#Multinomial logit model

[olahdatasemarang]

ECM algorithm for the multinomial logit model Use emlogit With (In) R Software

ECM algorithm for the multinomial logit model Use emlogit With (In) R Software ristek.link/emlogit

[cminic]

12.8.24 study

So. Um. I told myself I would start really studying earlier for finals, but, I guess at least I’m studying?

Notes to self:

In stats, reviewed logit functions, understanding slope of logit function…

You got this! (To myself and for anyone needing motivation…)

To be reviewed:

STAT

Chi-square

ANOVA (need to know how to calculate each variable in the outputting table)

KNN

Bonferroni, Tukey

R^2 vs adjusted R^2

The various F tests

Multinomial models

DS

hypothesis testing (is in both exams!)

A/B testing

Kmeans

SVD and PCA (understand each of the factors, matrix multiplication)

SQL in Python

Map Reduce

Days until statistics final: 2

Days until data science final: 4

(Remember to take biology exams asap.)

[dayintheproductivelife]

#25 Food for Thought: Child Labour

My chocolate says it’s free of slave labour. What does that even mean? They say that no children were involved in the production of the cacao beans, but if that’s true, what are the children doing instead? Going to school? 

You might take it as an irrefutable fact that child labour is bad. And, if you’ve spent more then 13 seconds thinking about it, you probably also believe that child labour is purely a result of poverty. At least, I used to believe these things.

Don’t get me wrong, child labour is associated with lower schooling outcomes, and then there is a huge range of activities genuinely dangerous to children (nobody really thinks it’s a good idea to have a kid hack down weeds with a machete).

But working on a (household) farm (which is what 71% of all child labourers do) has benefits, too. It’s a form of on-the-job training and socialisation. Take cacao farming in the Ivory Coast, where 40% of global cacao comes from. Here, most on-farm child labourers are the biological sons of the cacao farmers. Each year, they engage in more tasks on the farm until, by the age of 15, they know the whole range of farming activities. Why biological sons? Because cacao farming is considered a male activity, and because inheritance laws favour male offspring. The experience these boys gain helps them to increase their incomes later in life. 

What happens if we forbid child labour on the cacao farm? It depends. If there is no money for schooling, the child still won’t go to school. Since wages of older children often support the education of their younger siblings, some children may have to drop out of school. The child may also work off-farm, say as a domestic servant - which my chocolate bar doesn’t much care about. But returns to education in domestic services is even lower than in farming, which makes it even less profitable for the child to go to school.

So, here’s the take-home message. Nothing is ever black and white. In an ideal world, children would go to quality schools while their parents work in jobs that pay them a living wage and allow them to feed their children and keep them healthy so they can actually learn. We don’t live in such a perfect world, and so we need to be aware of trade-offs - and not blindly believe every chocolate bar that wants us to feel like we’re good people because we’re spending more of our excess income. 

Sources:

International Labour Organization (2017). Global estimates of child labour: Results and trends, 2012-2016. Geneva, International Labour Office.

Nkamleu, G. B. and A. Kielland (2006). Modeling farmers' decisions on child labor and schooling in the cocoa sector: a multinomial logit analysis in Côte d'Ivoire. Agricultural Economics 35(3): 319-333.

Webbink, E., et al. (2013). Household and context determinants of child labor in 221 districts of 18 developing countries. Social indicators research 110(2): 819-836.

[ladynan27]

Nancy's Data Academy Blog

Data Sciences Academy:  Data Management & Visualization

 

Course Data Set Used:  GapMinder

 

Variables to be studied: 

·       Employment & Alcohol Consumption

·       Income & Alcohol Consumption

 

Questions to be asked:

·       Is there a relationship between Employment & Alcohol Consumption? 

·       Is there a relationship between Income Level & Alcohol Consumption? 

 

Hypothesis:  (Scan down to bottom)

 

Research:

 

Employment & Alcohol Consumption

 

1.        Although most of these authors acknowledge the possibility of reverse causality (i.e., unemployment affecting alcohol use), few have rigorously examined the issue. Most early studies on this topic find that alcohol use decreases when the unemployment rate increases. Brenner (1979) uses aggregate data to show that, in the long term, alcohol consumption per capita increases with personal income even though, in the short term, alcohol use increases shortly after recessions. Ruhm (1995), using fixed-effects estimation with state-level panel data from 1975 to 1988, finds that per-capita alcohol consumption is pro-cyclical. He argues that the income effect offsets any increases in alcohol use that may be caused by the emotional stress of experiencing financial difficulties.

 

2.       Numerous studies have been conducted in different countries which have focused on relationship between job loss and use of alcohol at an individual level. The findings have not been consistent and each of the following conclusions have been supported: (1) unemployment increases alcohol use and abuse; (2) unemployment reduces alcohol use and abuse; (3) unemployment does not alter drinking behavior. The fourth finding is that unemployment has all the above listed consequences, i.e., some drink more, some drink less, and some individuals do not alter their drinking habits following job loss (Crawford et al. 1987; see also Hammer 1992; Janlert & Hammarstrom 1992; Warr 1987).  The contradictory results obtained in both longitudinal and cross-sectional studies may be due to various factors. The target populations and the selection of variables differ from one study to another. There may also be various mediating factors which affect the relationship between unemployment and drinking habits. It is plausible that under particular conditions some individuals increase alcohol use following job loss, but this is not the general pattern (Lahelma 1993). The conclusion which has received strongest support in existing studies is that unemployment increases alcohol use and abuse among heavy drinkers (Crawford et al. 1987; Dooley et al. 1992; Lahelma 1993; Winton 1986). Since several studies have shown that unemployment may increase as well as decrease alcohol use, it has been suggested that moderate drinkers and heavy drinkers may respond differently to job loss. The former may decrease and the latter increase their alcohol consumption (Crawford et al. 1987; Janlert & Hammarstrom 1992).

 

3.       August 15, 2012 Source Alcoholism: Clinical & Experimental Research

Summary:  Many studies have found that problem drinking is related to subsequent

unemployment; However, the reverse association is unclear. Some studies have found that unemployment can increase total drinking, alcohol disorders, and/or problem drinking while others have found that unemployment can decrease drinking or have no effect at all. An analysis of binge drinking as either a predictor or outcome of unemployment has found that binge drinking among women seems to have a significant association with long-term unemployment.

 

4.       The effects of unemployment on health behaviors, and substance use in particular, is still unclear despite substantial existing research. This study aimed to assess the effects of individual and spousal unemployment on smoking and alcohol consumption. The study was based on eight waves of geocoded Framingham Heart Study Offspring Cohort data (US) from 1971-2008 that contained social network information. We fit three series of models to assess whether lagged 1) unemployment, and 2) spousal unemployment predicted odds of being a current smoker or drinks consumed per week, adjusting for a range of socioeconomic and demographic covariates. Compared with employment, unemployment was associated with nearly twice the subsequent odds of smoking, and with increased cigarette consumption among male, but not female, smokers. In contrast, unemployment predicted a one drink reduction in weekly alcohol consumption, though effects varied according to intensity of consumption, and appeared stronger among women. While spousal unemployment had no effect on substance use behaviors among men, wives responded to husbands' unemployment by reducing their alcohol consumption. We conclude that individual, and among women, spousal unemployment predicted changes in substance use behaviors, and that the direction of the change was substance-dependent. Complex interactions among employment status, sex, and intensity and type of consumption appear to be at play and should be investigated further. 

