linear-regression

I have 365 columns. In each column I have 60 values. I need to know the rate of change over time for each column (slope or linear coefficient). I created a generic column as a series of numbers from 1:60 to represent the 60 corresponding time intervals. I want to create 356 linear regression models using the generic time stamp column with each of the 365 columns of data. In other words, I have many columns and I would like to create many linear regression models at once, extract the coefficients and put those coefficients into a new matrix. First of all, statistically this might not be the

Firstly, I am not familiar with Python and I still barely understand the mechanism of Python code. But I need to do some statistical analysis through Python. I have tried many many ways to figure out but I failed. Basically, I have 3 arrays of data (assume these arrays are X , Y , Z ). I did some analysis with ( X , Y ) and ( Z , Y ) by making the scatter plot and put the best fit with the data to see the correlation. №1 and №2 are quite easy enough. Now I need to see the edge on view from the graph which is the one with combined X and Z . So, I made the equation (see below). import pylab as

enter image description here How can I draw a linear regression line in this graph? here is my code: import numpy as np import pandas_datareader.data as web import pandas as pd import datetime import matplotlib.pyplot as plt #get adjusted close price of Tencent from yahoo start = datetime.datetime(2007, 1, 1) end = datetime.datetime(2017, 12, 27) tencent = pd.DataFrame() tencent = web.DataReader('0700.hk', 'yahoo', start, end)['Adj Close'] nomalized_return=np.log(tencent/tencent.iloc[0]) nomalized_return.plot() plt.show() Pic 1 Jupiter Notebook Pic 2 my Jupiter Notebook You can use scikit

I need to apply lm() to an enlarging subset of my dataframe dat , while making prediction for the next observation. For example, I am doing: fit model predict ---------- ------- dat[1:3, ] dat[4, ] dat[1:4, ] dat[5, ] . . . . dat[-1, ] dat[nrow(dat), ] I know what I should do for a particular subset (related to this question: predict() and newdata - How does this work? ). For example to predict the last row, I do dat1 = dat[1:(nrow(dat)-1), ] dat2 = dat[nrow(dat), ] fit = lm(log(clicks) ~ log(v1) + log(v12), data=dat1) predict.fit = predict(fit, newdata=dat2, se.fit=TRUE) How can I do this

I want to run linear regression for the same outcome and a number of covariates minus one covariate in each model. I have looked at the example on this page but could that did not provide what I wanted. Sample data a <- data.frame(y = c(30,12,18), x1 = c(7,6,9), x2 = c(6,8,5), x3 = c(4,-2,-3), x4 = c(8,3,-3), x5 = c(4,-4,-2)) m1 <- lm(y ~ x1 + x4 + x5, data = a) m2 <- lm(y ~ x2 + x4 + x5, data = a) m3 <- lm(y ~ x3 + x4 + x5, data = a) How could I run these models in a short way and and without repeating the same covariates again and again? Backlin Following this example you could do this:

I've a dataset with 70 variables, and I want to try polynomial regression on it. If the number of columns were three/four I could just hand code something like this -- model <- lm(y ~ poly(var1,3) + poly(var2,3) + poly(var4,4) How would we go about this, if we have 70 variables? Should we type in manually names of all the variables or is there a easier method? You could paste the formula, if all variables are named systematically: form <- as.formula(paste("y~", paste0("poly(var", 1:10, ")", collapse="+"))) or (for polynomial of 3rd degree): form <- as.formula(paste("y~", paste0("poly(var", 1