Categorical data can take on a limited (usually fixed) number of possible values called categories. Categorical values "label", they do not "measure". Nominal and dichotomous/binary scale types are categorical. Some people consider ordinal scale categorical too.

learn more… | top users | synonyms (3)

0
votes
0answers
8 views

Sample size when fitting categorical survey data

I have a model which fits data from repeated surveys: at time $t$, a number $n_t$ respondents is asked a question and can give one of $K$ answers ($k=1, ..., K$). This is repeated $T$ times ($t = 1, ...
0
votes
1answer
23 views

categorical data normalization in SVM Classification [on hold]

I have a set of features (contineous + categorical)...I have converted the different categories to numerical, for example (object1, object2, object3) = (1,2,3)..etc. and ran SVM... I obtain high SVM ...
0
votes
0answers
18 views

Linear Probability Model Construction

I am dealing with a questionnaire consisting of 15 questions. I performed a factor analysis to all the questions and it has given three factors each consisting of 5, 7 and 3 numbers of questions ...
0
votes
0answers
15 views

maximum likelihood for count data [on hold]

Can maximum likelihood estimation method of parameter estimation be used in categorical data? I was looking for some examples of it. What other parameter estimation techniques work for this kind of ...
5
votes
1answer
35 views

How to deal with factors with rare levels in cross-validation?

Suppose in a regression analysis in R, I have a factor type independent variable with 3 levels in my train dataset. But in the test data set that same factor variable has 5 levels. Therefore I can not ...
0
votes
0answers
14 views
0
votes
1answer
20 views

Any reason to report a Chi Square Test of Independence on a 2×2 table when I'm already reporting a 95% CI on the odds ratio?

I'm analyzing a 2×2 contingency table, and am going to a report a 95% CI on the Odds Ratio. Is there any point in prefacing this with a Chi Square Test of Independence? I understand that if the Odds ...
1
vote
0answers
13 views

Prediction model on hybrid data

I am currently working with a data set where I have both continuous, discrete and categorical (without any order) data. And I have to predict a continuous data. To be concrete, my problem is a ...
0
votes
0answers
17 views

Stat test to use with nominal variables

I have a research question looking at whether one level of a nominal variable is more likely to elicit an action than another. We are using a control and experimental group so our IV is either ...
0
votes
0answers
30 views

Correlation with categorical variables - Interpretation of aov()

I want to know if one (or more) out of three categorical variables (season of measurement, geology, grazing) influence the numerical variable (spread of a plant). Sure, I read the answers here ...
0
votes
0answers
33 views

interpreting a 3 way interaction between categorical variables with mixed model with random effect

I have ran a glmer analysis with R. This question have been comment regarding three way interaction for ANOVA models. this is clearly not the same thing as a general linear mixed model first because ...
2
votes
0answers
28 views
+50

How to use information about likelihood of classes in a classifier?

General question: How can information about the likelihood of classes be used to improve a classifier? Suppose that the probability of each class is known quite precisely (from a very large sample), ...
1
vote
0answers
37 views

Removing additive effect in Multiple Regression in R

I have this data set that I will used for my model ...
2
votes
1answer
44 views

Factor analysis to remove noise

I would like to perform factor analysis/PCA to remove potential hidden latent variables from methylation data that would be due to noise/measurement error and batch effects. However, the variable i ...
0
votes
0answers
15 views

Separating the Intercept in many Dummy Variables in Multiple Regression in R

I did a multiple regression on a dummy variable using R about how much people will pay on a certain product. Given this variables and levels: ...
0
votes
0answers
10 views

Analyzing whether several categorical variables might have a causal relationship with a dependent variable

I am trying to use some data to assess why certain terrorist organizations claim responsibility for their attacks and why sometimes they don't. I have a data set that contains relevant information so ...
0
votes
0answers
12 views

Finding the underlying pdf by sampling “buckets” of values

The situation I'm looking at a system where I can perform the following trial: $N$ samples are randomly taken from a population of $V$ different values (which can be treated as categorical), ...
1
vote
0answers
15 views

Apply trained MDS model to new data

I have both a distance matrix and the original vectors, and am using MDS (Multidimensional Scaling) with R to generate vectors in more dimensions for the data. With dimensionality reduction (for ...
3
votes
0answers
36 views

OLS with ordinal dependent variable - do the coefficients mean anything?

