answer and R code for: 'h' and 'i' The built-in data set, rock, is a data frame recording the measurements of rock sample from a petroleum reservoir. We are interested in the area column of rock. Using R we can convert this column into the vector x by theassignment x<-rock$shape. Assume that the n area measurements x=(X₁, X2,...,xn) are a random sample from a population with true unknown mean μ and true unknown variance 02. Remember, let x be defined by x<-rock$shape.a) Calculate, n, the number of elements in x.b) Calculate the sample standard deviation s, of x.c) Estimate true mean μ, using this data by calculating the sample mean.d) Calculate the sample variance of x.e) Calculate the interquartile range of x, using R.f) Calculate the 50th percentile of x using R.g) Calculate a48trimmed mean for x using R.h) Since the sample size is >30 we can create a confidence interval for μ using a normal critical value. If we want the confidence interval to be at the 94% level and we use a normal critical value, then what critical valueshould we use?i) Calculate a 94% confidence interval(using a normal critical value) for μ.(j) How long is the 94% confidence interval just created in part i?k) Copy your R script for the above into the text box here.

h) Confidence level is 94% i.e., 0.94. Significance level α=1-0.94=0.06

Answered: answer and R code for: 'h' and 'i'

Linear Algebra: A Modern Introduction

4th Edition

ISBN:9781285463247

Author:David Poole

Publisher:David Poole

Chapter4: Eigenvalues And Eigenvectors

Section4.6: Applications And The Perron-frobenius Theorem

Problem 24EQ

See similar textbooks

Similar questions

The built-in data set, rock, is a data frame recording the measurements of rock sample from a petroleum reservoir. We are interested in the area column of rock. Using R we can convert this column into the vector x by the assignment x<-rock$shape. Assume that the n area measurements x=(x1, x2,...,xn) are a random sample from a population with true unknown mean ? and true unknown variance ?2. Remember, let x be defined by x<-rock$shape.
The following are a sample of observations on incoming solar radiation at a greenhouse: 12.1 39.6 7.3 8.8 20.7 18.2 89.9 22.2 (a) Assign the data to an vector called solar.radiation (b) Find the mean, median and variance of the radiation observations. (c) Add 10 to each observation of solar.radiation, and assign the result to sr10. Find the mean, median, and variance of sr10. (d) Multiply each observation by -1.2, and assign the result to srm2. Find the mean, median, and variance of srm2. How do the statistics change now? (e) Plot a histogram of the solar.radiation, sr10, and srm2.
Compute the sum of squares error (SSE) by hand for the given set of data and linear model: (6,6),(7,7),(8,9); Y = x - 1
Compute the sum-of-squares error (SSE) by hand for the given set of data and linear model. (6, 6), (7, 7), (9, 10); y = x − 1
A snack food manufacturer estimates that the variance of the number of grams of carbohydrates in servings of its tortilla chips is 1.34. A dietician is asked to test this claim and finds that a random sample of 16 servings has a variance of 1.22. At α=0.05, is there enough evidence to reject the manufacturer's claim? Assume the population is normally distributed. Complete parts (a) through (e) below.
Compute the sum-of-squares error (SSE) by hand for the given set of data and linear model. (4, 4), (5, 5), (6, 7); y = x − 1 SSE=
The betas are chosen so as to minimize: a. Errors b. SSR c. PMP d. Bias e. Noise
A study conducted at Ateneo de Naga University revealed that students who attended class 95 to 100% of the time usually received an A in the class. Students who attended class 80 to 90% of the time usually received a C+ to B+ in the class. Students who attended class less than 80% of the time usually received a D or an F or eventually withdrew from the class. In this research in AdNU with 5000 students in which 100 students participated in the survey, what is the parameter of the study? a. 80% b. 80 - 90% c. 100 d. 95 - 100 % e. 5000
Interpret the estimated regression coefficient corresponding to the Z variable. Data Salary Education Experience Sex 29.7985 15 3 1 21.8219 4 0 0 22.8978 4 0 0 22.0917 1 1 0 21.8993 5 0 0 22.4829 3 1 1 28.0772 15 0 0 y=salary 23.6292 6 1 1 x1=education level in schooling years 32.3595 0 15 1 x2=experience level in employment level 21.794 1 0 0 d=sex (1 for male,0 for female) 19.8762 3 0 0 Ln(Y) = alpha +beta1X1 +Beta2X2+ Beta3D +Beta4Z +e 21.0253 3 0 0 where z =X2D 24.6323 0 5 1 19.0247 0 0 0 18.8857 0 0 0 21.8552 1 0 0 24.2675 6 1 0 18.7931 0 0 0 18.9276 0 0 0 23.4441 5 1 1 20.8047 2 0 0 18.26 0 0 0 20.6726 0 2 1 21.7815 3 0 0…
The following sample contains the scores of 6 students selected at random in Mathematics and English. Use the scores in English as the dependent variable Y. Mathematics score (X) 70 92 80 74 65 83 English score (Y) 74 84 63 87 78 90 ∑x=464, ∑y=476,∑x^2=36354,∑y^2=38254, ∑xy=36926. Estimate the regression parameters and also write the prediction equation.
A study is made of the relationship between annual production volume of Good A and factory floor area. Table below shows the data from a sample of 10 factories. Factory Factory floor area, X (‘000m2 ) Annual production volume, Y ($‘ 000) 1 40 3.5 2 600 25.0 3 60 4.8 4 72 3.5 5 400 30.0 6 90 5.0 7 200 12.0 8 70 4.5 9 80 5.0 10 84 6.0 Construct a scatter plot.
A study of 25 online jewelry retailers was done to find the statistical relationship between the price yyin dollars of a diamond ring and the weight xxin carats of the diamond. Based on this data the following least-squares regression line was found: y^=−6047.75+11975.14xy^=−6047.75+11975.14x One online retailer sells a 3.00 carat diamond ring for 28,999 dollars. What is the residual (error) for the predicted price of a 3.00 carat diamond ring? Give your answer to two decimal places.

SEE MORE QUESTIONS

Recommended textbooks for you

Linear Algebra: A Modern Introduction

Algebra

ISBN:

9781285463247

Author:

David Poole

Publisher:

Cengage Learning

Elementary Linear Algebra (MindTap Course List)

Algebra

ISBN:

9781305658004

Author:

Ron Larson

Publisher:

Cengage Learning

Linear Algebra: A Modern Introduction

Algebra

ISBN:

9781285463247

Author:

David Poole

Publisher:

Cengage Learning

Elementary Linear Algebra (MindTap Course List)

Algebra

ISBN:

9781305658004

Author:

Ron Larson

Publisher:

Cengage Learning

SEE MORE TEXTBOOKS