源于宇宙大爆炸之宇宙起源,这种观念更加悖理,这是因为:宇宙不可能有起源,生命是宇宙中的存在物,是一种特殊的关系存在物,人类及其意识均是宇宙之中的存在物,凡是存在物必有其起源。我们只能是认识宇宙之中的存在物,认识其起源,不可能认识宇宙本身,否则“认识”也就违反它的本身意义,无所谓“认识”。任何认识均有其“已知”和“未知”这两含义。若无已知,则不能认识;若无未知,则无可认识。变未知为已知必须以已知出发才有认识过程和结果,增强智能。全知全能的上帝、佛陀、神圣均基于同一论,均源自于“非已”,均因否定其本身而不成立,均因对立同一之矛盾而违反理性,与科学不符。同一论因违反理性而使生命起源于氧化主宰性和对应自主性之认识得以明晰,这是因为宗教信仰之主宰性是基于利益。生命特性中的氧化主宰性虽然 违反理性,但是该性质却成为生物独有,非生物没有的利生因素。氧化主宰性以违反理性(假)的意义与对应自主性(真)的意义相对立,两方对立而一致结果创造了生物,使生命起源了。当代天文学、地球化学、生物化学、分子生物学……为生命起源于H2O之结论提供了明确的科学证据:地球之外的氢元素,地壳内的氢和氧两元素,生物体中的H2O成分均有高丰度,证据非常充分。
生物不同于非生物,也不同于生命。生物是生命关系的对象双方,生命是对象双方生物之间的关系,更进一步讲生命是氧化主宰性生物与对应自主性生物之间的关系。生物是特殊的对象存在物,生命是特殊的关系存在物。在生物、生命学研究中,生物呈现为粒状态,生命呈现出为场状态。生物和生命两概念的含混意义,导致药物之粒状态和场状态这两者的意义混同,也导致了诸病之根茎“基因组”至今仍然错误定义为单一序列。因此,诸多绝症无法根治,人的寿命研究也是不得要领。其根本原因在于基因组对象意义与生理组关系意义均未明确,这两组必须是并行共存,缺一不可。生理组之生命意义比基因组更基本、更深远,主导性更强。在生理组中人脑场状态具有至高无上,主导全身之功能。人体生命场状态可以表达为心 ~ 脑,其中符号“~”表示生命关系。生命关系决定了人体场状态。人脑场状态可以表达为左脑~右脑,其中符号“~”表示左右脑之间的意识关系,人的意识由此而生。
O ~ H2
医学中的医理和药理是两大研究内容,人体生理为其基础。自顺势疗法为始开创了现代科学化的人体场状态疗法,人人都期盼心 ~ 脑场状态健生法能更上一层楼。




脑场指导音乐 - 华文版 (2013最新版)



bank statement

  • ask ur employer to direct deposit ur paycheck into ur bank account
  • check with ur bank to see if ur account has free bounced-check protection or courtesy overdraft protection, if bank say yes, u need to tell bank that u want to opt out of this coverage and have bank send u a written confirmation
  • u have to pay an annual fee of $ 10 to $ 50 to have the bank take money out of ur saving account (or off ur credit card) when u are about to bounce a check
  • with the bounced-check protection, the bank coughs up their own money to cover ur checks, for this bit of financial chivalry, u get hit with a fee that is typically $ 25 per check
  • there also can be additional charge of about $ 3 per day, until u pay back the bank for the amount they covered
  • to look for a better deal, shop around with banks and credit unions; u can join a credit union simply by knowing someone who is a member, credit unions are nonprofits and thus tend to offer low interest rates and fees on their financial products,

home phone line, cell phone

boost ur minutes on cell phone
get rid of home line

insurance deductible

  • u should raise ur insurance deductible
  • the higher ur deductible (what u pay before the insurer covers the cost of a loss, 自付額), the lower ur annual premium 
  • if u to for the low deductible and end up making claims, ur insurer will either raise ur premium or cancel ur policy outright, thus, ur low deductible will end up costing u more in the long run
  • u should boost $ 250 or $ 500 deductible to  $ 1000
  • if u do need to make a claim, and u don't have the cash to cover the deductible then u will use ur low-rate credit card 

tax refund

  • stop getting a tax refund --- don't have too much withheld from each paycheck and later to fill out ur tax returns
  • change ur withholding so less money is subtracted from ur paycheck
  • contact ur HR on ur W-4 form; the more exemptions u claim, the less money will be withheld from each paycheck
  • the less money is withheld, the more u will have to pay ur bills on time each month