Published in final edited form as: Soc Sci Med. 2014 June ; 110: 89–95. doi:10.1016/j.socscimed.2014.03.034.

 

5.       This article investigates the association between alcohol consumption and labor market outcomes in Russia, using data from the Russian Longitudinal Monitoring Survey (RLMS). It estimates cross-sectional and fixed effects models of the impacts of alcohol consumption on employment and wages for males and females using three different measures of drinking. The cross-sectional findings indicate that alcohol consumption has an inverse U-shaped impact on employment and wages for females. The impact on males appears to be positive but the inverse-U shape is less pronounced. Once the unobserved individual heterogeneity is accounted for using fixed effects, alcohol consumption is found to have no significant effect on employment for either males or females. The fixed effect wage models indicate that alcohol consumption has a small, positive, but linear impact on the wage rate for both males and females. Models including fixed effects generate estimates that are smaller in magnitude compared with those of cross-sectional models. The findings are robust to several diagnostic checks.  Southern Economic Journal Vol. 71, No. 2 (Oct., 2004), pp. 397-417 (21 pages) Published By: Southern Economic Association

 

Income & Alcohol Consumption

 

1.        A person’s income level may influence how much they drink, a new study suggests. The study found people with lower incomes had more variation in how much they drank, compared with people with higher incomes .It appears that the low-income group includes more light drinkers and non-drinkers, as well as more heavy drinkers, than the high-income group. People with higher incomes, in contrast, are more likely to drink overall, but they are also more likely to moderate how much alcohol they consume, according to NPR.  The study found genetics play a bigger role in the drinking habits of people with low incomes, while environmental factors were more influential for people who earn higher salaries. The researchers say people in higher-income communities may have more uniform family norms about drinking.  The findings appear in Alcoholism: Clinical and Experimental Research. The study included 672 pairs of adult twins. They were interviewed twice, 10 years apart. Some of the twins were identical (their genetic material is the same), while others were fraternal (their genetic connection is the same as siblings born at separate times). Each pair shared the same environment growing up.  The researchers say their finding that genetics play a bigger role in the drinking habits of people with low income suggests the stresses of being poor could trigger genetic vulnerabilities for alcohol use.  “Our study’s key finding is that genetic and environmental effects on the amount of alcohol use are not constant across all individuals in the population, but instead vary by the socioeconomic context,” lead researcher Nayla Hamdi of the University of Minnesota said in a news release. She added the findings suggest “genes and environments do not influence alcohol use in isolation but rather in interaction with one another.”  Partnership to End Addiction, By Partnership Staff March 2015

 

2.     Lifetime patterns of income may be an important driver of alcohol use. In this study, we evaluated the relationship between long-term and short-term measures of income and the relative odds of abstaining, drinking lightly-moderately and drinking heavily. We used data from the US Panel Study on Income Dynamics (PSID), a national population-based cohort that has been followed annually or biannually since 1968. We examined 3111 adult respondents aged 30-44 in 1997. Latent class growth mixture models with a censored normal distribution were used to estimate income trajectories followed by the respondent families from 1968-1997, while repeated measures multinomial generalized logit models estimated the odds of abstinence (no drinks per day) or heavy drinking (at least 3 drinks a day), relative to light/moderate drinking (<1-2 drinks a day), in 1999-2003. Lower income was associated with higher odds of abstinence and of heavy drinking, relative to light/moderate drinking. For example, belonging to a household with stable low income ($11-20,000) over 30 years was associated with 1.57 odds of abstinence, and 2.14 odds of heavy drinking in adulthood. The association between lifetime income patterns and alcohol use decreased in magnitude and became non-significant once we controlled for past-year income, education and occupation. Lifetime income patterns may have an indirect association with alcohol use, mediated through current socioeconomic conditions.  Soc Sci Med. 2011 Oct; 73(8): 1178–1185.  Published online 2011 Aug 26. doi: 10.1016/j.socscimed.2011.07.025

 

3.     Taken together, the findings discussed in this review suggest that although individuals with higher SES may consume similar or greater amounts of alcohol compared with individuals with lower SES, the latter group seems to bear a disproportionate burden of negative alcohol-related consequences. Future studies—particularly rigorous meta-analyses—are needed to more fully explore the mechanisms underlying these relationships. This research can contribute to data gathered in the context of larger public health efforts, including the Healthy People 2020 Initiative, which seeks to assess health disparities in the U.S. population by tracking rates of death, chronic and acute conditions, and health-related behaviors for various marginalized subpopulations (U.S. Department of Health and Human Services 2010). This knowledge should be applied toward the development of multilevel interventions that address not only individual-level risks but also economic disparities at higher levels that have precipitated and maintained a disproportionate level of negative alcohol-related consequences among more marginalized and vulnerable populations. Such interventions would fit well in the context of larger public health efforts (e.g., Affordable Care Act; HHS Action Plan to Reduce Racial and Ethnic Health Disparities) that are aiming to increase access to health care among people with low SES, create more preventative health programs, and improve quality of care for people seeking health care services in lower-SES areas (U.S. Department of Health and Human Services 2010, 2011).

 

4.    Professionals and people on higher incomes drink alcohol more frequently than those in routine and manual jobs in the UK, according to figures that have been seized upon by both advocates and critics of Scotland’s new minimum unit price for alcohol. An annual survey by the Office for National Statistics found that around seven in 10 people working in managerial and professional jobs — including doctors, lawyers, nurses and teachers — said they had drunk alcohol in the week before an interview. The proportion was only five in 10 from a group that included jobs such as labourers, lorry drivers and receptionists. The share of adults who had had alcohol in the past week increased steadily with income, from less than 50 per cent of the lowest earners to almost 80 per cent of those earning £40,000 or more. Delphine Strauss in London, MAY 1 2018

 

5.    PRINCETON, N.J. -- Upper-income and highly educated Americans are more likely than other Americans to say they drink alcohol. Whereas eight in 10 adults in these socio-economic status groups say they drink, only about half of lower-income Americans and those with a high school diploma or less say they drink. The results are based on Gallup's annual Consumption Habits poll, conducted July 8-12. Overall, 64% of Americans say they drink alcohol, consistent with Gallup's historical trend. Gallup has consistently found large differences in alcohol consumption among education and income subgroups over time. The income and education differences in drinking are typically larger than those seen by gender, age, race, region and religion. Americans of higher socio-economic status certainly have greater economic resources, and can likely afford to buy alcohol when they want to drink. But they also are more likely to participate in activities that may involve drinking such as dining out at restaurants, going on vacation or socializing with coworkers (given the higher drinking rates among working compared with nonworking Americans). The direct connection between drinking and engaging in these activities is not clear from the data, but such a connection could help explain why upper-income Americans are more likely to drink alcohol than other Americans. While not as powerful a predictor as income and education, religiosity is also strongly related to alcohol consumption. Specifically, 47% of those in the current poll who attend church weekly say they drink alcohol, compared with 69% who attend church less often than that, if at all. There are also notable differences in drinking by gender, with men (69%) more likely to report drinking alcohol than women (59%). Racial differences are also apparent in that non-Hispanic whites (69%) are significantly more likely to say they drink alcohol than nonwhites (52%). Among age groups, drinking is most common among 30- to 49-year-olds. Detailed percentages by subgroup appear at the bottom of the article. WELL-BEING JULY 27, 2015 Drinking Highest Among Educated, Upper-Income Americans BY JEFFREY M. JONES