I currently read a paper in which the author has asked people 3 different questions regarding their life satisfaction, all of which are to be rated on a four point scale: 1) very low, 2) low, 3) high, ...
0
votes
0answers
20 views

Linear Logistic Regression where Dummy variable is strictly dependent of other covariates

I want to do a logistic regression for something being granted or not. In this I have independent variable that is dependent of a dummy variable. For example, I want to regress ...
1
vote
2answers
37 views

multiple linear regression with interactive categorical variables

I want to include in a multiple linear regression model, the interaction between categorical variables. I have three categorical variables: CO2 (0,1) Temperature (0,1) Soil (1,2,3) But when i ...
0
votes
0answers
21 views

Comparing Binomial Success Parameters in a Stratified Approach - An Example in Biostatistics

I would like to contrast the effectiveness of drug treatment and surgical treatment in a study with the following data. Each row represents one trial, and each trial uses either drugs or surgeries to ...
0
votes
1answer
37 views

Season dummies in R [closed]

I have heating power data from one year (8670 observations). I also have regressors for day length and temperature (8670 observations also). I would like to add seasonality with 24h (1 day) 168h (1 ...
8
votes
4answers
294 views

Develop a statistical test to distinguish two products

I have a data set from a customer survey, I want to deploy a statistical test to see whether there is significance difference between product 1 and product 2. Here is a data set of customers' ...
2
votes
1answer
26 views

Dummy variables for people and time

I have panel data for people over a number of years. People can be categorised based on a certain characteristic, X. Individual's yearly observations can be categorised based on whether a particular ...
1
vote
1answer
25 views

Dummy Variable in OLS regression

I would like to include in my OLS regression a dummy variable with two categories (d=0,d=1)and n=75. When the dummy takes the value of 1, it refers to 19 observations of the 75. Does it matter for my ...
3
votes
1answer
25 views

categorical data: design of the analysis

There is a dataset of about 8500 different kinds of mushrooms, each datapoint has about 20 features. The features are purely categorical: color of the cap, its shape and so on. None of them are ...
1
vote
0answers
19 views

Q-Methodology: which correlation coefficient to use: Pearson vs Spearman vs Kendall

Please note: This question pertains to Q Methodology, a research method used to study people's subjectivity. Q embodies ontological and epistemological assumptions that sometimes differ markedly ...
0
votes
0answers
45 views

how to regress with dummy variables only

im stuck here. i have this model and i"m wondering what model to use to predict whether one is poor or not. someone told me that logit doesn't work here where all explanatory variables are dummy ...
0
votes
1answer
19 views

time series based classification

I want to classify some data. Basically the data is time series in nature. The target variable is categorical. I know there are so many algorithms for predicting the time series model. However, I have ...
0
votes
1answer
29 views

VAR Impulse response with dummies

I have dummy variables (DV) which measure policy reforms (e.g. Independence of the judiciary, barriers-to-entry in a market etc.). These can be either “0,1” or, say, “0,1,2,….. upper”. Say I have a ...
0
votes
2answers
65 views

How to compare nested factor levels to mean, not to first level, and how to test the last nested level in R lm()

I have two factors j (with 3 levels A, B, C) and k (with 3 levels M, N, O), with k nested within j. Level A of j is the reference level and it has only one k level, M, in it. What I want to test ...
2
votes
1answer
158 views

A framework for multi-valued categorical attributes

In the scenario in which I'm working each entity could be represented in terms of N distinct properties that I will call p1, p2, ..., pn. For each of them, an entity, can have its specific range of ...
0
votes
0answers
18 views

Practical Application of the Use of Multiple Linear Regression with factor Scores

I have regressed ML equations using factor scores. I obtained two equations for two strains of chicken. Now how can the equations be of benefit to Farmers for weighing their birds, bearing in mind ...
3
votes
2answers
59 views

Are (some) time dummies redundant if another variable controls for a part of the sample period?