home equity loan, home equity line of credit, home loan

  • home equity loan (HEL) -- interest rate is fixed, better, lower risk
  • home equity line of credit (HELOC) -- interest rate is adjustable, rate rise and fall along with the general interest rate in the economy, (interest rate may rise), thus greater risk; the only time to use a HELOC is if u think rates will remain stable or decline during ur payback period
  • don't use HELOC, just keep HELOC handy for emergency
  • don't use these home loan to pay off credit card debt

student loan

  • you can have all sorts of other debt (such as credit card debt, auto loans) "dismissed" through bankruptcy, but ur student loans stick with u
  • unless u get a court to let u off as a hardship case, which typically requires permanent disability, ur student loan debt will stay with u until u die 

mortgage, loan

  • keep ur credit card bills super-low in the months before u apply for a mortgage
  • cut off ur credit spending for the two months before u apply for a mortgage; do everything u can to minimize ur charges
  • for two months before applying for a major loan, try to keep ur credit card spending to an absolute minimum

Sunday, March 05, 2017

multiple regression, class, lecture, 2017

1.theoretical model→hypothesis (relationship between variables)
2.use evidence (data) to test hypothesis → test theoretical model
3.use econometrics in testing hypotheses

  • regression analysis (result), no matter how statistically significant, can't prove causality. Regression analysis can only test whether a significant quantitative relationship exists, it can only test the strength and direction of the quantitative relationship involved
  • Y= a + b X; a (constant, intercept), b is coefficient; b is slope coefficient
  • Linear regresions need to be linear in the coefficients, do not necessarily need to be linear in the variables. Linear regresson analysis can be applied to an equation that is nonlinear in the variables if the equation can be formulated in a way that is linear in the coefficients. When econometricians use the phrase "linear regression", they usually mean "regressin that is linear in the coefficients"
  1. linear in variables -- an equation is linear in the variables if plotting the function in terms of X and Y generates a straight line --- Y= a + b X (is linear in the variables);  however,  Y = a + b Xis not linear in the variables
  2. linear in coefficients -- if linear regression techniques are to be applied to an equation, that equation must be linear in coefficients; An equation is linear in coefficients only if the coefficients (b) appear in the simplest form: that is, coefficients are not raised to any powers, (other than one), are not multiplied or divided by other coefficients, don't themselves include functions (e.g., log or exponents)--- Y= a + b X (is linear in the coefficients); however, Y= a + Xb (is not linear in the coefficients, a, b); Of all possible equation for a single explanatory variable (X), only functions of the general form f (Y)= a + b f (X) are linear in the coefficients a and b 
Stochastic Error Term ε
  • some variation in Y can't be explained by the model. This variation probably comes from sources such as omitted influences, measurement errors, incorrect functional form, random and unpredictable occurrences
  • unexplained variation (error) --- expressed through a stochastic (or random) error term
  • a stochastic error term is a term that is added to a regression equation to introduce all the variation in Y that can't be explained by the included Xs
  • error term is the difference between the observed Y and the true regression equation (the expected value of Y)
  • error term is a theoretical concept that can never be observed
  • 座標上某點A, 與 true regression line (can't be observed) 間之距離, 稱為 error term (can't be observed)
  • see below for picture
Y= a + b X1+ c X2 +d X3 + ε
  • b (regression coefficient): the impact of one unit increase in X1 on the dependable variable Y, holding constant the (influence of) other independent variables (X2, X3) -- isolate the impact on Y of a change in one variable from the impact on Y of changes in the other variables
  • if a variable is not included in an equation, then its impact is NOT held constant in the estimation of the regression coefficients
  • Time series --- data consists of a series of years or months ----                                                  Y= a + b X1t+ c X2t +d X3t + ε  (t = 1,2,3.....n), t is used to denote time
Residual (e)
  • Theoretical regression equation (purely abstract): Y = a + bX1+ ε, We can't actually observe the values of the true regression coefficients.      
  • Estimated regression equation: Y' = 105 + 12 X1, We calculate estimates of these coefficients from the observed data. 105,12 are estimated regression coefficients, they are obtained from sample data and are empirical best guess for the true regression coefficients (a,b)
  • the closer Y' is to Y, the better the fit of the equation
  • the difference between the estimated value of Y' and the actual value of Y is defined as "residual (e)" 
  • residual is a real-world value that is calculated for each observation every time a regression is run
  • the smaller the residuals, the better the fit, the closer the Y' will be to the Y
  • 座標上某點A, 與 estimated regression line (can be observed, estimated regression line 與true regression line 不同) 間之距離, 稱為 residual (can be observed and calculated/measured)
  • 就某個角度而言, residual can be thought of as an estimate of the error term (can't be observed)
  • difference between error term and residual (picture)

