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Statistical Modeling - Science topic
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Questions related to Statistical Modeling
I am working in the field of forest ecology by using statistical modelling. Please write me for any further clarification.
I am currently doing my undergraduate study, and I am using UTAUT2 to evaluate factors that can affect adoption of a new application we made. I have 30 sample size, all obtain using a purposive sampling only. I want to use SEM by performing PLS but it seems that I needed a bigger sample size, what other statistical models can I use?
I am refering to models like REM, SC, REST and CTDS
I can't find articles that describe the quality of multivariate statistical models according to RMSEC, RMSECV and RMSEP values. I find only describing about R2 and RPD, as in the Williams 2003 article.
In my time series dataset, I have 1 dependent variable and 5 independent variables and I need to find the independent variable that affects the dependent variable the most (the independent variable that explains most variations in the dependent variable). Consider all 5 variables are economic factors.
LUE model consider the climatic factors even some model considers CO2 fertilization effect on radiation energy conversion, so I think the lue model is not only statistical model but also on the basis of bio-physical or bio-chemistrical theory. But I have no idea which is right.
Seek help from well-meaning scholars. Thanks a lot.
I want a statistical model to analyze my data on rare a rare disease( asymptomatic or submicroscopic malaria). I want a consultation from experts in the field.
I am convinced that logistic regression is not suitable for my study however there are dozens of published articles used it. I want to see it in different way.
Hence, need your prompt responses.
Currently, data is available in forms of text, images, audio, video and other such forms.
We are able to use mathematical and statistical modeling for identifying different patterns and trends in data which can be used through machine learning which is a A.I's subsidiary for performing different decision making tasks. The data can be visualized in variety of forms for different purposes.
Data Science is currently the ultimate state of Computing. For generating data we have hardware, software, algorithms, programming, and communication channels.
But, what could be next beyond this mere data creation and manipulation in Computing?
I have a dataset of 140 students who have sat an exam. The exam had a total of 30 marks, split equally into an A and B question. The A question was numerical and was made up of several calculations. The B question was an essay response to a problem.
The exam has been marked twice. The first time using a mark scheme, each of the calculations had a maximum possible number of marks as did the essay. Each sub question was marked with reference to the mark scheme before all of the scores were totalled. The second time was based purely on academic judgement, the same marker considered the whole exam and made a judgement based on the overall performance.
I've calculated the correlation (.847, p=.00003), and I'm keen to explore this further, but I'm drawing a blank on which statistical models could be used? Any suggestions?
The statistical model I used for the calculation of air-sea CO2 fluxes, gives me results of net air-sea CO2 flux in GtC yr-1.
I would like to convert GtC year-1 to mmol m-2 d-1
I have done my thesis in simple lattice design (2 replications) for 2 years. I want to do combined analysis of variance. How can do it? which statistical software? which program?
Having effect sizes from multiple time points after intervention, which is the best way to take it into account? Which statistic model can I use?
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I applied Ordinal Logistic Regression as my main statistical model, because my response variable is 7 Point-Likert Scale data.
After testing for Goodness of Fit using AIC, i got my best fit model, including 4 independent variables (3 explanatory and 1 factor variable).
However, I encounter 1 negative coefficient value (0.44 odds) of 1 explanatory variable (all explanatory variables are also 7 point Likert-scale).
My theoretical assumption is simple: the more frequency of explanatory variables (engage in activities) happen, the higher impact score on response variable (mutual understanding)
That's why I am confused when 1 independent variable has negative coefficient.
In this case, how should I interpret this IV?
Thank you very much,
I have previously conducted laboratory experiments on a photovoltaic panel under the influence of artificial soiling in order to be able to obtain the short circuit current and the open-circuit voltage data, which I analyzed later using statistical methods to draw a performance coefficient specific to this panel that expresses the percentage of the decrease in the power produced from the panel with the increase of accumulating dust. Are there any similar studies that relied on statistical analysis to measure this dust effect?