For an OLS regression, on the one hand, I have a dummy variable for each sample year (from 2000 to 2012). On the other hand, I have a binary variable that is 1 if observations refer to a concrete part ...
3
votes
2answers
95 views

R seasonal time series

I use the decompose function in R and come up with the 3 components of my monthly time series (trend, seasonal and random). If I ...
0
votes
1answer
24 views

Categorical variable coding to compare all levels to all levels

I am trying to determine the best coding system for my categorical variables to use in a regression with categorical and continuous variables. I have been using this page as a resource but none of the ...
0
votes
0answers
8 views

Identifying the significant properties

(The problem is in linguistics.) I have a list of vowels from various words, some of which underwent a change, and for each of them a list of phonetic properties. I believe that the mechanism is this: ...
1
vote
1answer
22 views

Can I perform a chi-squared analysis with a categorical variable and the means of a continuous variable?

I have one variable that is categorical (with 5 levels), and another variable that consists of either mean or sum scores (0 to 28) due to the way you have to calculate the scores of this measure. Can ...
2
votes
0answers
17 views

How do I find the odds ratio using the output of a loglinear model?

This was a homework problem from Agresti (7.2) that I didn't get, even after looking at my class' solutions. Help? So we are given the output of a loglinear regression ($λ_{ij}^{XY}$). The output is: ...
2
votes
0answers
28 views

Distance between independent observations of a categorical variable

I have a random variable $T: \{ \text{blue}, \text{green}, \text{red} \} \rightarrow [0,1]$ and a number of observations of $T$: ...
0
votes
0answers
16 views

Kriging, Gaussian Processes with categorical data

Theoretically it is possible to use Kriging also for categorical features by using a kernel function which supports factors. Does anybody know some references on this topic or whether they are ...
0
votes
1answer
26 views

Is Chi-square the right test to use when comparing multiple proportions

In my analysis, I have 4 groups: FLU-P (symptoms with virus) FLU-N (symptoms without virus) HCY-P (healthy with virus) HCY-N (healthy without virus) I am looking at the level of antibody ...
0
votes
0answers
18 views

PCA on Wine data with only one binary data(white/red wine) and other quantitative data

I am working on wine data with the following format: ...
1
vote
3answers
172 views

Is it advisable to drop certain levels of a categorical variable? [duplicate]

Let's say that I have one categorical variable with six levels, and I then create five indicator variables in order to represent the six levels. If two of the five variables are insignificant, then do ...
0
votes
0answers
6 views

Fixed time effect vs variable controlling for a part of the sample period

To control for time fixed effect, I have included in my model a dummy variable for each year. However, since some years of the sample period are especially relevant (those corresponding to the ...
0
votes
0answers
16 views

VIF & CI in Regression with Dummies and Interaction Terms

I am checking my dataset for multicollinearity using VIF and condition indices(CI).My dataset is cross-sectional macroeconomics data (n=75). I have six independent variables (x1,x2,x3,x4,x5,x6) plus ...
1
vote
0answers
23 views

Multiple t tests or an ANOVA?

I have low anxiety and high anxiety participants (21 in high anxiety and 20 In low). I measured anxiety and then categorised Pts according to this. They completed two memory tasks. In one memory ...
0
votes
0answers
28 views

Interactions Dummies with an Independent Variable

In my model I want to include two dummies ($d_1$,$d_2$) and also the interaction effects of these two dummies with another independent variable, $x_1$. The interaction terms are $x_1\cdot d_1$,$x_1 ...
0
votes
0answers
29 views

Multicollinearity as interaction terms added: Separate or common analysis?

Using OLS, the starting aim of my analysis is to study how different types of credit card contracts affect the dependent variable (y: use of credit cards). I have generated three dummies (v1, v2, v3) ...