Stata

According to World Health Organization’s report upon household air pollution and health (2018), it has been estimated that globally around 3 billion of population uses kerosene and solid fuels (i.e. cow dung, charcoal, wood, coal and crop waste) for cooking food in ineffectual stoves and in open fires. However, the majority of the population lives in rural areas and has low income. As the process of such type of cooking is inefficient which results in producing high level of toxic compounds and gases within the surroundings of household, these gases then directly penetrates into the lungs causing health diseases. Such gases and compound fall into household air pollution or indoor air pollution categories.
This report examined the impact of household air pollution upon the health of household members and the minimizing this impact through usage of efficient cookstoves (i.e. Jaan Pakistan Supreme Stove). The survey was conducted upon the respondents residing in some of the off-grid districts of Pakistan by asking questions regarding their own understandings of danger of household air pollution and feasibility of using efficient biomass fuel combustion technology and its impact upon their lives.

Co-authoring Dofiles can be challenging as most Stata users have idiosyncratic preferences and methods for organizing and writing Dofiles. Which standards and practices can research teams adopt to improve the cohesion of this group work? This article proposes some best practices to overcome team research coordination issues adapting methods from software engineering and data science along with personal experience with group research. We prioritize improvements that increase efficiency of the team workflow by establishing global parameters and directories, standardizing communication between team members, and enabling reproducibility of results.

Savings is known as a major factor in the economic development (Najafi, Ghorbani, 1998, p.48). Economists have always realized the importance of savings in the economic development (Komayjani, Rahmani, 1993, p.3). Each country needs investment to achieve economic growth; the necessary condition for investment is savings. (Mojtahed and Karami, 2003, p. 3). One of the problems in the economy of Iran and developing countries is the low rate of savings, and so the weak growth of investment. Thus, capital is considered as the main scarce factor in these countries. Hence, capital accumulation for economic growth and development of these countries is of great importance (Permeh, 2004,p.170). The purpose of this study, is to study the effect of growth rate of real GDP per capita on the savings rate in twelve (12) developing countries, (Islamic Republic of Iran, Botswana, the Dominican Republic, Zimbabwe, Niger, Burundi, Burkina Faso, Central African Republic, Rwanda, Togo, Nepal, Benin) and seven (7) developed countries (United States of America, Iceland, Luxembourg, Norway, Singapore, Greece, United kingdom). Data analysis as panel data with annual data from 1965 to 2010 is expressed in eviews7 and stata11 software. The results show the positive effects of the lags saving rate and the growth rate of real per capita GDP on the savings rate. The results are statistically significant. Also,The results show that the structure between the two groups (developed and developing countries) is different. Effectiveness rate and the growth rate of real GDP per capita on the savings rate in developed countries is lower than in developing countries.

Free cash flow has been subject of discussions in the last few decades. Central to these discussions is how best free cash flows should be utilized. This paper examines the link between free cash flow and debt; on capital expenditure. An unbalanced panel data was obtained from audited annual financial statements of twenty-six companies listed in the Malta Stock Exchange. The data covered an eight-year period, from 2012 to 2019, and was analysed using STATA14 and Microsoft Excel. F-test was initially conducted to choose between random effect and fixed effect models but result of the estimates was found unreliable, hence Hausman test was employed. Other diagnostic tests, including Durbin-Watson and Breusch-Pagan, were equally conducted. Our results showed a significantly negative association between the free cash flow – capital expenditure dyad, and a significantly positive association between debt and capital expenditure. These suggest that firms rely more on debt to finance capital ...

Before implementing any multivariate statistical analysis based on empirical covariance matrices, it is important to check whether outliers are present because their existence could induce significant biases. In this article, we present the minimum covariance determinant estimator, which is commonly used in robust statistics to estimate location parameters and multivariate scales. These estimators can be used to robustify Mahalanobis distances and to identify outliers. Verardi and Croux (1999, Stata Journal 9: 439–453; 2010, Stata Journal 10: 313) programmed this estimator in Stata and made it available with the mcd command. The implemented algorithm is relatively fast and, as we show in the simulation example section, outperforms the methods already available in Stata, such as the Hadi method.

Corner solution responses are frequently observed in the social sciences. One common approach to model phenomena that give rise to corner solution responses is to use the tobit model. If the decision to participate in the market is decoupled from the consumption amount decision, then the tobit model is inappropriate. In these cases, the double-hurdle model presented in Cragg (1971, Econometrica 39: 829–844) is an appropriate alternative to the tobit model. In this article, I introduce a command, dblhurdle, that fits the double-hurdle model. The implementation allows the errors of the participation decision and the amount decision to be correlated. The capabilities of predict after dblhurdle are also discussed.

