# Tagged Questions

Prediction is concerned with assessing the probability of unknown values from known values and inferred relationships.

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I am facing a problem with mobile-application data. I want to build a model to predict the number of daily downloads of an application on the App Store, using as predictors the daily rankings of the ...
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### Event prediction through path analysis?

I have the following problem: I want to predict an event's occurrence by investigating the steps a user goes through. Fe. I want to analyse which webpages a user visited that lead him to buy or not ...
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### Time interval prediction

I want to predict a specific time interval(ex. patient processing time in clnic) with some boolean value like whether this patient has cough or whether he/she has certain disease. I have tried using ...
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### Soft Question - Practical Statistcs: What to learn, why learn it and how? [on hold]

BEGINNING: I want to ask a few soft questions about statistics. I understand that maybe/probably some/all off these questions cannot be answered categorically but I would like to try and get some ...
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### How to predict the risk of an event?

I'm working on a medical problem, where I want to analyze the effect of taking cholesterol medications on the occurrence of heart attack. Once a medication with a specific dosage is prescribed, it'll ...
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### Probability of a single real-life future event: What does it mean when they say that “Hillary has a 75% chance of winning”?

As the election is a one time event, it is not an experiment that can be repeated. So exactly what does the statement "Hillary has a 75% chance of winning" technically mean? I am seeking a ...
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### Purposeful model building for prediction and inference

What are some of the best practices and steps to building models for prediction and or inferences? What have been taught to me during my classes was the steps outlined in Chapter 4 of Hosmer et al. ...
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### How to calculate probability of success based on prior p-value

The FDA often requires a sponsor to conduct multiple clinical trials prior to approval. Given the following observations in a ph2 and ph3 trial, how would you go about predicting the probability of ...
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### Classification(Machine Learning) or Survival Analysis

I am working on building prediction model for disk failures (time taken to occur a disk failure and what parameters could strongly affect disk failures). I am bit confused on- What data ...
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### Some help to get started with Spatio-temporal data analysis on different engines

I will soon receive experimental data from $n$ engines ($n$ is small, say, 10) for sensor data at the locations in figure: As you can see, each engine is equipped with $5\times8=40$ sensors, ...
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### Sample size needed for prediction modeling/validation with logistic regression

I have a dataset with about 30 potential predictors and 115 observations. I'm looking into building a prediction model with the data using logistic regression. From what I have read - the typical ...
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### How to apply Box Cox to train and test data?

I am trying to standardize my data to performing prediction on it. Some of the features in my data are skewed and hence I am applying Box Cox transformation to reduce skewness. My data also ...
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### Prediction Algorithms in Statistics

Here it is what I'm looking for: Having a random variable, with an unknown distribution of its values, is there any smart algorithm that can predict the next value of the variable based on n samples ...
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### Time series prediction using ARIMA

I have a dataset which contains data from a sensor for every 5 minutes and am trying to predict for example 10 future values based on the first 500 values. My data looks like the following and could ...
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### R: Using Poisson statistics to predict terrorist attacks in France [closed]

I am a French data analyst and am pretty certain that a statistical description of the terrorist attacks would be possible, and even useful. As "rare" events, I would choose the Poisson probability ...
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### Is it accepted to fit model with standardized data and predict on non-standardized data? [duplicate]

If you standardize your training data, then can it work on unstandardized data during predictions accurately? Many algorithms require the feature data to be standardized and I am wondering how/why/...
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### Predicition approaches: is there a possibility to derive the specificity, sensitivity and accuracy of a combined approach?

Consider different prediction approaches classifying each sample of a set of input samples into two categories, e.g. positive and negative. Sensitivity, specificity, precision and accuracy of all ...
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### Use Conditional Random Field (CRF) to predict the stock price of the next time stamp

I have a dataset consist of twitter sentiment score at any past time stamp t for a stock $s$, denoted as $\{x_{s,0}, x_{s,1}, ..., x_{s,t-1}\}$. Also, I know the corresponding stock price \$\{y_{s,0}, ...
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### How does a LOESS model do its prediction?

I understand the theory behind LOESS, but how does it do prediction without coefficients? I'd like to use LOESS prediction, but need to be able to explain it.
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### Using RNN (LSTM) for predicting one feature value of a time series

I have been reading several papers, articles and blog posts about RNNs (LSTM specifically) and how we can use them to do time series prediction. In almost all examples and codes I have found, the ...
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### Predicted values in negative binomial model with 0-inflation

My count data are zero-inflated, for which I utilized glmmADMB. ...
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### Model for predicting Gentrification

I am looking for a way to design a model, which can in some way predict a gentrification process. I am using various indicators such as age structure, income, real estate prices etc. over a time ...
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### modelling a proportion dependent variable when two teams are involved

I am wondering what the best way to proceed in trying to predict the amount of possession a basketball team gets in a game. I have heard that beta regression tends to be good for proportions, but I am ...
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### Time series prediction in R over more than 180,000 past data points takes forever?