Ordinary Least Square
  • the purpose of regression analysis is to take a purely theoretical equation, Y= a + bX+ ε, and use a set of data to create an estimated equation, Y'= a'+b'X
  • OLS is a regression estimation technique that calculates a', b', so as to to minimize the sum of the squared residuals--- OLS minimizes Σ (Y-Y') 2
  • OLS is the simplest of all econometric estimation techniques
  • Most other techniques involves complicated nonlinear formulas or iterative procedures, many of which are extensions of OLS itself
Decomposition of variance -- decomposition of the variance in Y

Saturday, March 04, 2017

原始點實際案例 >> 按患處或病名查詢

Friday, March 03, 2017

“There are a lot of pressures in this career, there's a lot going on. It's really important to not lose yourself. Stay true to who you are and do what is most important and of greatest interest to you.” – Catherine Woolley, PhD

Thursday, March 02, 2017

Windows 10 PRO的XP模式工具

之前版本微軟在Windows 7系統中提供了一個名為Windows XP Mode的工具,

使用者可以在Windows 7系統中虛擬出一個Windows XP環境,確實幫助到企業解決舊版本軟體相容性問題

How to add an XP Mode Virtual Machine to Windows 10 (or 8) using Hyper-V





Wednesday, March 01, 2017

percent of sales, 營收比例

若你是因為某項特定產品, 而對一家公司感興趣, 則你須要知道這項產品對該公司是否重要?
這項產品佔該公司的營收比例 (percent of sales)有多少?

產品A能讓甲公司的股價大幅上升, 此乃因為甲公司的規模較小

對大型的B公司而言 (其有很多不同產品,), 產品A的重要性相對就沒有那麼大---對B公司的股東來說, 產品A的重要性就不大, 故你的選項有二: (1) 找尋生產類似產品的公司, 或 (2)忘了這項產品,
 If you walk into a situation thinking, "I hope I don't lose," you'll perform worse than if you think, "I'm here to win."

Take a deep breath and tell yourself, "I'm going to do well." That slight change in your thought process will increase your chance of success.

Visualize success.---Olympic gold medalist Lindsey Vonn said, "By the time I get to the start gate, I've run that race 100 times already in my head, picturing how I'll take the turns." Mental imagery has a profound effect on the way your body behaves.

Studies consistently show that no matter your skill level, visualizing yourself going through the motions will help you do better. Whether you're about to ask for a raise or give an important presentation, imagine yourself going through the motions. Thinking about each step in the process can help you perform at your peak.

Use positive self-talk.
When asked about his decision to leave the Cleveland Cavaliers to play for the Miami Heat, LeBron James told reporters, "I wanted to do what's best for LeBron James and do what makes LeBron James happy."

Initially, social media buzzed with teasing about James referring to himself in the third person. Although some suspected he was losing touch with reality, the truth is that talking to himself by name was likely part of his key to success. Studies have found that talking to yourself by name in this way reduces anxiety and helps you make better decisions. So rather than saying, "I can do this," call yourself by name. As strange as it sounds, it may help you regulate your emotions so you can focus your energy on the task at hand.

The first step to improving your game—whatever your game might be—is to think like a champion.

Tuesday, February 28, 2017

因為在任何時間, 若你發現你付不出汽車貸款, 你可以把車子賣掉
在你在任何新車駛離賣場時, 那輛車子的價值馬上掉了至了20%以上

international shipping

home equity line of credit, 房屋淨值信用貸款

  • 房屋淨值是房屋市價扣除所欠貸款後的剩餘價值; 若房屋市價在你購買後上漲, 房屋的淨值也會跟著增加; 你可以向銀行申請房屋淨值信用貸款, 以這筆錢作為應急之用

sell short

most people buy a stock at 10 and sell it as 25, they buy and sell to make a profit

selling short reverses the process by which the profit is made. You sell the stock at 25 and then buy it at 10. How do u sell it if u don't first have it? You borrow the stock from sb. Eg., u go JP Morgan and borrow 100 shares of stock and sell the stock at 25, which is its current price. Then sell it because u think it will be going down in value. So when it goes to 10, u buy 100 shares and give them to JP Morgan. The bank has its 100 shares back. U have ur profit.

before asking how much u are going to get paid for a job, first decide whether it is the right job, whether it is the right place for u, because if it is the right place and u do the job right, the money will come, the money will find u

Monday, February 27, 2017

dynamic structural equation modeling (DSEM) for intensive longitudinal data using multilevel time series analysis in Mplus Version 8.
A Short History of Nearly Everything
by Bill Bryson

Sunday, February 26, 2017

The purpose of life is to live it, to taste experience to the utmost, to reach out eagerly and without fear for newer and richer experience.