I hope I can find researchers interested in this line of research and that we can do joint work together!
I'm working on a research for developing a nonlinear model (e.g. exponential, polynomial and...) between a dependent variable (Y) and 30 independent variables ( X1, X2, ... , X30).
As you know I need to choose the best variables that have most impacts on estimating (Y).
But the question is that can I use Pearson Correlation coefficient matrix to choose the best variables?
I know that Pearson Correlation coefficient calculates the linear correlation between two variables but I want to use the variables for a nonlinear modeling ,and I don't know the other way to choose my best variables.
I used PCA (Principle Component Analysis) for reduce my variables but acceptable results were not obtained.
I used HeuristicLab software to develop Genetic Programming - based regression model and R to develop Support Vector Regression model as well.
Greetings to all those interested and eager to help.
In short: During 12 breeding seasons, my colleagues and I researched the nesting of one bird species on an area of about 11,000 hectares. We spent the first two years looking exclusively for territories/nests. We recorded a total of 34 different territories/nests. For the next ten years, we had in mind to monitor the reproductive parameters (laying dates, number of eggs, number of offspring, etc.) for all 34 territorial pairs found. However, due to the vast study area, hard mountain relief, bad weather conditions and lack of time in general, we did not visit every territorial pair every year (we did it completely randomly). So, in some years, we followed the nesting parameters in only five territories, while in others, we managed to monitor up to 20 territories and collect reproductive data. For each year collected data table contained: year, number of controlled nests, number of nests with incubation, number of successful pairs, number of fledglings, productivity and nesting success. We defined productivity as the number of fledged juveniles divided by the number of successful nesting attempts. Nesting success was defined as the number of fledged juveniles divided by the total number of nesting attempts during one calendar year. My question is whether it is possible and in what way (statistical modelling, simple formula, etc.) to express the population trend for the entire monitored population with the help of partially collected reproductive data? So is it possible to project a ten-year overall population trend based on annual productivity and nesting performance data?
Thanks in advance for the comments, suggestions and literature.
How to develop a suitable model based on problem. Give some real time example
I work with a knockout model infecting cells with cre-adenovirus to induce the deletion.
I am analyzing my qPCR results and I am not quiet certain about the best statisticalmethod to use, because I want to analyze the correlation of cre-adenoviral infection and animal genetic status (basically, I want to know how much of the gene expression is affected by cre-adenoviral infection by itself and how much is as a result of the mutation).
To evaluate that I have four groups:
- Wild-type cells not infected
- Wild-type cells cre-infected (n=3 for wild-type cells)
- Transgenic cells not infected
- Transgenic cells cre-infected (n=6 for transgenic cells)
I have tried different statistical models, but I believe I am still lacking understanding about choosing a statistical method.
PS: the reason for sample size differences was the unexpected result of gene expression being significantly affected in wild-type cells infected with cre-adenovirus - when I proved that wild-type cells were also affected, I did not have the opportunity to gather more samples.
I have a data in which the relationship between two parameters seems to fit to a model that has two oblique asymptotes. Does any one have any idea about what type of function I should use? Please find attached a screenshot of the data. I appreciate any help.
I want to read more about ordered probit model, but my searches are returning only application articles. Are there any good recomendations of texts that explain more of the theory behind it?
Any member of RG help me in applyuing statistical models for the explanation of adsorption process, like mono layer adsorption model, Double layer adsorption model and Multi-layer adsorption model and other then these another important statistical model if used please share with me. I read the article of Qun Li et al. group article that is published in chemical engineering journal in Dec 2021, Where these models are used but I am remain unable to apply these models on my adsorption articles.
The title of the article is:
"Effective adsorption of dyes on an activated carbon prepared from carboxymethyl cellulose: Experiments, characterization and advanced modelling"
I was wondering whether anyone had any suggestions as to what statistic to use (I use R Studio).
