Homework Answers
Solution2:
To pedict height based on head circumference
height=dependent variable
head circumference=independent variable
Rcode:
datar<-"Height(cm) Head_Circumference(cm)
179 60
161 55
162 57
155 60
158 56
172 57
191 60
179 57
163 58"
datan<-read.table(textConnection(object=datar),
header=TRUE,
sep="",
stringsAsFactors = FALSE)
datan
hist(datan$Height.cm.,main="Histogram of Height",col="blue")
hist(datan$Head_Circumference.cm.,main="Histogram of Head
circumference",col="green")boxplot(datan$Height.cm.,main="Boxplot
of Height",col="blue")
boxplot(datan$Head_Circumference.cm.,main="Boxplot of Head
circumference",col="green")
qqnorm(datan$Height.cm.,main="Normal Q-Q plot for Height")
qqline(datan$Height.cm.)
Output:
yes response variable height is not approximatey normal.since mean >median value it is positively skewed
From histogram of circumference,it is approximately normally distributed
From histogram of height,it is positively skewed
No outliers found for height or circumeference from boxplots.
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Please help me with these questions with R codes.. thank
you!!
Here’s the data I have...
Please provide with R codes! thank you!! Data: Question: Data: 179 161 162 605557 155 60...
Please provide with R codes! thank you!!
Data:
Question:
Data: 179 161 162 605557 155 60 158 56 172 57 191 60 179 57 163 58 Height (cm) Head Circumference (cm) 2. Draw at most 3 plots to visually describe your data. Is your response variable approximately Normal? 3. Numerically describe the centre, spread and any unusual points of your variables/data. 4. Fit and describe a simple linear regression model between head circumference and height. 5. Are the regression parameters...
Do 2.3.4 Use the data "Orange" in R. You should include the r code as well...
Do 2.3.4
Use the data "Orange" in R. You should include the r code as well as the output in your file, with appropriate answer to questions. Answer the following questions in your document 1. Fit a simple linear regression using "circumterence" as response and "age" as predictor. Is there a significant linear relationship between the two variables? State the null and alternative hypothesis, test statistic and p-value 2. Find which observation has the largest residual in absolute value) Give...
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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)...
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)...
4. The anscombe data set in the datasets R package (should automatically be loaded) contains 4 pa...
4. The anscombe data set in the datasets R package (should automatically be loaded) contains 4 pairs of response-explanatory variables. The pairs are xl-yl, x2-y2, x3-y3, and x4-y4 where x is the explanatory variable and y is the response variable. (a) Run 4 simple linear regression analyses (one on each of the 4 pairs) to verify that the regression output is exactly the same (up to numerical accuracy) b) For each pair, describe what is wrong (if anything) and use...
**R-STUDIO KNOWLEDGE REQUIRED*** PLEASE ANSWER THE FOLLOWING WITH ****R-STUDIO**** CODING- thank ...
**R-STUDIO KNOWLEDGE REQUIRED***
PLEASE ANSWER THE FOLLOWING WITH ****R-STUDIO****
CODING- thank you so much!!
I am specifically look for the solution to part
***(h)**** and *****(i)***** below using R-Studio
code:
The data set in question
is:
YEAR Height Stories
1990 770 54
1980 677 47
1990 428 28
1989 410 38
1966 371 29
1976 504 38
1974 1136 80
1991 695 52
1982 551 45
1986 550 40
1931 568 49
1979 504 33
1988 560 50
1973 512...
can somebody please help me with the question quickly I really need help with this, choose a country and research population data in order to fill out the table below.
2. Choose a country and research population data in order to fill out the table beloa. Copy the population numbers counted each five years, as shown in the data base, for the years from 1950 to 2000 . Add a column, \(t\), measuring years șince 1945 .b. What is the country you selected? In what part of the world is it? What is the magnitude of its population numbers? \(\left(100,000^{\circ} \mathrm{s}\right.\), millions, hundred millions, billions?) Is it growing or shrinking...
Please provide with R codes! thank you!!
Data:
Question:
Data: 179 161 162 605557 155 60 158 56 172 57 191 60 179 57 163 58 Height (cm) Head Circumference (cm) 2. Draw at most 3 plots to visually describe your data. Is your response variable approximately Normal? 3. Numerically describe the centre, spread and any unusual points of your variables/data. 4. Fit and describe a simple linear regression model between head circumference and height. 5. Are the regression parameters...
Do 2.3.4
Use the data "Orange" in R. You should include the r code as well as the output in your file, with appropriate answer to questions. Answer the following questions in your document 1. Fit a simple linear regression using "circumterence" as response and "age" as predictor. Is there a significant linear relationship between the two variables? State the null and alternative hypothesis, test statistic and p-value 2. Find which observation has the largest residual in absolute value) Give...
Exercise 1. For this exercise use the bdims data set from the openintro package. Type ?bdims to read about this data set in the help menu. Of interest are the variables hgt (height in centimeters), wgt (weight in kilograms), and sex (dummy variable with 1-male, 0-female). Since ggplotO requires that a categorical variable be coded as a factor type in R, run the following code: library (openintro) bdíms$sex2 <-factor (bdins$sex, levels-c (0,1), labels=c('F', 'M')) (a) Use ggplot2 to make a...
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)...
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)...
4. The anscombe data set in the datasets R package (should automatically be loaded) contains 4 pairs of response-explanatory variables. The pairs are xl-yl, x2-y2, x3-y3, and x4-y4 where x is the explanatory variable and y is the response variable. (a) Run 4 simple linear regression analyses (one on each of the 4 pairs) to verify that the regression output is exactly the same (up to numerical accuracy) b) For each pair, describe what is wrong (if anything) and use...
**R-STUDIO KNOWLEDGE REQUIRED***
PLEASE ANSWER THE FOLLOWING WITH ****R-STUDIO****
CODING- thank you so much!!
I am specifically look for the solution to part
***(h)**** and *****(i)***** below using R-Studio
code:
The data set in question
is:
YEAR Height Stories
1990 770 54
1980 677 47
1990 428 28
1989 410 38
1966 371 29
1976 504 38
1974 1136 80
1991 695 52
1982 551 45
1986 550 40
1931 568 49
1979 504 33
1988 560 50
1973 512...
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