This area in Detroit is now America’s first 100% organic, self-sustainable neighborhood

Saturday, February 25, 2017

Announcing ggraph: A grammar of graphics for relational data

Friday, February 24, 2017


CyberLink Media Suite 10(Power2Go & MediaShow)

cyberlink  power2go

Thursday, February 23, 2017

representative gov


whole list


首頁 > 業務項目 > 人事統計

宜蘭縣政府, 主計處, 首頁 性別統計專區性別統計圖像



survey 4 areas

北部7: 臺北市新北市、宜蘭縣桃園市新竹縣基隆市新竹市
中部5: 臺中市苗栗縣彰化縣南投縣雲林縣
南部6: 高雄市臺南市嘉義縣屏東縣澎湖縣嘉義市
東部2: 臺東縣花蓮縣













Wednesday, February 22, 2017



85902906 ms. wang for survey
性別工作平等申訴案件件數-按地區分, march 1, 2017 will be updated online
85902901 ms. chen

資料整理與檢核之實務_ By Using R


R Data Input and Output



Tuesday, February 21, 2017

data, 中央研究院



1.      會員類別



(1)       國內外公私立研究機構之專任研究人員

(2)       國內外公私立大專院校之專兼任教師

(3)       政府機構之專任研究人員

(4)       捐贈或授權資料予「學術調查研究資料庫」之個人或單位代表


(1)       國內外公私立大專院校大學部學生、碩博士班研究生

(2)       國內外公私立研究機構研究助理人員

(3)       國內外公私立大專院校研究助理人員

(4)       政府機構研究助理人員


1.3 院內會員


1.4 網路會員












(1)   依原資料提供者之要求;

(2)   基於法律之規定;

(3)   為保障本資料庫之財產及權益;

(4)   在緊急情況下為維護其他會員或第三人之權益。


















(1)    張貼任何內容具詐欺、毀謗、侮辱他人或違反法律之文字、圖片或任何形式之檔案。

(2)    散布任何具有侵害、破壞、中止、干擾或毀損電腦、系統、程式、軟體或類似物件之病毒軟體、程式、檔案等類似載體。

(3)    侵犯他人著作權、商標權、專利權、營業祕密或其他具智慧財產權之圖片、文字、圖形、言論、軟體、檔案或程式。

(4)    從事任何廣告或商業交易。

(5)    違法或未經授權擅自進入其他會員之帳號或本資料庫未開放使用之空間。




6.7 會員須遵守研究倫理及個人資料保護法等相關法規。



6.10會員利用本資料庫資料撰成之論著,必須於謝詞(acknowledgement),及參考文獻(References)處詳細書明資料出處(請見附件之範例) 。










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passive representation

examines whether the composition of the bureau- cracy mirrors the demographic composition of the general pop- ulation or whether women and minorities are under- represented in government agencies (Brown 1999; Dolan 2000, 2002; Esman 1999; Kelly and Newman 2001; Naff and Crum 2000; Riccucci and Saidel 2001).

Brown, Deryck R. 1999. Ethnic Politics and Public Sector Management in Trinidad and Guyana. Public Administration and Development 19(4): 367-79.

Dolan, Julie. 2000. The Senior Executive Service: Gender, Attitudes and Representative Bureaucracy. Journal of Public Administration Research and Theory 10(3): 513-29. - .

2002. The Budget-Minimizing Bureaucrat? Empirical Evidence from the Senior Executive Service. Public Administration Review 62(1): 42

Esman, Milton J. 1999. Public Administration and Conflict Management in Plural Societies: The Case for Representative Bureaucracy. Public Administration and Development 19(4): 353-

Kelly, Rita Mae, and Meredith Newman. 2001. The Gendered Bureaucracy: Agency Mission, Equality of Opportunity, and Representative Bureaucracies. Women and Politics 22(3): 1

Naff, Katherine C., and John Crum. 2000. The President and Representative Bureaucracy: Rhetoric and Reality. Public Administration Review 60(3): 98-109.

Riccucci, Norma M., and Judith R. Saidel. 1997. The Representativeness of State-Level Bureaucratic Leaders. PublicAdministration Review 57(5): 423