I have some muscle cell circumference measurements (response) and I have two explanatory variable columns (Control/Diet) and exercise (Yes/ No) (categorical) and I need the option to run an interaction between these explanatory variables (as I had 4 groups) as well as separately. A Bartlett's test states that its non-parametric data so does anyone have any suggestions as to what statistic/model I could use? I have looked into GLM's but as far as I can see, they don't work with interactions/ non-binomial data.
Thanks in advance for any recommendations.
I am trying to estimate the strength of the relationship between a set of independent categorical variables (coded as binary variables; 1=yes; 0=no) and a continuous dependent variable. Which statistical model would suit here?
In my database have some variables such as income and age that are described in classes ( $0 to $2,000 or 20 to 29 years old for example). However, the texts I have read more often than not use those variables as numbers, not classes. As I see, using numbers allows for a more comprehensible analysis of most methods. Should I do it?
In the example mentioned, what should I test to convert $0 to 2,000 to $1.000?
If not, is there any other conversion possible?
The work entails tracing erosion in the last 100 years. It is testing the stability of the ecosystem as soil is deposited in various sites in the catchment. What statistical components do I need to look at and what is the most fit model that I can use so that I can analyse my data well?
This work is still at formative stages hence yet to be done. The purpose of the question is to find out from other experts in this area if and how statistics can be used to improve it.
I aim to allocate subjects to four different experimental groups by means of Permuted Block Randomization, in order to get equal group sizes.
This, according to Suresh (2011, J Hum Reprod Sci) can result in groups that are not comparable with respect to important covariates. In other words: there may be significant differences between treatments with respect to subject covaraites, e.g. age, gender, education.
I want to achieve comparable groups with respect to these covaraites. This is normally achieved with stratified randomization techniques, which itself seems to be a type of block randomization with blocks being not treatment groups, but the covariate-categories, e.g. low income and high income.
Is a combination of both approaches possible and practically feasible? If there are, e.g. 5 experimental groups and 3 covariates, each with 3 different categories, randomization that aims to achieve groups balanced wrt covariates and equal in size might be complicated.
Is it possible to perform Permuted Block Randomization to treatments for each "covariate-group", e.g. for low income, and high income groups separately, in order to achieve this goal?
Thanks in advance for answers and help.
I need some research papers on this topic. can somebody help me to pin some of the documents, please
i) What kind of objective may be acheived by applying Markov chain analysis?
ii)How would be the arrangement of data?
We are aware that a shift in monsoon peak discharge may have an adverse impact on several water-based applications such as agriculture, dam operations, etc. E.g.
I want to see whether a biomarker level at baseline can be used to predict the prognosis after a treatment alone as compared to a clinical parameter?
Which statistical model will be best to investigate it?
We have been conducting one continuous camera trap survey (2019 - present) with (n=40) camera traps set up across our study site. The objective is to determine prey availability in terms of demographic classes. However, since the majority of the prey species are not identifiable to individual, we are limited to unmarked species. Furthermore, we are open to the idea of using models that require population closure but will have to violate the assumption as we are purposefully comparing prey availability between breeding and non-breeding seasons.
Thank you in advance.
Many statistical tests require approximate normality (normal distribution should be seen approximately). On the other hand, normality tests such as Kolmogorov-Smirnov and Shapiro-Wilk are sensitive to the smallest departure from a normal distribution and are generally not suitable for large sample sizes. They can not show approximate normality (Source: Applied Linear Statistical Model). In this case, the Q-Q plot can show approximately normal.
Based on what is written in the book "Applied Linear Statistical Model", a severe departure from normality is only considered, in this case, parametric tests can no longer be used. But if severe departure is not seen, parametric tests can be used.
What method do you know to detect approximate normality in addition to using a Q-Q plot?
in ASTM G102, corrosion rate is calculated using current density based on Faraday's law.
Question 1) Amplitude of current measured ACM sensor has a very large range, Should i take the average of the measured values?
Question 2) In most papers, statistical models are used for ACM sensor, why is ASTM G102 not being used?