 

Hypothesis:   If a person’s income level increases, then the quality and frequency of their alcohol consumption also increases

[pinerquick]

Repertoire of contention

Carstensen, Kai & Heinrich, Markus & Reif, Magnus & Wolters, Maik H., 2020." Variable selection in general multinomial logit models,"Ĭomputational Statistics & Data Analysis, Elsevier, vol. Tutz, Gerhard & Pößnecker, Wolfgang & Uhlmann, Lorenz, 2015." Electoral Punishment and Protest Politics in Times of Crisis,"ĮconStor Open Access Articles and Book Chapters, ZBW - Leibniz Information Centre for Economics, pages 227-250. Bremer, Björn & Hutter, Swen & Kriesi, Hanspeter, 2020." Are Political Parties Recapturing the Streets of Europe?,"ĮconStor Open Access Articles and Book Chapters, ZBW - Leibniz Information Centre for Economics, pages 251-272. These are the items that most often cite the same works as this one and are cited by the same works as this one. We conclude by arguing that contention repertoires remained largely unaffected by the Great Recession demonstrations were and remained the prevailing form of protest in all three regions during the whole period under study. Lastly, we turn to actors and we show that protest events increasingly feature social groups without formal organizational structures. We find that demonstrations and strikes remain the dominant form of protest across regions and time periods, while transformations in the action repertoire of contention, in the form of violent events, took place only in some parts of the south and were short lived. demonstrations, strikes, and confrontational and violent actions. Hence, we show variations in the use of commonplace action forms, i.e. In so doing, we ask whether and how the Great Recession transformed customary action repertoires in southern, north-western, and eastern Europe. In this chapter, we examine the types of protest and the types of actors over time. a protest wave, often comes with a qualitative expansion of the conflict, which can take two forms: changes in the action repertoire and a growing diversity of involved actors. The choice of specific action repertoires allows protesters to increase their visibility and eventually their success.

[360digitmg-training]

Finest Online Data Science Coaching Institute Hyderabad

Hyderabad is estimated to have around 8346 openings for multiple job roles in the field of Data Science making it the most effective place to choose a career in Data Science. I searched virtually all the most effective institutes in HYDERABAD for data science. After the course, these will provide a paid internship. I am doing the coding.

In this Data Science course we will be masking subjects proper from the very basics of Statistics and Python, R Programming to advanced Data Modeling and Predictive Analytics concepts. The minimum requirement for this course is to have a Bachelor's diploma. Also, having information about Mathematics, Statistics, Python programming, and Computer Science will be crucial for this course.

Later modules cope with Data Mining Unsupervised Learning, Recommendation Engines, Network Analytics, Machine Learning, Decision Tree and Random Forest, Text Mining, and Natural Language Processing. The last modules cope with Machine Learning - classifier techniques, Perceptron, Multilayer Perceptron, Neural Networks, Deep Learning Black-Box Techniques, SVM, Forecasting, and Time Series algorithms. This is essentially the most enriching Data Science Certification in Hyderabad in phrases of the array of matters covered. Data Science is all about mining hidden insights of knowledge pertaining to trends, behaviour, interpretation and inferences to allow informed decisions to support the business. The professionals who carry out these activities are said to be a Data Scientist / Science skilled. Data Science is the most high-in-demand profession and as per Harvard and probably the most type after occupation in the world.

Data Science derives techniques and theories from various fields corresponding to Mathematics, Statistics, Science, Information, and Computer Science. It also incorporates the techniques of cluster evaluation, data mining, knowledge visualization, and machine studying. Data Scientist is one of the hottest professions.IBM predicts the demand for Data Scientists will rise by 28% by 2020. This course will make you study Python programming, exploratory knowledge analysis, statistical strategies, SQL, machine studying methods, time series, text mining, and knowledge visualization using Tableau.

Extension to logistic regression We have a multinomial regression method used to foretell a multiple categorical consequence. Understand the idea of multi logit equations, baseline and making classifications utilizing likelihood outcomes. Learn about handling a quantity of categories in output variables together with nominal in addition to ordinal data.

Of course, they assist you to perceive the mathematical concepts and logic, too, which makes studying better and extra thorough. It was a 360DigiTMG experience with 360DigiTMG; I had enrolled in this course with coach Simon Tavasoli, who has good knowledge on the topic and was very supportive in explaining the course in detail. Plus, the content material on the platform covers the topic intimately – overall, a superb learning expertise with 360DigiTMG. The data and data science online training in hyderabad abilities you've gained working on initiatives, simulations, case studies will set you ahead of the competition. After profitable submission of the project, you may be awarded a capstone certificate that may be showcased to potential employers as a testimony to your learning.

This module will train you how to work with impartial subqueries. Subqueries involving joins and aggregations This module will train you how to work with subqueries/commands that involve joins and aggregations. In this module, you will undergo the forms of joins and discover ways to mix information.

As part of this module you learn further totally different regression methods used for predicting discrete information. These regression strategies are used to research the numeric knowledge generally identified as reliable knowledge. Based on the discrete likelihood distributions, specifically Poisson, adverse binomial distribution, the regression fashions try to match the data to those distributions. Alternatively, when excessive zeros exist within the dependent variable, zero-inflated models are most popular, you'll study the types of zero-inflated models used to fit extreme zeros information.

We might be preparing them for interviews by conducting a quantity of mock interview classes. 360DigiTMG is the most effective institute for Data Science Coaching in Hyderabad. Besides distinctive coaching delivery, we do support our students with one hundred pc placement help. Completing the Data Science Course in Hyderabad at FITA Academy has helped me to grasp the fundamentals of Data Science.

The stipulations for conducting a Hypothesis test, interpretation of the results will be discussed in this module. In this tutorial you'll study intimately about steady probability distribution. Understand the properties of a continuous random variable and its distribution beneath regular situations. To identify the properties of a steady random variable, statisticians have outlined a variable as a regular, studying the properties of the standard variable and its distribution. You will be taught to verify if a continuous random variable is following regular distribution using a standard Q-Q plot. Learn the science behind the estimation of value for a population utilizing pattern information.

Attend one-on-one sessions with profession mentors on how to develop the required expertise and angle to secure a dream job based mostly on a learners’ instructional background, previous experience, and future profession aspirations. Over 20+ reside interactive sessions with a trade professional to gain information and experience on how to build skills which are anticipated by hiring managers. These shall be guided classes and that may allow you to keep on track with your upskilling goal.

Apply the concepts gleaned on this course and get acknowledged within the office. This data science demonstrates your proficiency in advanced downside fixing with essentially the most sophistical technology available within the market. The Data Science certificate is your passport to an accelerated profession path. Learn to analyse the unstructured textual data to derive significant insights. Understand the language quirks to carry out data cleansing, extract options utilizing a bag of words and construct the key-value pair matrix called DTM.