A key issue in the estimation of production functions is the correlation between unobservable productivity shocks and input levels. Profit-maximizing firms respond to positive productivity shocks by expanding output, which requires additional inputs. Negative shocks lead firms to pare back output, decreasing their input usage. Olley and Pakes (1996) develop an estimator that uses investment as a proxy for these unobservable shocks. More recently, Levinsohn and Petrin (2003a) introduce an estimator that uses intermediate inputs as proxies, arguing that intermediates may respond more smoothly to productivity shocks. This paper reviews Levinsohn and Petrin's approach and introduces a Stata command that implements it.

This is a STATA Homework Sample document that has been solved by Programming Assignment team who have trained professionally. For any programming language assignments, feel free to contact them on [email protected] or visit our website on https://www.statisticshomeworkhelper.com/stata-assignment/

Yazar, İstanbul Üniversitesi SBE İktisat Anabilim Dalında Doktorasını 11.05.2016 tarihinde tamamlamıştır. Bu belge ZSA yaparken izlenecek yol ve yöntemleri kısaca tanımlar. Analizlerin nasıl yapılacağı ve yorumlanacağına dair detaylı bilgi içermez. Bir başka deyişle, ZSA için sadece bir pusuladır. Not: Eğer bu dökümandaki metodolojiyi izleyerek bir çalışma yaparsanız yazara atıfta bulunabilirsiniz.

We present a new Stata program, vselect, that helps users perform variable selection after performing a linear regression. Options for stepwise meth- ods such as forward selection and backward elimination are provided. The user may specify Mallows’s Cp, Akaike’s information criterion, Akaike’s corrected informa- tion criterion, Bayesian information criterion, or R2 adjusted as the information criterion for the selection. When

ABSTRACT The purpose of this study is to discover the influence of the internet on student accomplishment at Pertamina University in 2016 with the use of Grade Point Average (GPA) as an indicator. The methods that I use are qualitative and quantitative. The data collection technique is purposive sampling by taking 98 samples from 1200 populations. Data analysis is OLS multiple linear regression with STATA.14 software.

We present a new Stata program, vselect, that helps users perform variable selection after performing a linear regression. Options for stepwise meth- ods such as forward selection and backward elimination are provided. The user may specify Mallows’s Cp, Akaike’s information criterion, Akaike’s corrected informa- tion criterion, Bayesian information criterion, or R2 adjusted as the information criterion for the selection. When the user specifies the best subset option, the leaps-and-bounds algorithm (Furnival and Wilson, Technometrics 16: 499–511) is used to determine the best subsets of each predictor size. All the previously men- tioned information criteria are reported for each of these subsets. We also provide options for doing variable selection only on certain predictors (as in [R] nestreg) and support for weighted linear regression. All options are demonstrated on real datasets with varying numbers of predictors.

*Level 5 (UG 2nd year) Module: Statistics for Political Science II Mark: 70 (First).*

This research paper will analyse the effect income has on people’s perception about happiness and turnout. The logic underpins my causal inferences are respectively that materialistic welling can be emotionally felt and create greater incentives for people to vote at elections to further preserve their income or wealth. Thus, I expect positive correlations between the Independent Variable (IV) i.e. households total net income (hinctnta) and both of the Dependent Variables (DV), i.e. the level of happiness (happy) and turnout at the most recent national election (vote). A number of variables that might carry confounding effects are controlled for in this research to minimise the possibility of a spurious relationship between the IV and the DV, they are: age, gender, highest level of education (ES – ISCED), political Left-right placement, religiosity. An interaction term (IV) – engagement with paid work over the past 7 days is added to moderate the impact income has upon happiness (H1).

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about this book

http://penerbitsalemba.com/v3/book-display.php?id=897
Statistika Kesehatan: Aplikasi Stata dan SPSS
Penulis : Najmah

Tahun : 2017

Kode Buku : 08-0268

Jumlah Halaman : 214

Imprint : Salemba Medika

Dimensi : 19 cm x 26 cm x 0 cm

Harga : Rp 87.900,- Rp 70.320,-

Stok : 324

Berat : 0.3 Kg

Editor DR BESRAL, SKM, M.Si

INFO tentang buku, slides dan data bisa didownload disini ya
http://penerbitsalemba.com/v3/book-display.php?id=897

