PLEASE ANSWER ALL parts .
IF YOU CANT ANSWER ALL, KINDLY ANSWER PART (E) AND PART(F)
FOR PART (E) THE REGRESSION MODEL IS ALSO GIVE AT THE END.
REGRESSION MODEL:
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PLEASE ANSWER ALL parts .
IF YOU CANT ANSWER ALL, KINDLY ANSWER PART (E) AND
PART(F)...
1. For each of the following regression models, write down the X matrix and 3 vector....
1. For each of the following regression models, write down the X matrix and 3 vector. Assume in both cases that there are four observations (a) Y BoB1X1 + B2X1X2 (b) log Y Bo B1XiB2X2+ 2. For each of the following regression models, write down the X matrix and vector. Assume in both cases that there are five observations. (a) YB1XB2X2+BXE (b) VYBoB, X,a +2 log10 X2+E regression model never reduces R2, why 3. If adding predictor variables to a...
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...
Please I want someone help me to solve this question a,b,c,d,e I’m not sure about my...
Please I want someone help me to solve this question
a,b,c,d,e
I’m not sure about my solution
This is the data
# Set directory to data folder
setwd("C:data")
# getwd()
# Read data from csv file
data <- read.csv("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)
plot(model)
par(mfrow=c(1,1))
# Residual Plot
data$residuals...
Below are given (a) A scatterplot of Y versus X and (b) A plot of residuals...
Below are given (a) A scatterplot of Y versus X and (b) A plot
of residuals versus fitted values after a simple linear regression
model was fit to the data. What is the equation of the fitted line?
Discuss what is indicated about the relationship between Y and X as
it relates to simple linear regression.
Fitted Line Plot Y = - 14.64 + 7.431 X R-Sq R-Sq (adj) 2.43700 91.9% 91.8% 1 > 20- 3 4 5 6 7...
When you use linear regression to fit a linear model, and create a scatterplot of actual...
When you use linear regression to fit a linear model, and create a scatterplot of actual vs. predicted values, you would ideally see: a. the points lie close to the diagonal line from bottom left to upper right b. the points form a random "cloud" C. the point lie close to a horizontal line (write a, b or c): (True/False) If you have many variables (features), you will tend to prefer non-parametric methods to parametric methods. The two plots below...
For expert using R , I solve it but i need to figure out what I...
For expert using R , I solve it but i need to figure out what
I got is correct or wrong. Thank you
# Simple Linear Regression and Polynomial Regression
# HW 2
#
# Read data from csv file
data <-
read.csv("C:\data\SweetPotatoFirmness.csv",header=TRUE,
sep=",")
head(data)
str(data)
# scatterplot of independent and dependent variables
plot(data$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)...
Not all bivariate relationships are linear. When you plot a scatterplot, sometimes you observe a curved...
Not all bivariate relationships are linear. When you plot a scatterplot, sometimes you observe a curved relationship. In those cases, we can apply a transformation to the data that will make the relationship approximately linear. [We generally prefer simpler models. And linear regression models are simpler than curved regression models. Also, transforming data is common in statistical practice.] One of the most common transformation methods is the log transformation. In this problem, you apply the log transformation to both variables...
Please answer (e), (f), (g) 1. List the variables Sales and Ads in the data file...
Please answer (e), (f), (g)
1. List the variables Sales and Ads in the data file salad.xlsx on the LMS networks. Sales is the number of boxes of Cheerios sold by Kellogs during 12 sample periods and Ads is the expenditure on Cheerios advertising in million real US dollars. (a) Scatterplot Sales versus Ads . Please discuss the plot briefly. (b) Fit the following linear regression model by using the command Im( sales ads) in R. Sales; = Bo +...
actually other expert help me with a solution for hw1 (thanks a lot for him). so...
actually other expert help me with a solution for hw1 (thanks a
lot for him). so , if you look just the question that I post you
can see it or just write the first line of the question , thank you
for your interest in my question . I post the code that I used at
first homework
# Set directory to data folder
setwd("C:data")
# getwd()
# Read data from csv file
data <- read.csv("SweetPotatoFirmness.csv",header=TRUE,
sep=",")
head(data)
str(data)...
This is a time series statistics question please answer problem 3.36 (all parts a through d) is p...
this is a time series statistics question please
answer problem 3.36 (all parts a through d) is pictured. it refers
to using the data called cpg which is already a built-in data set
to r code. show output and code used for r code. thanks.
3.36 One of the remarkable technological developments in the computer industry has been the ability to store information densely on a hard drive. In addition, the cost of storage has steadily declined causing problems of...
1. For each of the following regression models, write down the X matrix and 3 vector. Assume in both cases that there are four observations (a) Y BoB1X1 + B2X1X2 (b) log Y Bo B1XiB2X2+ 2. For each of the following regression models, write down the X matrix and vector. Assume in both cases that there are five observations. (a) YB1XB2X2+BXE (b) VYBoB, X,a +2 log10 X2+E regression model never reduces R2, why 3. If adding predictor variables to a...
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...
Please I want someone help me to solve this question
a,b,c,d,e
I’m not sure about my solution
This is the data
# Set directory to data folder
setwd("C:data")
# getwd()
# Read data from csv file
data <- read.csv("SweetPotatoFirmness.csv",header=TRUE,
sep=",")
head(data)
str(data)
# scatterplot of independent and dependent variables
plot(data$pectin,data$firmness,xlab="Pectin,
%",ylab="Firmness")
par(mfrow = c(2, 2)) # Split the plotting panel into a 2 x 2
grid
model <- lm(firmness ~ pectin , data=data)
summary(model)
plot(model)
par(mfrow=c(1,1))
# Residual Plot
data$residuals...
Below are given (a) A scatterplot of Y versus X and (b) A plot
of residuals versus fitted values after a simple linear regression
model was fit to the data. What is the equation of the fitted line?
Discuss what is indicated about the relationship between Y and X as
it relates to simple linear regression.
Fitted Line Plot Y = - 14.64 + 7.431 X R-Sq R-Sq (adj) 2.43700 91.9% 91.8% 1 > 20- 3 4 5 6 7...
When you use linear regression to fit a linear model, and create a scatterplot of actual vs. predicted values, you would ideally see: a. the points lie close to the diagonal line from bottom left to upper right b. the points form a random "cloud" C. the point lie close to a horizontal line (write a, b or c): (True/False) If you have many variables (features), you will tend to prefer non-parametric methods to parametric methods. The two plots below...
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)...
Please answer (e), (f), (g)
1. List the variables Sales and Ads in the data file salad.xlsx on the LMS networks. Sales is the number of boxes of Cheerios sold by Kellogs during 12 sample periods and Ads is the expenditure on Cheerios advertising in million real US dollars. (a) Scatterplot Sales versus Ads . Please discuss the plot briefly. (b) Fit the following linear regression model by using the command Im( sales ads) in R. Sales; = Bo +...
actually other expert help me with a solution for hw1 (thanks a
lot for him). so , if you look just the question that I post you
can see it or just write the first line of the question , thank you
for your interest in my question . I post the code that I used at
first homework
# Set directory to data folder
setwd("C:data")
# getwd()
# Read data from csv file
data <- read.csv("SweetPotatoFirmness.csv",header=TRUE,
sep=",")
head(data)
str(data)...
this is a time series statistics question please
answer problem 3.36 (all parts a through d) is pictured. it refers
to using the data called cpg which is already a built-in data set
to r code. show output and code used for r code. thanks.
3.36 One of the remarkable technological developments in the computer industry has been the ability to store information densely on a hard drive. In addition, the cost of storage has steadily declined causing problems of...
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