For more information

360DigiTMG - Data Analytics, Data Science Course Training Hyderabad  

Address - 2-56/2/19, 3rd floor,, Vijaya towers, near Meridian school,, Ayyappa Society Rd, Madhapur,, Hyderabad, Telangana 500081

099899 94319

https://g.page/Best-Data-Science

[craigbrownphd]

If you did not already know

Sequential Multinomial Logit Motivated by the phenomenon that companies introduce new products to keep abreast with customers’ rapidly changing tastes, we consider a novel online learning setting where a profit-maximizing seller needs to learn customers’ preferences through offering recommendations, which may contain existing products and new products that are launched in the middle of a selling period. We propose a sequential multinomial logit (SMNL) model to characterize customers’ behavior when product recommendations are presented in tiers. For the offline version with known customers’ preferences, we propose a polynomial-time algorithm and characterize the properties of the optimal tiered product recommendation. For the online problem, we propose a learning algorithm and quantify its regret bound. Moreover, we extend the setting to incorporate a constraint which ensures every new product is learned to a given accuracy. Our results demonstrate the tier structure can be used to mitigate the risks associated with learning new products. … Meta-Interpretive Learning (MIGO) World-class human players have been outperformed in a number of complex two person games (Go, Chess, Checkers) by Deep Reinforcement Learning systems. However, owing to tractability considerations minimax regret of a learning system cannot be evaluated in such games. In this paper we consider simple games (Noughts-and-Crosses and Hexapawn) in which minimax regret can be efficiently evaluated. We use these games to compare Cumulative Minimax Regret for variants of both standard and deep reinforcement learning against two variants of a new Meta-Interpretive Learning system called MIGO. In our experiments all tested variants of both normal and deep reinforcement learning have worse performance (higher cumulative minimax regret) than both variants of MIGO on Noughts-and-Crosses and Hexapawn. Additionally, MIGO’s learned rules are relatively easy to comprehend, and are demonstrated to achieve significant transfer learning in both directions between Noughts-and-Crosses and Hexapawn. … km-means The $k$-means algorithm is the most popular nonparametric clustering method in use, but cannot generally be applied to data sets with missing observations. The usual practice with such data sets is to either impute the values under an assumption of a missing-at-random mechanism or to ignore the incomplete records, and then to use the desired clustering method. We develop an efficient version of the $k$-means algorithm that allows for clustering cases where not all the features have observations recorded. Our extension is called $k_m$-means and reduces to the $k$-means algorithm when all records are complete. We also provide strategies to initialize our algorithm and to estimate the number of groups in the data set. Illustrations and simulations demonstrate the efficacy of our approach in a variety of settings and patterns of missing data. Our methods are also applied to the clustering of gamma-ray bursts and to the analysis of activation images obtained from a functional Magnetic Resonance Imaging experiment. … Principal Component Pursuit (PCP) see section 1.2 ➘ “Robust Principal Component Analysis” … https://analytixon.com/2022/07/09/if-you-did-not-already-know-1765/?utm_source=dlvr.it&utm_medium=tumblr

[insurehunter297]

Limdep 9 New Version

Java SE 9 Archive Downloads. Go to the Oracle Java Archive page. The JDK is a development environment for building applications using the Java programming language. The JDK includes tools useful for developing and testing programs written in the Java programming language and running on the Java TM platform.

For more than two decades, it has been the econometrics software of choice at universities, central banks, and corporations around the world. Their current release, Version 9.0, is easier to use than ever while continuing to offer the most advanced tools available for cutting-edge econometrics research.

The LIMDEP Logit$ and Probit$ commands support a variety of categorical dependent variable models that are addressed in Greene’s Econometric Analysis (2003). The output format of LIMDEP 9 is slightly different from that of previous version, but key statistics remain unchanged. New Form I-9 Released. 31, 2020, USCIS published the Form I-9 Federal Register notice announcing a new version of Form I-9, Employment Eligibility Verification, that the Office of Management and Budget approved on Oct. This new version contains minor changes to the form and its instructions.

LIMDEP 9.0 GUIDE PDF

LIMDEP & NLOGIT are powerful statistical & data analysis software for panel data , stochastic frontier, multinomial choice modeling, probit, fixed effects, mixed. Part I Reference Guide to Using LIMDEP Part I Reference Guide Chapter 1 Chapter 2 Chapter 3 Chapter 4 Chapter 5 Chapter 6 Chapter 7 Chapter 8 Chapter 9. LIMDEP Econometric Modeling Guide, Volume 2. NLOGIT Reference Guide. Documentation for LIMDEP/NLOGIT consists of some 2, pages of.

Author:Zulur NikotaurCountry:MalaysiaLanguage:English (Spanish)Genre:EnvironmentPublished (Last):19 May 2012Pages:407PDF File Size:16.25 MbePub File Size:8.84 MbISBN:334-7-86963-260-4Downloads:29721Price:Free* (*Free Regsitration Required)Uploader:Telrajas

They help reduce landfill by eliminating waste from outdated manuals. You can carry a full set of manuals without worrying about space or weight, and you can read the manuals anywhere. The manuals may be updated more easily.

There are chapters on Descriptive statistics Linear regression Panel data analysis Heteroscedasticity Binary choice models Models for count data Censored and truncated data Survival models Nonlinear regression Time series models Nonlinear optimization Sample selection models and many others. Topics are arranged by modeling framework, not by program command.

LIMDEP Reference Guide: Version – William H. Greene – Google Books

If you wish to read a 9. of the manuals in the traditional way, you can print it and have the benefit of a paper-based book. Second, we have included extensive explanatory text and dozens of examples, with guire, for every technique and model presented. They save transportation costs and the pollution associated with shipping books. You can personalize your manual by changing the font size and viewing format. They use very few natural resources.

The full set of formulas for all computations are shown with complete mathematical documentation of the models. Each model fit by the program is fully documented. Is Green In our effort to be environmentally conscious, we have discontinued printing paper manuals for our new versions.

Limdep 9 New Version Free Download

Documentation: PDF Manuals

Additional chapters in this guide show how to do numerical analysis and how to program your own estimators. There are chapters on.

These operations include reading and transforming data and setting the sample. No paper is used in the production of our manuals so they save trees and reduce pollution.

The LIMDEP 11 Reference Guide provides all instructions for operating the program, including installation, invocation, and most of the basic setup operations that precede model estimation. The LIMDEP 11 documentation with over 2, pages, contains full reference guides for the program, background econometrics, and sample applications.

Ebooks are increasingly popular and our new manuals offer many benefits and advantages: Reproduction in whole or in part without permission is prohibited. Manual links offer easy access to additional information and related websites. With a download option, the manuals are available much faster than waiting for delivery of printed manuals. This manual also describes the optimization procedures, how to use the matrix algebra package and scalar scientific calculator as stand alone tools and as part of LIMDEP programs, what types of results are produced by the program, and some of the common features of the model estimation programs, such as how to do post estimation analysis of model results, including partial effects and simulation.

You can easily search for any information directly in the manuals rather than through an index. Whether you download the manuals or receive them on disc, transportation costs are significantly reduced or eliminated.

Related Articles (10)

(Redirected from STATISTICA)

StatisticaOriginal author(s)StatSoftDeveloper(s)TIBCO SoftwareStable releaseOperating systemWindowsTypeNumerical analysisLicenseProprietary softwareWebsitewww.tibco.com/data-science-and-streaming

Statistica is an advanced analytics software package originally developed by StatSoft and currently maintained by TIBCO Software Inc.(1)Statistica provides data analysis, data management, statistics, data mining, machine learning, text analytics and data visualization procedures.

