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.

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16 views

Sample size for multinomial distribution

Suppose we have multinomial distribution with $k$ outcomes having the same probability $1/k$. What sample size do we need to guarantee with the probability $95\%$ that $m$ of the oucomes occur at ...
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12 views

Is it possible to scale categorical variable before calculating a distance matrix?

My ultimate goal is to find similar customers by comparing characteristics of non-customers to existing customers. The characteristics are mostly non-numeric. My hope was to scale(data) and then ...
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12 views

How to deal with interaction between several dummy variables and one continuous variables in one or two regression models?

I want to know the relationship between revenue and cost in several conditions. Dependent variables is REVENUE.REVENUE is a continuous variable from 0~1000+.I have several key independent variables: ...
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2answers
27 views

Relationship between categorical factors

I am not sure what this is called in English, but if we have two categorical factors, we can say that one of them (A) is finer than the other (B) if it holds true that if two observations belong to ...
0
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11 views

categorical weighted vector [on hold]

I have 4 variables Each variable is an ordered categorical variable The range of values for each variable is ...
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0answers
12 views

Significance across Categories of Quantitiative Data

I have a quantitative independent variable that has been grouped into categories (A-G). Example: Age of people by decades (20s, 30s, 40s, etc.) I want to determine if the difference between the ...
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0answers
30 views

errors in the orthogonal factor model [on hold]

I am using R psych package in order to design a factor model. By default, an oblique rotation (oblimin) is performed by fa. The number of factors (fa.parallel$nfact) has been previously estimated. ...
1
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1answer
18 views

Is least square dummy variable model better than random effects model?

I have a panel dataset with one dependent and twelve independent variables. There are 50 individuals with data for 100 days. Theoretically, most of them should be significant. First, I checked for ...
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0answers
9 views

Not sure how to model some probabilities on web traffic

So, I have a dataset of web site visits (a tidy one, thankfully), of which the most important columns are user_id and page. I have a training set for the first 11 months of the year, and a test set ...
0
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1answer
21 views

Difficulties fitting a Cox PH model with categorical interactions to complex survey data

I'm attempting to fit a Cox proportional hazards model to a set of NHANES data; the code to load and clean the data is here, and the resulting dataset is here. The difficulty I'm having is ...
0
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0answers
14 views

orthogonal factor model

I have a matrix of data that is clustered in several sets of variables (X1,...,Xn). In order to define these clusters I have used hierarchical clustering to get the desired number of clusters and ...
0
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0answers
28 views

which is the most sutible technique to detect outliers? [on hold]

i know a technique to detect outliers: 1- make a model & calculate residual for each data point 2- delete the top 10% residuals from the data 3- fit the data again that's fine but this leads ...
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0answers
8 views

Can variables from different samples be factor analysed?

The TL;DR of my problem is this: Can I perform a factor analysis include variables from different sources (but measuring the same concept)? I have a data set which measures leadership behaviour and ...
3
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1answer
27 views

Difference between languages (spoken)?

I'm trying to perform a hierarchical clustering, to aggregate some "zones" or neighborhoods of a city, based on the language that is used most in that zone In order to do so, I have at hand a dataset ...
0
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0answers
12 views

Interpreting the entropy of a Dirichlet distribution

I was looking for a measure to interpret the "spikiness" of categorical histograms. So, if it becomes unnaturally skewed towards a certain value at a given time, I want a metric that will show some ...
0
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0answers
20 views

How to combine two categorical variables? [closed]

I need to make new variable combining two categorical variables using PASW statistics. Both variables have "yes" and "no" answers."yes"=1, "no"=0 My new variable has to have 4 categories: 0= for ...
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2answers
49 views

How to proceed with chi square test in R if one line has zeros?

