STAT r grogramming
Fit a regression of waiting as a function of eruptions (i.e. waiting~eruptions; waiting on the y-axis and eruptions on the x-axis). What can we say about this regression? Compare the distribution of the residuals (model$resid where model is your lm object) to the distribution of the variables.
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Help with coding in R: cyl<-factor(scan(text= "6 6 4 6 8 6 8 4 4 6...
Help with coding in R: cyl<-factor(scan(text= "6 6 4 6 8 6 8 4 4 6 6 8 8 8 8 8 8 4 4 4 4 8 8 8 8 4 4 4 8 6 8 4")) am<-factor(scan(text= "1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 0 0 0 0 0 1 1 1 1 1 1 1")) ## 1)## Using the data `cyl` and `am` (transmission...
For expert using R , I solve it but i need to figure out what I...
For expert using R , I solve it but i need to figure out what
I got is correct or wrong. Thank you
# Simple Linear Regression and Polynomial Regression
# HW 2
#
# Read data from csv file
data <-
read.csv("C:\data\SweetPotatoFirmness.csv",header=TRUE,
sep=",")
head(data)
str(data)
# scatterplot of independent and dependent variables
plot(data$pectin,data$firmness,xlab="Pectin,
%",ylab="Firmness")
par(mfrow = c(2, 2)) # Split the plotting panel into a 2 x 2
grid
model <- lm(firmness ~ pectin , data=data)
summary(model)
anova(model)
plot(model)...
Need help with stats true or false questions Decide (with short explanations) whether the following statements are true or false a) We consider the model y-Ao +A(z) +E. Let (-0.01, 1.5) be a 95% con...
Need help with stats true or false questions
Decide (with short explanations) whether the following statements are true or false a) We consider the model y-Ao +A(z) +E. Let (-0.01, 1.5) be a 95% confidence interval for A In this case, a t-test with significance level 1% rejects the null hypothesis Ho : A-0 against a two sided alternative. b) Complicated models with a lot of parameters are better for prediction then simple models with just a few parameters c)...
2. Suppose Y ~ Exp(a), which has pdf f(y)-1 exp(-y/a). (a) Use the following R code to generate data from the model Yi...
2. Suppose Y ~ Exp(a), which has pdf f(y)-1 exp(-y/a). (a) Use the following R code to generate data from the model Yi ~ Exp(0.05/Xi), and provide the scatterplot of Y against X set.seed(123) n <- 500 <-rnorm (n, x 3, 1) Y <- rexp(n, X) (b) Fit the model Yi-Ao + Ax, + ε¡ using the lm function in R and provide a plot of the best fit line on the scatterplot of Y vs X, and the residual...
Answer the following question by showing the codes in R 2. Consider the dataset mtcars and...
Answer the following question by showing the codes in R
2. Consider the dataset mtcars and suppose we are interested in modeling the mpg of a vehicle based on a single variable presented in the dataset. a) Use the cor ) function in R, apply it to only numerical variables in the dataset. Identify the numerical variable that shows the most significant correlation, and generate a scatterplot between this variable and mpg. b) Use the 1m() function in R to...
3. In the multiple regression model shown in the previous question, which one of the following st...
3. In the multiple regression model shown in the previous question, which one of the following statements is incorrect: (b) The sum of squared residuals is the square of the length of the vector ü (c) The residual vector is orthogonal to each of the columns of X (d) The square of the length of y is equal to the square of the length of y plus the square of the length of û by the Pythagoras theorem In all...
Since residuals measure how far the observations are from the regression line, they are often used...
Since residuals measure how far the observations are from the regression line, they are often used to assess the fit of the regression line to the data. We might display these vertical deviations graphically using a residual plot. By plotting the residuals against the explanatory variable x, we effectively magnify the deviations (that is, change the y-axis from response to vertical deviations), which allows for a better and closer examination of the deviations. Describe what a residual plot would look...
What information does R (that is, r-squared) provide in general about the fit of a regression...