Overview(edit)

Statistica is a suite of analytics software products and solutions originally developed by StatSoft and acquired by Dell in March 2014. The software includes an array of data analysis, data management, data visualization, and data mining procedures; as well as a variety of predictive modeling, clustering, classification, and exploratory techniques.(2) Additional techniques are available through integration with the free, open source R programming environment.(3)(4)Different packages of analytical techniques are available in six product lines.(5)

History(edit)

Statistica originally derives from a set of software packages and add-ons that were initially developed during the mid-1980s by StatSoft. Following the 1986 release of Complete Statistical System (CSS) and the 1988 release of Macintosh Statistical System (MacSS), the first DOS version (trademarked in capitals as STATISTICA) was released in 1991. In 1992, the Macintosh version of Statistica was released.

Statistica 5.0 was released in 1995. It ran on both the new 32-bit Windows 95/NT and the older version of Windows (3.1). It featured many new statistics and graphics procedures, a word-processor-style output editor (combining tables and graphs), and a built-in development environment that enabled the user to easily design new procedures (e.g., via the included Statistica Basic language) and integrate them with the Statistica system.

Statistica 5.1 was released in 1996 followed by Statistica CA '97 and Statistica '98 editions.

In 2001, Statistica 6 was based on the COM architecture and it included multithreading and support for distributed computing.

Statistica 9 was released in 2009, supporting 32 bit and 64-bit computing.

Statistica 10 was released in November 2010. This release featured further performance optimizations for the 64-bit CPU architecture, as well as multithreading technologies, integration with Microsoft SharePoint, Microsoft Office 2010 and other applications, the ability to generate Java and C# code, and other GUI and kernel improvements.(2)

Statistica 12 was released in April 2013 and features a new GUI, performance improvements when handling large amounts of data, a new visual analytic workspace, a new database query tool as well as several analytics enhancements.(2)

Localized versions of Statistica (including the entire family of products) are available in Chinese (both Traditional and Simplified), Czech, English, French, German, Italian, Japanese, Polish, Russian, and Spanish. Documentation is available in Arabic, Chinese, Czech, English, French, German, Hungarian, Italian, Japanese, Korean, Polish, Portuguese, Russian, Spanish, and other languages.

Acquisition history(edit)

Limdep 9 New Version Gst Download

Statistica was acquired by Dell in March 2014.(6) In November 2016, Dell sold off several pieces of its software group, and Francisco Partners and Elliott Management Corporation acquired Statistica as part of its purchase of Quest Software from Dell.(7) On May 15, 2017, TIBCO Software Inc. announced it entered into an agreement to acquire Statistica.(1)

Release history(edit)

List of releases:(2)

PsychoStat - 1984

Statistical Supplement for Lotus 1-2-3 - 1985

StatFast/Mac - 1985

CSS 1 - 1987

CSS 2 - 1988

MacSS - 1988

STATISTICA/DOS - 1991

STATISTICA/Mac - 1992

STATISTICA 4.0 - 1993

STATISTICA 4.5 - 1994

STATISTICA 5.0 - 1995

STATISTICA 5.1 - 1996

STATISTICA 5.5 - 1999

STATISTICA 6.0 - 2001

STATISTICA 7.0 - 2004

STATISTICA 7.1 - 2005

STATISTICA 8.0 - 2007

STATISTICA 9.0 - 2009

STATISTICA 9.1 - 2009

STATISTICA 10.0 - 2010

STATISTICA 11.0 - 2012

STATISTICA 12.0 - 2013

Statistica 12.5 - April 2014(8)

Statistica 12.6 - December 2014(9)

Statistica 12.7 - May 2015(10)

Statistica 13.0 - Sept 2015(11)

Statistica 13.1 - June 2016(12)

Statistica 13.2 - Sep 30, 2016

Statistica 13.3 - June, 2017 (13)

Statistica 13.3.1 - November, 2017 (14)

Statistica 13.4 - May 2018 (15)

Statistica 13.5 - November 2018 (16)

Statistica 13.6 - November 2019 (17)

Statistica 14.0 - December 2020 (18)

Graphics(edit)

Statistica includes analytic and exploratory graphs in addition to standard 2- and 3-dimensional graphs. Brushing actions (interactive labeling, marking, and data exclusion) allow for investigation of outliers and exploratory data analysis.

User interface(edit)

Limdep 9 New Version

Operation of the software typically involves loading a table of data and applying statistical functions from pull-down menus or (in versions starting from 9.0) from the ribbon bar. The menus then prompt for the variables to be included and the type of analysis required. It is not necessary to type command prompts. Each analysis may include graphical or tabular output and is stored in a separate workbook.

See also(edit)

References(edit)

^ ab'TIBCO Software to Acquire Data Science Platform Leader Statistica'.

^ abcd'TIBCO® Data Science'. TIBCO Software Inc.

^Christian H. Weiss 'Commercial meets Open Source: Tuning STATISTICA with R'R-Project. March 2008.

^'StatSoft Certifies REvolution Computing R Language'Archived 2013-01-26 at archive.todayHPCwire. December 2008.

^STATISTICA Product Overview

^us, Dell. 'Press Releases'. Dell. Retrieved 2015-09-23.

^'Press Release'. Francisco Partners.

^'Statistica 12.7 Release Notes Guide'. Dell Software. Retrieved 2015-09-23.

^'Statistica - Release Notes and Guides'. support.software.dell.com. Retrieved 2015-09-23.

^'Statistica 12.7 Release Notes'. documents.software.dell.com. Retrieved 2015-09-23.

^'Statistica - Release Notes and Guides'. support.software.dell.com. Retrieved 2015-10-14.

^'Dell Statistica 13.1 Release Notes'(PDF).

^'TIBCO Statistica 13.3.0'.

^'TIBCO Statistica 13.3.1'.

^'TIBCO Statistica 13.4.0'.

^'TIBCO Statistica 13.5.0'.

^'TIBCO Statistica 13.6.0'.

^'TIBCO Statistica 14.0.0'.

Further reading(edit)

Afifi, A.; Clark, V.; May, S. (2003). Computer-Aided Multivariate Analysis. New York: CRC Press.

Hill, T., and Lewicki, P. (2007). STATISTICS Methods and Applications. Tulsa, OK: StatSoft.

Nisbet, R., Elder, J., and Miner, G. (2009). Handbook of Statistical Analysis and Data Mining Applications. Burlington, MA: Academic Press (Elsevier).

Sá, Joaquim (2007). Applied Statistics Using Spss, STATISTICA, Matlab and R. Berlin: Springer. ISBN3-540-71971-7.

Stein, Philip G.; Matey, James R.; Pitts, Karen (1997). 'A Review of Statistical Software for the Apple Macintosh'. The American Statistician. 51 (1): 67–82. doi:10.1080/00031305.1997.10473593.

External links(edit)

Electronics statistics textbook online (1)

Retrieved from 'https://en.wikipedia.org/w/index.php?title=Statistica&oldid=1015231823'

[crookedtreepoetry]

Data Science Course Coaching With ML

Your Project group will be assigned a project mentor and shall be given chance to work on the project With steerage and Assistance. We have promotions and early chook offer every so often. Please verify on web site or discuss to our sales team to know extra about relevant discount. We present each choices i.e Classroom training and instructor led reside coaching choice. Based in your requirement and wish ,you possibly can selected coaching mode. We consider the requirement and need for freshers and Working professionals are completely different. So, We have separate course for freshers and dealing professionals.

You will study each detail of a typical Machine Learning project and understand how to solve real-world problems by making use of AI-associated solutions that make a enterprise influence. The Complete Machine Learning Course is a comprehensive coaching program designed for individuals who need to perceive the ins and outs of Machine Learning. The course will train you every thing you have to know to start constructing a career in Data Science.