This is my research problem so far:
In this scientific paper I will conduct an empirical investigation where the objective is to discover if the number of newly reported corona cases and deaths have been contributing towards the huge spike in volatility on the sp500 during the pandemic phase of the corona outbreak. This paper will try to answer the following questions: “Is there any evidence for significant correlation between stock market volatility on the SP500 and the newly reported number of corona cases and deaths in the US?”. “If there is significant evidence, can the surge in volatility mostly be explained by the national number of daily reported cases or was the mortality number the largest driver? “
So far i have conducted a time series object in R-studio containing the variables; VIX numbers, newly reported US corona cases and deaths. I have also converted my data into a stationary process and will later on test some assumptions. I have a total of 82 obersvations for each variable that stretches from 15. February to 15. June.
I do not have a lot of knowledge regarding all the different statistical models, and which ones that is logical to use in my case. My first thought was to implement a GARCH or OLS regression, although I am not sure if this is a smart choice or not. Hence, I ask you for some advice.
Thank you in advance :)
Best regards, stressed out student!
I have 3 constructs. I have one dependent variable with 7 categories and 30 sub-categories. I have one independent variable with 5 categories and 20 subcategories and I also have one mediator variables with six categories and no sub-category. All these categories and sub-categories are assigned the value 0 or 1( dichotomous) Besides that I have 6 control variables, four are continuous one is dichotomous, one is scale..... Which is the appropriate statistical model and appropriate tests I can use for such type of data?
The RQs are: a) What is the role of reporting structure in firm's value
What is the role of stakeholders relationship in the relation of reporting structure and firm's performance?
Just to include examples IV = Performance Category1= Financial indicators Sub-category1= profit, sub-category 2= leverage, sub-category3= liquidity
Category2 = no-financial indicators sub-category1= environmental sub-category 2= economic sub-category 3= social.
DV=Governance Category 1= Board structure sub-category1= board size sub-category2= board profile sub-category3= board's experience
Category 2= Accountability Sub-category1= no discrimination sub-category2= fairness in reporting
I am interested to do the analysis up to category level. I can change the data from dichotomous(binary) to nominal. I am thinking to use averages or percentages to analyse the relationship between variables and constructs. Can I use Partial Least Square-SEM or any other parametric or non-parametric model?
I would like to know if it is possible to use SAOMs (Stochastic actor oriented models) to analyse weighted networks?
Thank you in advance,
I was looking for best statistical model for my research, my research concerning pregnant women. Also what is the suitable sampling technique can be used?
I am working on a project, for that I want to make a statistical model. Please guide me for data and different operations used in this project.
We have very large data sets of populations across five years. We want to compare the proportion of people in different categories statistically, controlling for the differences in sample size. For example, if we have a total sample of 10,000 people in Year 1, and 17% are ages 18-24, how do we compare that 17% to the proportions of same age category in Year 2 with 15,000 total sample, Year 3 with 18,000 total sample etc.? I had assumed it would involve weighting but want to get expert opinions on approaches to this. Ultimately, the goal would be to see whether there are statistically significant differences in the proportions in each age category, controlling for differences in the total sample. Thank you in advance!
I would like to know if it is currenly possible to use temporal ERGMs (Exponential Random Graph Models) for analyzing weighted networks?
For now, it seems that software packages available to analyse TERGMs (tergm or btergm) only use binary networks.
Thanks in advance for your answer,
I want to statistically model mode choice behavior ( modes: bus, train, car, other modes). My independent variables are gender(male/female), car ownership (yes/no), age (continuous), household income( continuous), travel time, travel distance, travel cost (continuous). Along with that service quality parameters: comfort, reliability, safety etc which are ordinal data collected from questionnaire survey to be included in the model. Users has been asked to rank service quality parameters to rank in an ordinal scale of 1-5.
My dependent variable in nominal and independent variables contains nominal, ordinal, continuous variables. In order to model mode choice, which statistical method should I choose: Multi-nominal logistic regression or Ordinary Logistic Regression?
