Question
In: Statistics and Probability
Please do these questions in the R language 1. Load the cars dataset into R. It...
Please do these questions in the R language
1. Load the cars dataset into R. It is a built-in dataset.
2. Do an str() to determine the number of observations and variables. Enter your answer as a comment.
3. Plot speed on x axis and distance on y axis.
4. Find the correlation between speed and distance. What does the magnitude and sign indicate? Enter your answer as a comment.
5. Build a linear regression model with speed as the independent variable and distance as the dependent variable (all raised to first power).
6. Write the regression equation from question 5 as a comment.
7. Interpret the intercept and slope coefficients from question 6 in your own words as a comment.
8. What is the R square value of the regression model from question 5? What is the definition of R square value in the context of this question? Write your answer as a comment.
9. Are the coefficients significant at 0.001 level of significance for the regression model from question 5? How do you determine that? Enter your answer as a comment.
10. What is the distance value for a speed of 25 units using the regression equation from question 6? You can calculate it by hand. Enter your answer as a comment.
Solutions
orchestra answered 2 years agoRelated Solutions
R Problem Set: #Work with the inbuilt dataset "Cars" View(cars) This will show you the dataset...
R Problem Set:
#Work with the inbuilt dataset "Cars"
View(cars)
This will show you the dataset on 2 variables speed and
distance.
?cars
This will explain what the variables mean.
#Q1) Describe the dataset. What are the main findings?
#Q2) Design a relevant question to model using linear
regressions
#Q3) Run the regression and report the std error, t-stat, p value
and f stat.
#Q4) Is this a valid regression? Is the normality assumption
justified? Show clearly.
#Q5) Are there...
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into R.
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( be careful with the qualitative variable ShelveLoc. )
obtain the interaction plot of ShelveLoc and price.
Install and load the dataset named Carseats (in the ISLR package) into R. Create a new...
Install and load the dataset named Carseats (in the ISLR
package) into R.
Create a new dataframe that is a copy of Carseats. Create two
indicator (dummy) variables:
Bad_Shelf = 1 if ShelveLoc = “Bad”, 0 otherwise
Good_Shelf = 1 if ShelveLoc = “Good”, 0 otherwise
Also, create two interaction variables:
Price_Bad_Shelf = Price* Bad_Shelf
Price_Good_Shelf = Price* Good_Shelf
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R code:
## 2. __Basic dplyr exercises__
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`vehicles`. Answer the following questions.
install.packages("fueleconomy")
library(fueleconomy)
library(dplyr)
library(tidyr)
data(vehicles)
e. Finally, for the years 1994, 1999, 2004, 2009, and 2014, find
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What are the R codes for these questions below: 1. Load the library {car}, which contains...
What are the R codes for these questions below:
1. Load the library {car}, which contains the Salaries data set.
#Then, list the first few records
with head(Salaries). The display the summmary() for this dataset,
which will shows frequencies.
Then, load the library {psych} which contains the describe()
function and use this function to list the descriptive statistics
for the dataset.
2. Load the coefplot library and display a coefficient plot for
lm.fit.2 <- lm(salary~sex+yrs.since.phd, data=Salaries) using
the coefplot() function....
What are the R codes for these questions below: 1. Load the library {car}, which contains...
What are the R codes for these questions below:
1. Load the library {car}, which contains the Salaries data set.
#Then, list the first few records
with head(Salaries). The display the summmary() for this dataset,
which will shows frequencies.
Then, load the library {psych} which contains the describe()
function and use this function to list the descriptive statistics
for the dataset.
2. Load the coefplot library and display a coefficient plot for
lm.fit.2 <- lm(salary~sex+yrs.since.phd, data=Salaries) using
the coefplot() function....
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you will need to decide whether or not to center/scale the
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Use the “d_logret_6stocks” dataset to answer the questions.
(General Motor: GenMotor)（using R）
(1) Regress the return of General Motor on the returns of
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(2) Generate an ANOVA table to conclude if regression effects
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(3) Compute the correlation of General Motor and Citigroup, and
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dataset below
Date Pfizer ...
Solve it by R Use the “d_logret_6stocks” dataset to answer the questions. Test by using α=...
Solve it by R
Use the “d_logret_6stocks” dataset to answer the questions. Test
by using α= .01. (General Motor: GenMotor).
(1) Regress the return of General Motor on the returns of
Citigroup (with intercept). Report the estimated coefficients. Is
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(2) Suppose we “know” the return of Citigroup tomorrow is 0.05,
what is the predicted return of General Motor tomorrow?
(3) Compute the correlation of...
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