As part of this module you be taught additional totally different regression methods used for predicting discrete data. These regression techniques are used to investigate the numeric data known as depend information. Based on the discrete chance distributions specifically Poisson, unfavorable binomial distribution the regression models try to fit the data to those distributions.

You will also be taught concerning the google page ranking algorithm as a part of this module. Extension to logistic regression We have a multinomial regression method used to predict a a number of categorical consequence. Understand the concept of multi logit equations, baseline and making classifications using chance outcomes.

This is helping to create myriad knowledge science/analytics job alternatives in this house. The void between the demand and provide for the Data Scientists is huge and hence the salaries pertaining to Data Science are sky high and considered to be the most effective in the industry. Data Scientist profession path is long and profitable because the era of online data is perpetual and growing sooner or later. Kickstart your studying of Python for data science, in addition to programming normally, with this newbie-pleasant introduction to Python. Python is likely one of the world’s most popular programming languages, and there has never been higher demand for professionals with the power to apply Python fundamentals to drive enterprise options across industries. To perceive the data, they use a variety of tool libraries, corresponding to Machine Learning, statistics and probability, linear and logistic regression, time collection analysis, and more.

This information science demonstrates your proficiency in complicated drawback solving with the most sophistical technology available in the market. The Data Science certificates is your passport to an accelerated career path. 360digitmgResearch Lab is likely one of the best information science training institute in Hyderabad. This team advised me to go along with an information science course, for the first two days I heard the demo and simply received impressed by their instructing and with no second thought choose data science course.

Other than that, lots of the actual advantages, like accessing graded homework and tests, are only accessible should you improve. If you have to keep motivated to finish the complete course, committing to a certificate also puts money on the line so you’ll be much less more likely to give up. I assume there’s definitely private value in certificates, but, unfortunately, not many employers value them that much. One massive difference between Udemy and other platforms, like edX, Coursera, and Metis, is that the latter supply certificates upon completion and are usually taught by instructors from universities. It turned out to be extremely powerful working on one thing I was keen about. It was easy to work exhausting and be taught nonstop as a result of predicting the market was something I actually needed to accomplish.

With the instruments hosted within the cloud on Skills Network Labs, you will be able to check each tool and follow directions to run simple code in Python, R or Scala. To end the course, you'll create a ultimate project with a Jupyter Notebook on IBM Watson Studio and reveal your proficiency preparing a notebook, writing Markdown, and sharing your work with your friends. This certificate could be very nicely recognized in 360digitmg-affiliated organizations, including over 80 prime MNCs from all over the world and a number of the Fortune 500companies. You will work on highly exciting initiatives within the domains of excessive know-how, ecommerce, advertising, sales, networking, banking, insurance coverage, etc.

360digitmg doesn't currently have a approach to offer certificates, so I usually discover Udemy programs to be good for extra utilized studying materials, whereas Coursera and edX are normally better for concept and foundational materials. Python is used on this course, and there’s many lectures going through the intricacies of the various information science libraries to work via actual-world, interesting issues. This is likely one of the solely data science programs around that really touches on every a part of the data science process. Lastly, when you’re extra thinking about studying information science with R, then definitely check out 360digitmg new Data Analyst in R path.

In this tutorial you will study joint probability and its functions. Learn how to predict whether or not an incoming email is a spam or a ham e-mail. Learn about Bayesian likelihood and the applications in solving advanced business issues. The Boosting algorithms AdaBoost and Extreme Gradient Boosting are mentioned as a part of this continuation module You may also learn about stacking methods. Learn about these algorithms which are offering unprecedented accuracy and serving to many aspiring data scientists win the primary place in numerous competitions corresponding to Kaggle, and so on. As part of this module, you'll proceed to be taught Regression techniques applied to foretell attribute Data.

Explore more on - data science course in hyderabad with placements

360DigiTMG - Data Analytics, Data Science Course Training Hyderabad

Address:-2-56/2/19, 3rd floor, Vijaya towers, near Meridian school, Ayyappa Society Rd, Madhapur, Hyderabad, Telangana 500081

Contact us ( 099899 94319 )

https://360digitmg.com/data-science-course-training-in-hyderabad

Hours: Sunday - Saturday 7 AM - 11 PM

[ut-ite]

Seminar Series: Modelling Freight Vehicle Type Choice using Machine Learning and Discrete Choice Methods

Date: Friday, March 12, 2021 @ 12:00 PM - 1:00 PM ET

Location: Online via BbCollab

https://ca.bbcollab.com/guest/4f57ee641eb74135a9a05bb805a64cc3

Speaker: Usman Ahmed

Abstract: The choice of vehicle type is one of the important logistics decisions made by firms. The complex nature of the choice process is due to the involvement of multiple agents. This study employs a random forest machine learning algorithm to represent these complex interactions with limited information about shipment transportation. The data are from commercial travel surveys with information about outbound shipment transportation. This study models the choice among four road transport vehicle types: pickup/cube van, single unit truck, tractor trailer, and passenger car. The characteristics of firms and shipments are used as explanatory variables. Permutation-based variable importance is calculated to interpret the importance of each variable which shows that employment and weight are the most important variables in determining the choice of vehicle type. The random forest model is also compared with the multinomial and mixed logit models. The model prediction results on the testing data are compared. The results show that random forest model outperforms both the multinomial and mixed logit model with an overall increase in accuracy of about 8.3% and 11%, respectively.

Bio: Usman Ahmed is a PhD candidate at the Department of Civil and Mineral Engineering at the University of Toronto, under the supervision of Professor Matthew Roorda. He received his masters’ degree in Transportation Systems in 2018 from the Technical University of Munich and bachelors’ degree in Civil Engineering from the National University of Sciences and Technology, Islamabad. During his master’ studies, he also worked as a research assistant at Modelling Spatial Mobility research group. His research interests include transportation modelling, emissions modelling and machine learning applications in transportation.

Video: Not provided by request.

[innspubnet]

By: PO Mongare, JR Okalebo, CO Othieno, JO Ochuodho, AK Kipkoech, AO Nekesa

Key Words: Survey, Soil fertility technologies, Adoption

Int. J. Agron. Agri. Res. 15(6), 1-9, December 2019.

Certification: ijaar 2019 0199 []

Download Certificate

Abstract

A survey on adoption levels of the existing soil nitrogen replenishing technologies amongst farmers in three counties in western Kenya was carried out in June 2011. Three farmer associations were Angurai Farmers Development Project (AFDEP), Bungoma Small-Scale Farmers Forum (BUSSFFO) and Mwangaza Farmer Group (MFAGRO). During the survey 223 farmers were interviewed with roughly a half of the households surveyed being members of farmer associations (FAs) and the other half being non-members, who acted as the control. Stratified random sampling technique was used. A repeated measures Analysis of Variance (RM – ANOVA) showed that various soil nitrogen replenishment technologies were adopted to various degrees, F (4.39, 855.43) =23.36, p<.001). The findings of this study indicated that the available technologies most extensively used in the study area were the use of inorganic fertilisers (DAP), planting of improved legumes processing, Lab lab, Push Pull, and Super 2 Package. In second place, were technologies such as seed inoculation, foliar feed use, top dressing fertiliser (CAN) and use of improved legumes. The least used technologies were found to be Ua Kayongo (IR seed), MBILI intercropping, fortified compost, and use of Farm yard manure and liming. The results also indicated that generally, adoption of technologies was higher amongst farmer association members compared with non-members regardless of the county. Bungoma County had significantly highest level of technology adoption level compared to both Busia and Vihiga. Adoption of soil technologies was also found to be positively correlated with farmers’ educational level but inversely related with their age.

| Views 112 || Views 112 |        

Download the full paper

Dar WD, Twomlow SJ. 2007. Managing agricultural intensification: The role of international research. Crop Protection 26(3), 399-407.