I am currently analyzing my dataset, I have obtained body weight data in insects for 7 days consecutively exposed to six different treatments in triplicates. I would like to analyze the effect of treatment over time, which model should I be using in SPSS?
Thank you so much in advance
I am trying to setup a statistical model for a timecourse experiment. I have a total of 16 timepoints *7 before and 8 after treatment. I have 4 acclimation groups before treatment. At treatment, half the individuals from each group were treated with a protein inhibitor and all individuals are treated with a stress. Following treatment, I have 8 groups (inhibitor+stress, stress for each acclimation group). I have an unequal amount of measurements from each group at each time due to mortality and low quality data. This is not a repeated measures as each measurement is from a unique individual that was sacrificed. My data is non-normal as well possibly due to missing and low quality data. I read that I can use the average of each group to make up for the missing data points.
I have had great trouble trying to get each timepoint integrated in my model. I have tried analyzing by averaging all "before" and "after" timepoints for each group but it would be great to get results at higher resolution (point of the timecourse). I am using JMP but open to trying another program.
Any help you can provide here or point in a direction would be greatly appreciated!
*I forgot to add that I am missing data for a timepoint and some of the treatments do not have data for others.
I am currently building statistical models in Health care, education and Agriculture. and looking for someone who is willing to share primary data sets in these thematic areas. Support and or suggestions will be very much appreciated.
I have generated so many models having a different coefficient of determination (r2) and RMSE. In this analysis, it has been seen that it is not mandatory that a model having maximum r2 has a minimum RMSE. It is very difficult for me to choose a model among them. I just want your suggestion. Which model should I choose? Should I go with r2 or RMSE?
Does anyone have an example to share on a statistical model that incorporates temporal and spatial autocorrelation terms simultaneously?
Examples in ecology and hydrology research would be optimal. Thanks in advance.
For a research I am trying to compare google trends search volume series for two online retailers . However, as you migth know, Google Trends returns you a normalized search index value for a spesific time period and does not reflect real search volumes. So in my thinking, while estimating statistical models, this issue creates an important problem. Do you also think if a model includes more than one google trends variable, they should be weigthed? After that issue wiegthing could also be a problem too.
I have behavioral data (feeding latency) which is the dependent variable. There are 4 populations from which the behavioral data is collected. So population becomes a random effect. I have various environmental parameters like dissolved oxygen, water velocity, temperature, fish diversity index, habitat complexity etc. as the independent variables (continuous). I want to see which of these variables or combination of variables will have significant effect on the behavior.
Within the statistical process, there is a model (Mahalanobis Distance) that is used to support the regression. When is the ratio good on this scale? And why most Arab studies do not rely on it and do not refer to it. But most foreign studies rely on it to support the results of linear and multiple regression. Is it possible to clarify it? What is the best reliable percentage, as high and low values appear in spss?
While running Multinomial logistic regression in spss an error displaying in parameter estimate table. For Wald statistics some item value is missing because of the zero standard error and displaying a message below this table "Floating point overflow occurred while computing this statistic. Its value is therefore set to system missing". Does anyone know how to resolve this error?
My paper is looking at the most significant determinants of u.k. economic growth. I will look at macroeconomic , technological, human capital, socio-geographical and governance variables against the dependent variable of GDP growth. Anyone who has done research into growth-theories could suggest suitable statistical models which can be used on stata? I am looking at Baynesian Model Averaging currently but not sure that is the best model out there
Statistical modeling based on the parameters affecting the characteristics , is it viable model to predict concrete properties based on the parameters
Which error families are allowed in generalized least squares (gls) models? Can I have, for example, a binomial glm and define a covariance structure (which, I guess, makes it a gls) in it (see below example)?
model <- glmmTMB(response ~ ar1(predictor + 0 | group), data = data, family = binomial(link = logit))
And also, should I call a glm with a covariance structure a gls?