I am trying to program with R. My categorical data is in a table of 5*5. However, the 5th line has only zeros. When I run the chi square test in a software program, it returns the result INVALID. But ...
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0answers
7 views

subsample approach versus dummy variables, Fama MacBeth (1973) procedure

I am running an asset pricing test (Fama MacBeth); regressing six month ahead excess stock returns on past six month return (momentum) and a number of control variables (B/M, Size etc). I have run my ...
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1answer
80 views
+50

Categorizing Continuous Random Variable in Logistic Regression

I have a Bernoulli response variable and I am going to fit a logistic regression. One of my independent variables is a continuous random variable and I would like to categorize it before fitting the ...
0
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1answer
11 views

sub sample versus indicator variables (multiple regression)

In my study I have an continuous dependent variable (return) regressed on an independent continuous variable x1 (momentum) and a number of control variables. I am currently investigating whether this ...
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0answers
9 views

How to interpret linear regression with different categorical variables

I am looking at the effect of 1 categorical dependent variable A (3 outcomes) on 1 independent variable. This is significant. Then I want to see if this effect is still true if I add another ...
0
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0answers
10 views

R reads numerical values as factors [migrated]

In R3.1.2, I have uploaded a dataset. My numerical values, however, reads as Factors. By searching online, I found that this code should change my values from factors to numerical: ...
3
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1answer
58 views

> 1 interaction variable, single regression versus multiple regressions

In my study I have an independent continuous variable x1 (momentum) and four dummy variables D1 D2 D3 D4 which indicate industry type. I am investigating the four interaction variables between the ...
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1answer
29 views

Logistic regression categorical variable interpretation after transformed into dummy variable

Before training a glm model (in R), predictors were transformed into matrix and highly correlated/near zero variance variables were excluded: ...
0
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1answer
11 views

how to disentangle the influence of two correlated dummy variables

I am analyzing the effect of two factors on performance in an easily measured test. The two independent variables are "category" variables, let's call them "strategy" and "manager". Each test result ...
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1answer
48 views

Linear regression with unbalanced dummy variables + not normally distributed residuals

I am conducting a multiple linear regression analysis in SPSS. My DV is a score between 0 and 6, and my predictors are: one dichotomous nominal variable (native vs. non-native speakers) one ...
0
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0answers
9 views

How can convergence (in distribution) be assessed in the context of multiple imputation by chained equations?

The MICE algorithm starts by randomly imputing the missing values in a dataset, and then proceeds to predict the missing values in each variable by modeling the relationship between the non-missing ...
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0answers
30 views

Including 2-way interaction dummy variables in binary logistic regression

I am trying to do a binary logistic regression by including 2-way interaction where I have two drug dosages types each taking two levels (high and low) and dependent variable is categorical cancer ...
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0answers
15 views

What to do with highly unequal sample sizes in a dummy linear regression? (+ nonnormal distribution)

I am conducting a multiple linear regression analysis in SPSS. My DV is continuous (score between 0 and 6), and my predictors are: one dichotomous nominal variable (native vs. non-native speakers) ...
3
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0answers
17 views

Confidence interval for odds ratio with differents results

Find the confidence interval for the odds ratio, where $OR=e^{\hat{B_1}}=3.5701$, $\hat{B_1}=1.2726$, $\sigma(\hat{B_1})=0.5016$ with 95% confidence. First I followed the idea from notes that ...
0
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1answer
15 views

How to deal with quasi-continuous features?

I've searched around a bit for strategies to approach the problem I'm facing and haven't come up with much. I'm working with a data set that has many "quasi-continuous" features. That is, the ...
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0answers
11 views

Leverages and effect of leverage points

I just got some question about the hat matrix in linear models. My first question is Why in a balanced one-way layout $(n_1=...=n_c=n_0)$, all leverages $h_{ii}$ have the same value $\frac{1}{n_0}$? ...
0
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0answers
10 views

Decision Tree with skewed input or missing data

Looking for guidance here. What say I have 100K training records and one of the categories/features has 99K same values and the other 1K of another value. E.g. 99K male and 1K female for gender, as ...
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1answer
25 views

Treatment of categorical data in R [closed]

I work most of my time with categorical data (predictors and outcome), I usually do a trees in SPSS to make groups and rank which groups are more predominant to buy / not buy. But now I'm into R, and ...
0
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0answers
9 views

Replacing NA columns with Median in R [migrated]

I keep getting errors with the codes, which would be correct?
0
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1answer
28 views

From interaction model to additive model

I have two factors, and I've fitted a interaction model in R with $lm( \sim factor1*factor2)$. The parameters belonging to the interactions between the two factors are all non-significant (p-values ...
0
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0answers
13 views

Can you compare the means of two nonindependent sets of data that are not paired?