Government of Kenya. 2010. Agricultural Sector Development Strategy (2010 – 2020). Retrieved 20th October, 2016 from https://www.ascu.go.ke.

Jaetzold R, Schmidt H, Hornetz B, Shisanya C. 2006. Ministry of Agriculture Farm Management Handbook of Kenya VOL. II-Part C Subpart C1. Nairobi: Ministry of Agriculture.

Kebeney S, Msanya B, Semoka J, Ngetich W, Kipkoech A. 2015. Socioeconomic factors and soil fertility management practices affecting sorghum production in western Kenya: A case study of Busia county. American Journal of Experimental Agriculture 5(1), 1-11.

Kiptot E. 2008. Adoption dynamics of Tithonia diversifolia for soil fertility management in pilot villages of western Kenya. Experimental Agriculture 44, 73-484.

Kothari R. 2004. Research methodology, methods and techniques. New Delhi, New Age International (p) Ltd Publishers.

Marenya PP, Barrett CB. 2007. Household-level determinants of adoption of improved natural resources management practices among smallholder farmers in western Kenya. Food Policy 32, 515-536.

Matata P, Ajay O, Oduol P, Agumya A. 2010. Socio-economic factors influencing adoption of improved fallow practices among smallholder farmers in western Tanzania. African Journal of Agricultural Research 5(8), 818-823.

McCann JC. 2005. Maize and Grace. Africa’s Encounter with a New World Crop 1500–2000. Harvard University Press, Cambridge.

Nandwa SM. 2003. Perspectives on soil fertility in Africa. In: MP Gichuru, A Bationo, MA Bekunda, HC Goma, PL Mafongonya, DN Mugendi, HM Murwira, SM Nandwa, P Nyathi and M Swift (Eds). Soil fertility management in Africa: A regional perspective. Academic Science Publishers, Nairobi, Kenya.

Nkamleu GB. 2007. Modelling farmers’ decisions on integrated soil nutrient management in sub-Saharan Africa. A multinomial logit analysis in Cameroon. In: Bationo (Ed). Advances in intergrated soil fertility management in sub-Saharan Africa: Challenges and opportunities. Springer Publishers Netherlands 891-903.

Noordzij M, Tripepi G, Dekker F, Zoccali C, Tanck M, Jager K. 2010. Sample size calculations: basic principles and common pitfalls. Nephrol Dial Transplant 25, 1388-1393.

Obura PA, Okalebo JR, Woomer PL. 1999. The effect of PREP-PAC. Components on maize soybeans growth, yield uptake in the acid soil of western Kenya, PREP annual report.

Odendo M, Obare G, Salasya B. 2010. Determinants of the speed of adoption of soil fertility enhancing technologies in western Kenya. Contributed Paper presented at the Joint 3rd African Association of Agricultural Economists (AAAE) and 48th Agricultural Economists Association of South Africa (AEASA) Conference, Cape Town, South Africa, September 19-23, 2010.

Odendo M, Ojiem J, Bationo A, Mudeheri M. 2006. On-farm evaluation and scaling-up of soil fertility management technologies in western Kenya. Nutrient Cycling in Agroecosystems 76, 369-381.

Okalebo JR, Othieno CO, Woomer PL, Karanja NK, Semoka JRM, Bekunda MA, Mugendi DN, Muasya RM, Bationo A, Mukwana EJ. 2006. Available technologies to replenish soil fertility in East Africa. Nutrient Cycling in Agroecosystems 76, 153-170.

Ramisch JJ, Misiko MT, Ekise IE, Mukalama JB. 2006. Strengthening ‘Folk Ecology’: community based learning for integrated soil fertility management, western Kenya. International Journal of Agricultural Sustainability 4(2), 154-168.

Sanchez PA, Denning GL, Nziguheba G. 2009. The African green revolution moves forward. Food Security 1, 37-44.

Sanginga N, Woomer PL (Eds.). 2009. Integrated soil fertility management in Africa: Principles, practices and developmental process. Tropical Soil Biology and Fertility Institute of the International Centre for Tropical agriculture. Nairobi p. 263

Sileshi G, Akinnifesi FK, Debusho LK, Beedy T, Ajayi OC, Mongomba S. 2010. Variation in maize yield gaps with plant nutrient inputs, soil type and climate across sub-Saharan Africa. Field Crops Research 116, pp 1-13.

Smaling E, Nandwa S, Janssen B. 1997. Soil fertility in Africa is at stake. In Buresh, R., Sanchez, P. and Calhoum, F. (eds), Replenishing soil fertility in Africa. SSSA Special Publication, 51, Madison, USA.

Vanlauwe B, Bationo A, Chianu J, Giller KE, Merckx R, Mokwunye U, Ohiokpehai O, Pypers P, Tabo R, Shepherd KD, Smaling EMA, Woomer PL Sanginga N. 2010. Integrated soil fertility management: Operational definition and consequences for implementation and dissemination. Outlook on Agriculture 39(1), 17-24.

                                                          PO Mongare, JR Okalebo, CO Othieno, JO Ochuodho, AK Kipkoech, AO Nekesa.

Findings from a survey in Western Kenya to determine the soil fertility replenishment technologies adoption rates.

Int. J. Agron. Agri. Res. 15(6), 1-9, December 2019.

https://innspub.net/ijaar/findings-survey-western-kenya-determine-soil-fertility-replenishment-technologies-adoption-rates/

                                                          Copyright © 2019

By Authors and International Network for

Natural Sciences (INNSPUB)

https://innspub.net                

innspub.net

[digitattva]

Data Science Course in Pune

You will discuss with regard to the parameters employed in the perceptron algorithm which is that the inspiration of developing much complicated neural network fashions for AI applications. Understand the appliance of perceptron algorithms to categorise binary information in a very linearly separable situation. Extension to logistic regression we've a multinomial regression method wont to foretell a variety of categorical consequence. Understand the concept of multi logit equations, baseline and making classifications using chance outcomes. find out about handling variety of classes in output variables including nominal also as ordinal information. Yes, we do supply a money-back guarantee for lots of of our training programs.

Any graduate who has successfully accomplished our Data Science course is eligible to participate within the JobAssist program. No, the JobAssist program is intended which will facilitate your to find your dream job. it'll maximize your potential and probabilities of touchdown a profitable job. In career mentoring periods, material Experts or trade specialists answer questions associated with career growth and opportunities.

Click Here to Know More:- Data Science Course in Pune

Dedicated profession experts from IIMJobs will fully handhold learners to register on the portal, provide them suggestions and steerage to enhance their profile and follow the proper keywords. A flexi-cross is among the superb choices provided, and you may choose variety of sessions at your personal tempo for studying. The course is neat and in-line with current market requirements.

the information Science course are often accomplished in round six months once you dedicate some hours each day to learning. No, the JobAssist program may be a supplementary offering that comes along with the info Scientist course.

