regression

问题 I am doing a project involving scientific computing. The following are three variables and their values I got after some experiments. There is also an equation with three unknowns, a , b and c : x=(a+0.98)/y+(b+0.7)/z+c How do I get values of a,b,c using the above? Is this possible in MATLAB? 回答1: This sounds like a regression problem. Assuming that the unexplained errors in measurements are Gaussian distributed, you can find the parameters via least squares. Basically, you'd have to rewrite

问题 I'm having a little bit of a difficult time understanding how to implement the Panel OLS in pandas. I have received help on this topic and I thought I was understanding the situation. Now that I am trying to implement I am having difficulty. Below is my data: url='https://raw.githubusercontent.com/108michael/ms_thesis/master/crsp.dime.mpl.df.1' df=pd.read_csv(url, usecols=(['date', 'cid', 'log_diff_rgdp', 'billsum_support', \ 'years_exp', 'leg_totalbills', 'log_diff_rgdp', 'unemployment',

问题 So I would like to plot entire full range quantile lines in full range when using facet_wrap . The code goes as follows: library(tidyverse) library(quantreg) mtcars %>% gather("variable", "value", -c(3, 10)) %>% ggplot(aes(value, disp)) + geom_point(aes(color = factor(gear))) + geom_quantile(quantiles = 0.5, aes(group = factor(gear), color = factor(gear))) + facet_wrap(~variable, scales = "free") #> [multiple warnings removed for clarity] Created on 2019-12-05 by the reprex package (v0.3.0)

问题 I am trying to implement a exponential regression function. sp stands for sympy. I use numpy and sympy. Firstly, in func_exp I tried to use np.exp but it generated an error (attribute error), so I decided to use sympy instead. Well, this is the code import numpy as np from numpy.linalg import matrix_rank import scipy import scipy.integrate import random import matplotlib.pyplot as plt import matplotlib as mpl from mpl_toolkits.mplot3d import Axes3D from sympy import integrate import sympy as

问题 The poisson regression looks as follows in My R-code: poissmod <- glm(aerobics$y ~ factor(aerobics$x1) + factor(aerobics$x2) + aerobics$x3 + aerobics$x4, family = poisson) poissmod Now I have to compute a confidence interval for the factor aerobics$x1 (in a model without aerobics$x1 since this is not significant). This might look very easy, but I am not familiar with R and I can 't find the answer anywhere... Anyone who can help me? Thanks a lot in advance! 回答1: See e.g. the confint function

问题 I have the following problem. I want to compute the regression of an annual time series in matrix form. In total, I have 56 time series I extracted from gridpoints of an area I want to examine, so that I've got 56 values per year. I've plotted all values as points in a figure. Now I want to add a regression line to this figure, which contains all data. My goal is to compute the regression for the whole matrix. library(zoo) pdf(file="/home/user/name.pdf", pointsize=20, onefile = FALSE, width

问题 Mtcars has 32 observations of 11 variables. Assume that "mpg", "drat", "qsec" are the dependent variables of interest. Assume that "cyl" and "hp" are the independent variables for model type 1 and "disp" is the independent variable for model type 2. I want to automate some regressions but can't do Step (2) below. What do I want to do? On my actual dataframe, I have many more dependent variables of interest than independent variables. I want to run "lm" or "glm" for each of the following: mpg

问题 Hi have created a OLS regression using Statsmodels I've written some code that loops through every variable in a dataframe and enters it into the model and then records the T Stat in a new dataframe and builds a list of potential variables. However I have 20,000 variables so it takes ages to run each time. Can anyone think of a better approach? This is my current approach TStatsOut=pd.DataFrame() for i in VarsOut: try: xstrout='+'.join([baseterms,i]) fout='ymod~'+xstrout modout = smf.ols(fout

问题 I have data of fixed effects: genotypes = C, E, K, M; age = 30, 45, 60, 75, 90 days; random effects: block = 1, 2, 3; and variable = weight_DM. The file is in: https://drive.google.com/open?id=1_H6YZbdesK7pk5H23mZtp5KhVRKz0Ozl I have the slopes linear and quadratic of ages for each genotype, but I do not have the intercepts and standard errors. The R code is: library(nlme) library(lme4) library(car) library(carData) library(emmeans) library(ggplot2) library(Matrix) library(multcompView) datos

问题 I want to use Excel's buil-in function called LINEST() to do regression analysis in .net. I am able to use the function with squred matrix array, but when it is not square matrix say of order[12,3] then it gives error as: LinEst method of WorksheetFunction class failed Please help me out with this as it is very important for me to complete this code. This is my complete code: System.Data.DataTable dt = new System.Data.DataTable(); SqlCommand cmd =new SqlCommand("Select QtytoTransfer from DEmo