When I have to choose my identification variable in lvl 1 and lvl 2, I cannot choose it because it is grayed out. Do you know why ? and how to fix it?
I have run a three-factor mixed-design experiment with one between-subjects factor (biological sex: two levels) and two within-subjects factors (having four levels each). I have measured several continuous response variables, each of which I have already analysed with a standard ANOVA. I have also collected the values of a nominal (non-ordinal) categorical response variable. This response variable is non-binary (it takes one out of five possible values).
The question is: How to approach the statistical analysis of a three-factor mixed-design experiment with a non-binary non-ordinal categorical response variable?
In particular, I would like to be able to analyse main effects and interactions as in a standard ANOVA.
Any reference to a related R package would be more than welcome.
Accurate forecast can’t be possible by ARIMA model, generally used for forecasting.
In an exploratory study, If I want to state that certain components of counselling (7 items to assess), environmental modification (8 items) and therapeutic interventions ( 8 items) results in the practice of social case work, what analysis should I do?
NB: we have no items to assess practice of social work. Instead we want to state the practice of the other three components results in practice of social case work.
I have been recently studying proprietary voter files and data. While I know that voter files are for the most part public (paid or free), I am confused as to how companies match this data to other data.
For example, the voter files (public) never reveal who you voted for, or your behavioral attributes, and so on. So how do companies that sell this "enhanced" data match every "John Smith" to other data. How can they say that they have a profile on every voter? Wouldn't that require huge data collection? or are there models that simply do that job for them?
In the November issue of the Journal of Conflict Resolution (https://tinyurl.com/tyhrn2j) Adrian Lucardi and I, debate with Bunce, Wolchik, Hale, Houle, Kayser, and Weyland, about whether democracy protest diffuse? We find across thousands of statistical models, that the don't between 1989-2000, in general? BW and W suggest that they might in very unusual circumstances.
What are your thoughts? In what situations do you think they might spread? Why, despite all the protests occurring today in close succession of each other, is no one is talking about protest diffusion?
Minitab software is used to fit model to experimental data. I go to Stay>DOE>Response surface and choose regressors in uncoded versions, as well as their lower and higher values, then responses at the same time. As far as I know the unusual observations and large residuals points must be totally excluded from data and RSM must be performed again with new data. However, right after I remove these points and rub RSM, the results show new unusual observations and large residuals, and again I remove them. The same thing happens which leads to an undesirable reduction in data. So, what must be done with unusual observations and large residuals?
If I have two parameters, A and B, is it always better to include A*A*B and A*B*B, or just A*B is enough. Generally, what is the basis for choice, does it depend on the number of factors, particularly in Minitab software. In response surface methodology, for example, software itself defines the terms, but in regression>fit model it is possible to include additional terms.
I am looking for statistical models (or results of relevant numerical simulations) to estimate the number of different types of contacts between particles in a powder mixture. The simplest case would be a mixture of particles of type A and B, both spherical and of same uniform size.
The goal would be to calculate the number of A-A, B-B and A-B contacts per unit volume.
More complex cases would include size and shape distributions (which may or may not differ between the components) and mixtures with more than two components.
Any relevant references are highly appreciated! Thanks in advance.
There are some works in literature in which selectivity models are developed in Minitab software by response surface methodology based on the data that have been collected by other researchers and optimization is performed to propose optimal conditions to maximize the selectivity for desirable products. I take the same data just to check how response surface methodology works, but I never get the same model that have been obtained in this works. The coefficients in the model for regressors and their interactions don't match. Even in such cases that R square and adjusted R square values are the same. Even in the case that I check some data points withy model and get the same responses as they get, but the coefficients are totally different. I don't know exactly whether I miss something or I don't use the software rightly or something else. I want to note that, in Minitab I go to Stay>DOE>Response surface and choose regressors in uncoded versions, as well as their lower and higher values, then responses at the same time. I think the problem might be in coded variables, are uncoded variables converted into coded ones in the same way, or it might be done differently? Or the problem might be with the way I interpret the results after I run RSM. What do you think, is the problem related to it or any other factors?