I am doing within-group comparisons of monkey movement behaviour (e.g., daily path length, home range size) because I want to determine if their behaviour is different in the dry versus the wet ...
0
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1answer
25 views

Categorical factors in normal linear model

If I have two factors, $A$ with 2 levels, and $B$ with 3 levels, what should my base model be if I want to test if there is an interaction between the two factors? Do I choose the interaction model ...
0
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0answers
9 views

Misunderstanding/Confusion: product of factors in linear model

Is the normal linear model based on the factor $F_1\times F_2$ the same as the interactions model? I do not understand. Clearly, the product factor is the factor with levels like $(f_1,f_2)$. So, if ...
0
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1answer
60 views

What's the interpretation of `lm( y ~ x*z)`? [closed]

My intuitive understanding is that if $x$ and $z$ are categorical factors, then each observation $y_i$ is given a mean value which is equal to the mean value given to $y_j$ if $y_i$ and $y_j$ belong ...
0
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1answer
22 views

Convert categorical data with large number of levels to numeric data and what kind of mapping to use

I have a dataset with 20,000 rows and 11 columns. Out of the 11 columns 10 are categorical. Out of the 10, three have very large number of levels. i.e. levels >60. One of the variable is basically ...
0
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0answers
15 views

interpretation of coefficients in regression with categorical, continuous predictors

I am having trouble with interpretation of regression coefficients when more than one categorical variable is included. Also, I am not clear on how including interaction terms changes the coefficient ...
0
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0answers
8 views

Categorial Variables for a MNL model using mlogit on R

I have to build a multinomial logit model of transportation mode choice. Here is the data set I have to study: CASENUM is the ID of the respondent. Each respondent faces between 2 and 4 ...
1
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2answers
55 views

Product factor in R. What's the interpretation?

So I am reading an R guide which tells me that the product factor $B \times T$ is implemented in $R$ by using the $*$-operator on the factors after $\sim$. However, when I check the model matrix for ...
0
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0answers
26 views

How to evaluate the combined effect of 2 variables?

I'm trying to analyse the effect of smoking on health. The health variable is binary(healthy or not) but there are 2 different exposures to smoking(active=variable A and passive=variable B). Variable ...
-1
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0answers
8 views

Treatment of this particular dummy variable

I have these two dummy variables and a continuous variable in my dataset and I was wondering how I should include them in my model? dad10: A dummy variable = 1 if the individual lived with their dad ...
0
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0answers
12 views

How to recover the value of the coefficient of an omitted dummy variable?

Suppose I have a regression given by $S = \beta_0 + \beta_1X + \beta_2Y + \beta_3Z$ with dummy variables $X,Y,Z$ and $X + Y + Z = 1$. I plug into Stata or whatever and obtain estimates ...
0
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1answer
39 views

Survival analysis with multiple factors

I want to do survival analysis in a situation where I expect the survival time depends on two factors: Environment. Each person is in one of three environments, $E_1,E_2,E_3$. I expect that the ...
0
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0answers
6 views

Comparing homogeneity of variance and t-test

I have categorical variable with 2 levels having more than 1 response variables for Shapiro-Wilk test? I tried shapiro.test(abdata$Conductivity), how do I prepare ...
2
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0answers
16 views

How to set sum contrasts for unbalanced factors

Let's say that I have a model where the response time depends on accuracy (0/1, coded either as categorical or numerical) and another categorical variable (pres: idem/diff), both interacting with the ...