Free chapters disini ya
https://aut.academia.edu/NUsman


BAB 1 PENGANTAR STATA 1
BAB 2 ANALISIS DESKRIPTIF PADA STATA 25
BAB 3 APLIKASI STATA PADA PENGHITUNGAN EPIDEMIOLOGI (OR/RR)33
BAB 4 REGRESI LOGISTIK SEDERHANA DAN BERGANDA 43
BAB 5 REGRESI COX (BIVARIAT-MULTIVARIAT) 53
BAB 6 PENGANTAR SPSS67
BAB 7ANALISIS DESKRIPTIF PADA SPSS 85
BAB 8 UJI VALIDITAS DAN RELIABILITAS INSTRUMEN 107
BAB 9 ANALISIS DATA SURVEI (DATA SEKUNDER) PADA SPSS 117
BAB 10 REGRESI LOGISTIK SEDERHANA & GANDA DENGAN SPSS 133
BAB 11 REGRESI LINEAR SEDERHANA & GANDA (SPSS & STATA) 141
BAB 12 UJI CHI-SQUARE STATA & SPSS 153
BAB 13 UJI INDEPENDENT STUDENT T-TEST Stata & SPSS 169
BAB 14 UJI ANOVA STATA & SPSS 177
BAB 15 PAIRED T-TEST 183

SPECIAL THANKS:

Editor Ahli, Dr Besral, SKM, M.Sc

PENERBIT: SALEMBA MEDIKA


Tim Rempong
Teman-teman yang telah membantu proses pengeditan buku ini, Yeni, S.K.M., M.K.M., Desy Indah Permatasari, S.K.M., Adelina Fitri, S.K.M., Nur Hidayah S.K.M., Eka Mujiati,S.K.M., dan teman-teman FKM Unsri lainnya yang tidak bisa disebutkan satu persatu.


http://najmah-buku.blogspot.co.nz/2017/08/ditunggu-ya-di-gramedia-terdekat.html

We present a new Stata command, mmp, that generates marginal model plots (Cook and Weisberg, 1997, Journal of the American Statistical Association 92: 490–499) for a regression model. These plots allow for the comparison of the fitted model with a nonparametric or semiparametric model fit. The user may precisely specify how the alternative fit is computed. Demonstrations are given for logistic and linear regressions, using the lowess smoother to generate the alternate fit. Guidelines for the use of mmp under different models (through glm and other commands) and different smoothers (such as lpoly) are also presented. Copyright 2010 by StataCorp LP.

We present a new Stata command, irp, that generates the inverse response plot (Cook and Weisberg, 1994, Biometrika 81: 731–737) of a response on its predictors. Using the inverse response plot, an appropriate scaled power transformation for the positive response variable can be found so that the trans- formed response mean is linear in the predictors. The optimal transformation is

Explains the basics of analysing data in Stata:
- main descriptive statistics, 
- recoding variables.
- basic bivariate tests,
- OLS regression

It was written for an introductory course on statistics.
All examples based on the European Social Survey data.
You can download the data
www.europeansocialsurvey.org
You will  need to run the 'rename' do-file
http://www.europeansocialsurvey.org/data/useful_hints.html to get the data in the same format as the dataset used in the examples in the guide.

The joint modelling of longitudinal and survival data has received remarkable attention in the methodological literature over the past decade; however, the availability of software to implement the methods lags behind. The most common form of joint model assumes that the association between the survival and longitudinal processes are underlined by shared random effects. As a result, computationally intensive numerical integration techniques such as adaptive Gauss-Hermite quadrature are required to evaluate the likelihood. We describe a new user written command, stjm, which allows the user to jointly model a continuous longitudinal response and the time to an event of interest. We assume a linear mixed effects model for the longitudinal submodel, allowing flexibility through the use of fixed and/or random fractional polynomials of time. Four choices are available for the survival submodel; namely the exponential, Weibull or Gompertz proportional hazard models, and the flexible parametric model (stpm2). Flexible parametric models are fitted on the log cumulative hazard scale which has direct computational benefits as it avoids the use of numerical integration to evaluate the cumulative hazard. We describe the features of stjm through application to a dataset investigating the effect of serum bilirubin level on time to death from any cause, in 312 patients with primary biliary cirrhosis.

With the release of Stata 14 came the mestreg command to fit multilevel mixed effects parametric survival models, assuming normally distributed random effects, estimated with maximum likelihood utilising Gaussian quadrature. In this article, I present the user written stmixed command, which serves as both an alternative and a complimentary program for the fitting of multilevel parametric survival models, to mestreg. The key extensions include incorporation of the flexible para-metric Royston-Parmar survival model, and the ability to fit multilevel relative survival models. The methods are illustrated with a commonly used dataset of patients with kidney disease suffering recurrent infections, and a simulated example, illustrating a simple approach to simulating clustered survival data using survsim (Crowther and Lambert (2012, 2013)).