Data Science Stack Exchange
Data Science Stack Exchange

Questions tagged [regression]

Techniques for analyzing the relationship between one (or more) "dependent" variables and "independent" variables.

"Regression" is a general term for a wide variety of techniques to analyze the relationship between one (or more) dependent variables and independent variables. Typically the dependent variables are modeled with probability distributions whose parameters are assumed to vary (deterministically) with the independent variables.

Ordinary least squares (OLS) regression affords a simple example in which the expectation of one dependent variable is assumed to depend linearly on the independent variables. The unknown coefficients in the assumed linear function are estimated by choosing values for them that minimize the sum of squared differences between the values of the dependent variable and the corresponding fitted values.

1592 questions
6
votes
1 answer

Regression model for a count proces

In R I have data where head(data) gives day count promotion 1 33 20.8 2 23 17.1 3 19 1.6 4 37 20.8 Now day is simply the day (and is in order). promotion is the promotion-value…
Ole Petersen
  • 197
  • 7
5
votes
2 answers

One-hot encoding

I am going through tensor-flow tutorial and noticed that they use one-hot encoding in regression tensorflow. I don't fully understand how it works. Let us take oversimplified case of ordinary least square regression. Assume we have y = [1,2,3] and x…
user1700890
  • 345
  • 1
  • 3
  • 13
4
votes
2 answers

Type of regression with nominal, ordinal, interval and ratio data

Statement of problem: An ambulance is at the hospital dropping off a patient. The goal of the paramedic is to get released from the hospital as soon as possible. I am curious, what are the factors in how long an ambulance off loads a patient at the…
David Fort Myers
  • 209
  • 1
  • 4
3
votes
2 answers

How to model compositional data?

What is the best way to model compositional data problems? Compositional data is when each example or sample is a vector that sums to 1 (or 100%). In my case, I am interested in the composition of minerals in a rock and I have sensors that tell me…
MD004
  • 310
  • 1
  • 3
  • 10
3
votes
1 answer

COVID: How is the R coefficient calculated?

I'm having a hard time trying to figure out what the formula for estimating R is. I assume it's a coefficient used in some sort of regression type? If that is the case is it an exponential, logarithmic or another type of regression from which I…
RadiantHex
  • 131
  • 2
3
votes
1 answer

Can an ordinal variable with a wide range be considered as a continuous integer variable in regression?

Just as a trivial example: I have a sample of 25 children and I have gathered some information example: weight, age etc - to use as predictors. Now, I don't have the height of the children, but I have an ordinal variable with the shortest child…
user05
  • 41
  • 1
3
votes
2 answers

Continuous Estimated Time of Arrival

I'm trying to create a model for when a shipped product will arrive at its destination. There are several stages the delivery goes through, so it's not just drive time from point A to point B. My first model looks at the status of the product on the…
kevin.w.johnson
  • 131
  • 1
3
votes
1 answer

Forward or Backward Stepwise Regression

I understand the process and logic of why to perform stepwise regression. To me they should always arrive at the same function, just one adds coefficients and tests for significance while the other removes coefficients as it tests for significance.…
elberman
  • 131
  • 2
3
votes
1 answer

Always overestimate

I have a regression use case where I am supposed to estimate a value based on 3-4 features. Using random forest, I was able to get ~20% error. However, I have a constraint now. I can overestimate but not underestimate. So, at the cost of improving…
dknight
  • 141
  • 4
3
votes
1 answer

How to fit a product of linear expressions

How can I fit expressions of type $y = (a + bx_1)(c + dx_2)$ using regression?
Prakash
  • 131
  • 2
3
votes
1 answer

What does it mean when the coefficient of the squared term is negative in regression?

I am reading a research paper which models a regression model where the returns are regressed on the number of ad exposures. the equation looks something like this: $Returns = beta_1*nExp + beta_2*nExp^2$ nExp: The number of times the user looks at…
Dawny33
  • 8,296
  • 12
  • 48
  • 104
2
votes
1 answer

Understanding orthogonal regression

In orthogonal regression, we are trying to minimize the distance from each data point $(x,y)$ to the fitted model. My question is, how come that there is a distinction between independent and dependent variables in orthogonal regression? In my naive…
Jsevillamol
  • 161
  • 4
2
votes
3 answers

Fitting as much points as possible on a line

I want to fit a set of points on a 2D plane that looks like the blue points in the following picture. Instead of having a least square fit (the yellow, dotted line), I want a line that looks like the red line that either crosses the blue points as…
hsiaomichiu
  • 171
  • 3
2
votes
1 answer

Regression - predict n numbers based on another n numbers

I'd like to get a recommendation how to attack a problem of predicting multiple numbers. Training data contains 4 columns, each says a probability of record being in the bucket. So, for example: X = [0.25, 0.5, 0.25, 0.0] and corresponding output…
Peter Krejzl
  • 81
  • 5
2
votes
1 answer

Regression and Neural networks

I'm trying to restore this function: $$ F(x) = x*sin(\alpha x)+b; \space\space \alpha,b \in (-20,20) $$ My NN model(with Keras) is: 1 layer: GRU, 9 neurons, selu activation 2 layer: GRU, 3 neurons, selu activation 1 layer: GRU, 7 neurons, selu…
iamlion12
  • 33
  • 3
1
2 3 4 5