What information does R (that is, r-squared) provide in general about the fit of a regression model? It is exactly equal to the correlation between X and Y. It tells us the proportion of variability in the dependent variable, Y, that is explained by the model. It tells us the proportion of variability in the independent variable, X, that is explained by the model We should keep choosing different independent variables until R equals 1 O The closer R is...
matlab multiple regression
peruvian.txtProblem 1 (explore the data):In this exercise use the Peruvian blood pressure data set, provided in the file peruvian.txt (A NOTE for repeat students: The data is different from the data I shared last year.). This dataset consists of variables possibly relating to blood pressures of n = 30 Peruvians who have moved from rural high altitude areas to urban lower altitude areas. The variables in this dataset are: Age, Weight, Height, Pulse, Systol and Diastol. Before reading the data into MATLAB, it can be viewed in a...
7 Consider the following regression output involving the variables y and, rı, r2. (note log is the natural logarithm as usual) 4.12 0.88 r Model A: Model B: log(y)0.34 0.14 + 0.001 2 Model C: logly)2...
7 Consider the following regression output involving the variables y and, rı, r2. (note log is the natural logarithm as usual) 4.12 0.88 r Model A: Model B: log(y)0.34 0.14 + 0.001 2 Model C: logly)2011.4 log()0.02 r2 0.06 Model D: Model E: y = 5.4 + 0.82i --3.4 55.1 log(0.020 2 + 1.2r2 0.2 (1x2) Ceteris Paribus: (a) In Model A: If x1 increases 6 to 8 by 2 units, then the predicted change in y is Δy =...
For expert using R , I solve it but i need to figure out what
I got is correct or wrong. Thank you
# Simple Linear Regression and Polynomial Regression
# HW 2
#
# Read data from csv file
data <-
read.csv("C:\data\SweetPotatoFirmness.csv",header=TRUE,
sep=",")
head(data)
str(data)
# scatterplot of independent and dependent variables
plot(data$pectin,data$firmness,xlab="Pectin,
%",ylab="Firmness")
par(mfrow = c(2, 2)) # Split the plotting panel into a 2 x 2
grid
model <- lm(firmness ~ pectin , data=data)
summary(model)
anova(model)
plot(model)...
Need help with stats true or false questions
Decide (with short explanations) whether the following statements are true or false a) We consider the model y-Ao +A(z) +E. Let (-0.01, 1.5) be a 95% confidence interval for A In this case, a t-test with significance level 1% rejects the null hypothesis Ho : A-0 against a two sided alternative. b) Complicated models with a lot of parameters are better for prediction then simple models with just a few parameters c)...
2. Suppose Y ~ Exp(a), which has pdf f(y)-1 exp(-y/a). (a) Use the following R code to generate data from the model Yi ~ Exp(0.05/Xi), and provide the scatterplot of Y against X set.seed(123) n <- 500 <-rnorm (n, x 3, 1) Y <- rexp(n, X) (b) Fit the model Yi-Ao + Ax, + ε¡ using the lm function in R and provide a plot of the best fit line on the scatterplot of Y vs X, and the residual...
Answer the following question by showing the codes in R
2. Consider the dataset mtcars and suppose we are interested in modeling the mpg of a vehicle based on a single variable presented in the dataset. a) Use the cor ) function in R, apply it to only numerical variables in the dataset. Identify the numerical variable that shows the most significant correlation, and generate a scatterplot between this variable and mpg. b) Use the 1m() function in R to...
3. In the multiple regression model shown in the previous question, which one of the following statements is incorrect: (b) The sum of squared residuals is the square of the length of the vector ü (c) The residual vector is orthogonal to each of the columns of X (d) The square of the length of y is equal to the square of the length of y plus the square of the length of û by the Pythagoras theorem In all...
What information does R (that is, r-squared) provide in general about the fit of a regression model? It is exactly equal to the correlation between X and Y. It tells us the proportion of variability in the dependent variable, Y, that is explained by the model. It tells us the proportion of variability in the independent variable, X, that is explained by the model We should keep choosing different independent variables until R equals 1 O The closer R is...
7 Consider the following regression output involving the variables y and, rı, r2. (note log is the natural logarithm as usual) 4.12 0.88 r Model A: Model B: log(y)0.34 0.14 + 0.001 2 Model C: logly)2011.4 log()0.02 r2 0.06 Model D: Model E: y = 5.4 + 0.82i --3.4 55.1 log(0.020 2 + 1.2r2 0.2 (1x2) Ceteris Paribus: (a) In Model A: If x1 increases 6 to 8 by 2 units, then the predicted change in y is Δy =...
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