Our Data Science Course in Pune  is exhaustive and this certificates is proof that you have taken an enormous leap in mastering the domain. Our Big Data Hadoop certification training course helps you to master the ideas of the Hadoop framework, Big Data instruments, and methodologies to arrange you for achievement in your function as an enormous Data Developer.

Python and R are straightforward to be taught and maintain and subsequently, Godsend to developers in Data Science. Their extended libraries make it potential to stretch the functions of Python from Big Data Analytics to Machine Learning.

It can even define as a combination of information research, algorithms, and expertise to resolve complicated analytical points. The usable Data form may be completely different for various sections of people working in an exceedingly corporation.

Learn how various components of the Hadoop ecosystem match into the massive processing lifecycle. the information Science with Python course provides a full overview of knowledge Analytics instruments and methods using Python. Acquiring knowledge in Python are going to be the key to unlock your career as an information Scientist. Big dataThis area pertains to the flexibility of the knowledge skilled to aggregate, through all potential sources, quality knowledge and additionally manage the identical for productive analyses. Data Science, in its simpler terms, is about producing critical enterprise value from the data via numerous creative methods.

Choose from programmes specifically curated to suit every skilled’s coaching wants. We present end to complete data science placement help after the internship is over. We also float your resume to variety of dependable placement consultants with whom we now have an extended association. Perceptron algorithm is defined based totally on a biological brain model.

[craigbrownphd-blog-blog]

R Packages worth a look

Robust Survey Statistics Estimation (robsurvey) Multiple functions to compute robust survey statistics. The package supports the computations of robust means, totals, and ratios. Available methods ar … Interactive Document for Working with Multidimensional Scaling and Principal Component Analysis (MDSPCAShiny) An interactive document on the topic of multidimensional scaling and principal component analysis using ‘rmarkdown’ and ‘shiny’ packages. Runtime examp … Multinomial Logit Model (mnlogit) Time and memory efficient estimation of multinomial logit models using maximum likelihood method. Numerical optimization performed by Newton-Raphson me … Kaplan-Meier Multiple Imputation for the Analysis of Cumulative Incidence Functions in the Competing Risks Setting (kmi) Performs a Kaplan-Meier multiple imputation to recover the missing potential censoring information from competing risks events, so that standard right- … http://bit.ly/2O8MXwQ

[eurekakinginc]

"[D] Resources on Assortment Optimization"- Detail: Hey data scientists of Reddit, would like to seek some advice from the experts here.I am trying to implement an assortment optimization problem, where you have to pick a subset of good from the master set which maximises the objective (i.e. revenue/profit). Eventually, I would like to work towards a tractable SKU-level sale forecast function (choice model) that accounts for cannibalization, substitution and seasonality. As I understand it, this function would have to be fed into an optimisation algorithm (i.e. linear integer programming).From my readings, I gather that Multinomial Logit Model (MNL) should be the baseline/ starting point. However, I can't seem to grasp how I could extend MMNL to account for cannibalization, substitution and seasonality.Can any kind souls point me to a good place to start on optimization in the field of operations research for assortment optimization?​tldr: Where can I find resources on assortment optimization with cannibalization, substitution and seasonality effects?. Caption by Juzkev. Posted By: www.eurekaking.com

[livingwellpage]

Do consumers make rational choices for their Part D Plan?

To answer this question, one can examine how individuals choose health plans based on premiums, expected out of pocket cost, plan quality, and other factors. A paper by Abaluck and Gruber (2011) use data from 2006 and find that up to 70% of seniors appear to choose plans that are not optimal. Do these conclusions hold with more recent data? And are there improved methods for answering this question.

One question is how does one treat the error term in these regressions. In a standard random utility model, the error term captures heterogeneous tastes for unobserved product attributes. People are not perfectly rational, however, and thus the error term could also capture optimization error, genuine randomness in decision making, or other types of confusion.

To answer this question and examine whether PDP plan choice has improved in more recent data, a working paper by Keane et al. (2019) use a finite mixture of mixed logit model or MM-MNL model. Their basic approach uses the following model: 

U_ij = (Pj)α+[E(oop)ij]β1 + (σ^2_ij)β2 + (cj)β3 + (Qj)β4 + e_ij

In this equation, P is the premiums for plan j, E(oop) is the expected out of pocket costs,  σ^2_ij is the variance in out of pocket costs, cj, is a vector of financial characteristics of plan j that affect OOP, and Qij is a vector of plan quality measures, which in this study includes both star ratings and indicator variables for plan “brand”.

The authors initially conceive of 2 latent classes of individuals.  The rational, risk neutral individual would be indifferent to premiums and out of pocket costs (assuming the impact from discounting is small given the 1 year time horizon covered by Part D Plans) and thus α = β1. Also rational individuals should be indifferent among different financial characteristics (cj) that lead to the same E(oop) and σ^2_ij and thus β3=0. In short, we can divide the world into rational individuals where utility is defined as:

U_ij = [Pj + E(oop)ij]β1 + (σ^2_ij)β2 + (Qj)β4 + e_ij

with probability p and with probability 1-p individuals are not rational and have utility as described in the first equation.  In short, conditional on a person’s latent type (i.e., rational vs. not) and his/her preference parameters, we have a simple multinomial logit model.

The authors also extend this model by: (i) considering more than two types where the “rational” type is defined as above, and the data is used to determine the other types and (ii) the authors let individual characteristics (e.g., age, presence of Alzheimers’ disease, depression) affect individual decision-making ability.

The authors highlight the benefit of their model, saying:

Given estimates of the decision utilities of the confused type, as well as the distribution of their parameter vector ), we can learn how their behavior is sub-optimal. Do many consumers…place excessive weight on premiums vs. OOP costs? Or are these excesses statistically significant but quantitatively small?  Are there particular “irrelevant” financial attributes of insurance plans that consumers tend to overweigh in making decisions? 

The authors then use PDP administrative data from non-low income subsidy individuals as well as data from the Medicare Current Beneficiary Survey to test this approach.

The authors have a number of interesting findings. First, individuals place more weight on premium reduction than reducing future out-of-pocket cost. Second, a plan’s brand plans an important part in plan choice for some consumers. In general, fewer than one-in-ten consumers are perfectly rational from an economist’s defminition..

…we find that 9.8% of consumers are classified as the “rational” type, while 11.4% place excess weight on low premiums, and 78% place value on plan characteristics that are irrelevant once one conditions on the distribution of plan costs…As expected, people with dementia and depression are more likely to be “irrational.” And the bulk of the econometric error term is attributed to optimization error

A more important question may be, does this matter? If people are choosing incorrectly, are these error costing them $5 per year or thousands of dollars? The authors perform a welfare analysis to see how welfare would improve if people picked a more optimal plan.

…we find welfare losses to be modest except in a small subset of cases (e.g., people with dementia and depression face a high variance of OOP costs, suggesting they are not well insured). In contrast to traditional choice models, in our framework consumer welfare can be enhanced by eliminating “bad” options from the choice set. But as in Ketcham et al. (2019) we find that such policies lead at best to trivial welfare improvements. This occurs for two reasons: (i) Part D premiums are heavily subsidized, so even a “bad” plan is better than no plan, and (ii) given consumer heterogeneity, very few plans are “bad” for everyone. 

Do consumers make rational choices for their Part D Plan? published first on your-t1-blog-url