We have data values pertaining to BPS (bits per second) traffic on a networking device. We have data from for a particular month (October) from the past 4 years. The data points are available in a 1 ...
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### Is forecasting and predictions part of Inferential or Descriptive?

Can someone please explain to me which statistics forecasting and predictions are part of? Inferential or Descriptive? I am working on an homework. I was unable to come across the answer in my reading....
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### Time series prediction in R where data is available over past 4 years in 1 minute intervals

We have data from 'october' in the past 4 years and we want to predict what data for this year is going to look like in October. The data looks like this: 1 2 3 4 5 6 ... Every october has 31 days, ...
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### Random forest interpretation

I don't understand if in random forest the features mean that a variable is positively correlated with the probability or simply it means that the feature has an influence in the prediction. For ...
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### Dealing with Unbalanced categorical data for prediction in R

I'm currently working on a predictive model concerning a dataset of 180521 observations for ~10 variables (including the predicted class). The predicted class is caracterized as below : True : 8058 ...
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### Assigning a transfer value of a Football player given performance scores

I just recently landed my dream internship at a football statistics company and I am eager to impress. I have an excel spreadsheet of every player in the major leagues along with the minutes they ...
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### Restaurant Transaction Predictions

I'm new with R and have an intermediate background in stats. I'm developing a model that predicts the hourly transactions that a restaurant has. I work for a fast food chain in Latin America (we have ...
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### Such thing as a weighted correlation?

I have some interesting data on the most popular musical artists streamed divided by location into about 200 congressional districts. I want to see if it's possible to poll a person on his or her ...
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### How to split data for LSTM prediction of a vector?

I'm trying to decide how to best split my data to train a LSTM to predict the next time series vector, currently my inputs are 255,30 . So 255 time steps with each containing a vector of length 30 ...
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### predict function over or underestimates in cases where polynomials are included in lmer (and glmer) models

I have been having trouble with the predict function underestimating (or overestimating) the predictions from an lmer model with some polynomials. Hopefully my edits make it clearer. I have scaled ...
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### Predictions from betting quotes adjusted for money-flows

Recently at least two predictions were made based on (pools) of bookmaker quotes one for the UEFA Euro 2016 (see here and Zeileis A, Leitner C, Hornik K (2016). “Predictive Bookmaker Consensus Model ...
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### State-of-the-art methods for forecasting time series array

Suppose I have a set of measurements taken at regular intervals, and I want to predict future values of one of those measurements. There are relationships between the variables being measured. For ...
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### Processing time prediction

I'm performing some processing of rather large amounts of data. I did a hundred tests with some constant number of records (i.e 1, 2, 3, 4, 5, …, 99, 100 millions) and measured execution time with <...
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### Multivariate/interaction prediction from glmm

Previously I have asked how to calculate the predicted response for groups (split by two categorical variables) given a single continuous fixed effect, in a glmm. Now I would like to take it one step ...
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### Proving that lagging results is sufficient to prevent foreknowledge in model

I am trying to explain to a friend why lag prevents foreknowledge in a model. The example that sparked the discussion is here:quantstattrader My Attempts Shifting the prediction back by one wouldnt ...
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### Pattern recognition & classification in time series medical data

I wish to analyze time series data from glaucoma patients to get a better understanding of how that data may fit into patters and how that may relate to different treatment regimens and preservation ...
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### How to prove the reliability of a predictive model to executives?

I trained data from 500 devices to predict their performance. Then I applied my trained model to a test data set for another 500 devices and show pretty good prediction results. Now my executives want ...
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### Predictive inference of interval sampling for proportions?

Suppose I am sampling periodically (each month) over a year a substance that is degrading over time. The process of sampling is destructive so I am using difference samples each month. At the end of ...
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### Is it possible to add up the accuracy rates of 2 predictors?

Weather channel 1 has a 65% accuracy rate of predicting tomorrows weather Weather channel 2 has a 59% accuracy rate of predicting tomorrows weather Is it somehow possible to take into account ...
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### Bayesian prediction for unobserved sampling

I have the following bayesian ZIP model: ...
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### Is test MSE all that matters when it comes to prediction?

I'm wondering if when it comes to predictive analytics, whether a lower test MSE is really all that matters. Should I not even look at residuals - other model diagnostics when it comes to prediction ...
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### Time series with slightly unequal intervals

I'm very new to statistics, and I have a problem that may or may not exactly be considered a time series analysis problem. I have a large set of vehicle location measurements (x0, y0)...(xt, yt) taken ...
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### Binary outcomes with low goodness of fit (GOF) but good predictive power?

We know that it is possible that a model predicts the outcome reasonably well (and hence high R-square) but is actually misspecified (and hence low goodness of fit), as this example shows. The ...
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### Which will be better If I have 2 years data (training and testing) with a condition

Condition: I will always asks the model to predict the behavior of last 1 month data i.e I want the result on last 1 month of data. I have 2 years of data of my app, and I have to train the model and ...
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### How can I decide weight in negative binomial regression?

I'm on my project to predict the amount of demand of products in a store. we have lot of 0 on the amount of sales of products so when I did multiple regression, predictions of the demand had negative ...