I am interested in literature or research-results connecting Technology Entrepreneurship (either as a general concept or as a discrete variable) with Employment in high technology firms, (as a concept or discrete measurable variable). I am looking to statistically correlate the two variables, creating a whole statistical model.
If you want to estimate a sample size for a qualitative research in an unknown population, what is the suitable statistical model to use?
which statistical model is suitable to compare two curves for validation purposes, when comparing two force-displacement curves for instance?
I plan to carry out a survey research that will model a certain variable with other pre-specified variables. As this is not an intervention study, I am not obliged to register the study to such a database as clinicaltrials.gov. However, I plan to do that in order to increase transparency of my research. Particularly, I would like to make all the tested variables prospectively revealed in order to make the statistical modelling more valid.
Do you consider my plan correct? Would you give me any further tips on how to increase transparency of my study?
I have a problem regarding an analysis of a model in SPSS. The model consists of two IV (X and A), one Mediator (M) and my dependent Variable (Y). M is only a mediator for X though, not for A.
I used the Macro of Hayes (Model 4) to calculate my Model. However, here I can only put in A as a covariate, thus also establishing a mediator effect between A and M. To solve this, I redefined the Matrix of A, leaving my syntax as " process y=Y/m=M/x=X/total=1/cov=A/effsize=1/model=4/cmatrix=0,1."
Here is the twist: Process does not specify the total effect model (
"NOTE: Total effect model and estimate generated only when all covariates are specified in all models of M and Y."), therefore giving me no total of Rsquare of my complete model.
Is there any way I can get this result while using SPSS?
Our research group on mental healthcare research is frequently facing the challenge to evaluate complex mental healthcare interventions. I am interested in sharing experiences and discussing methodological issues about such research projects. Maybe write a review on this subject?
I am studying customer behavior towards buying products from a specific company.
The company has three different products, Y1, Y2, Y3. These are agricultural inputs and coded as 0,1( buying or not). the company is interested in increasing it selling through existing customers. Currently, interest is to compute probability that if A is buying Y1 can buy Y2 and/or Y3.
My thoughts saying that estimate three logistics models, one for each product.
Y1=f(Y2,Y3, Xi) -----------------Xi are continuous variables.
y2=f(y1 y3 xi)
y3= f(y1 y2 xi)
Is it fine? else what you suggest?
Can i test correlation between these models, as in case of SUR?
Can we estimate like SUR?
Any supporting literature please.
I am looking for the correct model available within SPSS. I have measurements of a metabolite (some of which are negative) and need to determine if this is associate with a systolic blood pressure measurement. Then if there is an association does this stand-up when adjusting for age, sex and BMI? Finally, I need the appropriate model to determine if the metabolite level is associated to hypertensive cases (yes/no).
Thanks in advance.
we usually writing limit (theta) of the wrapped exponential density following way
Is there any problem if I change the limit into (0,2pi] ?
Because I have seen that, the important characteristic that differentiates circular data from data measured on a linear scale is its wrap-around nature with no maximum or minimum
I performed a neuro-psychological battery consisting of 6 tests that measures different aspects of executive function. Each test has between 3 to 15 subscores. There is no consensus on whether one test or subscore is better than the other. There are a total of 64 subscores which are all non-normally distributed.
In this condition, is it appropriate to compare patients and controls using 64 univariate comparison using Mann whitney U and then perform Bonferroni correction or is there a more appropriate statistical model that I can use?
In my research I focus in paired correlated binary data. To accommodate the intraclass correlation, I choose to adopt a certain model without providing much theoretical support, and was criticized about it.
Currently I have been reading relevant goodness-of-fit testing papers, but I am uncertain whether it would be adequate to support the decision of my model choosing; Where should I be looking for? Is there anything I should be aware of? Thank you.