linear-regression

问题 How can I draw a barplot for means, while controlling for other variables through regression -- in a split-bars-by-vars fashion? My general problem I conduct a research to figure out which fruit is more likable: mango, banana, or apple. To this end, I go ahead and sample 100 people at random. I ask them to rate, on a scale of 1-5, the degree of liking each of the fruits. I also collect some demographic information about them: gender, age, education level, and whether they are colorblind or

问题 How can I draw a barplot for means, while controlling for other variables through regression -- in a split-bars-by-vars fashion? My general problem I conduct a research to figure out which fruit is more likable: mango, banana, or apple. To this end, I go ahead and sample 100 people at random. I ask them to rate, on a scale of 1-5, the degree of liking each of the fruits. I also collect some demographic information about them: gender, age, education level, and whether they are colorblind or

问题 How can I draw a barplot for means, while controlling for other variables through regression -- in a split-bars-by-vars fashion? My general problem I conduct a research to figure out which fruit is more likable: mango, banana, or apple. To this end, I go ahead and sample 100 people at random. I ask them to rate, on a scale of 1-5, the degree of liking each of the fruits. I also collect some demographic information about them: gender, age, education level, and whether they are colorblind or

问题 Closed. This question does not meet Stack Overflow guidelines. It is not currently accepting answers. Want to improve this question? Update the question so it's on-topic for Stack Overflow. Closed 10 months ago . Improve this question I would like to get the R-squared within for a fixed effect regression with multiple fixed effects (let's say Country, Year, Trimester). The least squared dummy variable (LSDV) model ( lm in R/ reg in Stata) would only provide the overall R-squared. The same is

问题 I was helping a friend with segmented regressions today. We were trying to fit a piecewise regression with a breakpoints to see if it fits data better than a standard linear model. I stumbled across a problem I cannot understand. When fitting a piecewise regression with a single breakpoint with the data provided, it does indeed fit a single breakpoint. However, when you predict from the model it gives what looks like 2 breakpoints. When plotting the model using plot.segmented() this problem

问题 I was helping a friend with segmented regressions today. We were trying to fit a piecewise regression with a breakpoints to see if it fits data better than a standard linear model. I stumbled across a problem I cannot understand. When fitting a piecewise regression with a single breakpoint with the data provided, it does indeed fit a single breakpoint. However, when you predict from the model it gives what looks like 2 breakpoints. When plotting the model using plot.segmented() this problem

问题 I have a huge dataset and would like to perform a rolling linear regression over a window of 60. However, I want that only the 60 previous values are considered for the linear regression. My Dataframe DF consists of following Columns: Date Company Y X1 X2 01.01.2015 Mill 0.13 -1 -3 01.02.2015 Mill 0.16 1 5 01.03.2015 Mill 0.83 3 4 01.04.2015 Mill -0.83 23 4 01.01.1988 Hall 0.23 1 3 01.02.1988 Hall 0.24 23 2 01.03.1988 Hall 0.78 19 -9 01.04.1988 Hall 0.73 4 12 01.05.1988 Hall 0.72 5 12 01.11

问题 I have 2 arrays which are true_stress and true_strain. I want to do a linear regression to their log10 versions but I keep getting the said error. from sklearn.linear_model import LinearRegression log_tStress = np.log10(true_stress) log_tStrain = np.log10(true_strain) regressor = LinearRegression() regressor.fit(log_tStrain, log_tStress) predict = regressor.predict(log_tStrain) ValueError: Expected 2D array, got 1D array instead: 回答1: Well it kinda is just what it says.You are feeding a 1D

问题 I know that this error message ( ValueError: too many values to unpack (expected 4) ) appears when more variables are set to values than a function returns. scipy.stats.linregress returns 5 values according to the scipy documentation (http://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.linregress.html). Here is a short, reproducible example of a working call, and then a failed call, to linregress : What could account for difference and why is the second one poorly called? from

问题 lets say I have following data ind1 <- rnorm(99) ind2 <- rnorm(99) ind3 <- rnorm(99) ind4 <- rnorm(99) ind5 <- rnorm(99) dep <- rnorm(99, mean=ind1) group <- rep(c("A", "B", "C"), each=33) df <- data.frame(dep,group, ind1, ind2, ind3, ind4, ind5) the following code is calculating multiple linear regression between dependend variable and 2 independent variables by group which is exactly what I want to do. But I want to regress dep variable against all combination pair of independent variables