Q1.Consider the following data:46Find out the following:(i)(ii)yKarl Pearson correlation coefficient (r)Regression equation (ŷ)4XAssignment No. 1y55Q2. Calculate Coefficient of determination (R²) from the following data:3457656446Q3. Calculate Estimated Standard error (SE) from the following data:342342Marks in Statistics2455Marks in Mathematics 35 46 45 3032 25 24 163Q4. Calculate the Spearman's rank correlation(r.). from the following data:Q5. Write a short note on Correlation and Regression analysis.6101620 60 2055 20 365 27 10 12 21

Given that X Y 4 6 6 4 5 5 2 4 3 6 NOTE:- According to bartleby guidelines expert…

Answered: Q1. Consider the following data: 4 6 6…

MATLAB: An Introduction with Applications

6th Edition

ISBN:9781119256830

Author:Amos Gilat

Publisher:Amos Gilat

Chapter1: Starting With Matlab

Section: Chapter Questions

Problem 1P

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Sir Francis Galton, in the late 1800 s, was the first to introduce the statistical concepts of regression and correlation. He studied the relationships between pairs of variables such as the size of parents and the size of their offspring. Data similar to that which he studied are given below, with the variable x denoting the height (in centimeters) of a human father and the variable y denoting the height at maturity (in centimeters) of the father's oldest (adult) son. The data are given in tabular form and also displayed in the scatter plot below, which gives the least-squares regression line as well. The equation for this line is y =95.35 +0.48 x Height of Height of son, father, x (in centimeters) centimeters) y (in 175.8 178.1 193.5 190.3 210. 183.2 176.6 173.3 171.1 200- 183.8 186.5 190. 175.8 175.6 180 159.8 167.2 xx 170 155.5 175.8 162.0 172.1 160- 190.9 188.0 150 180.1 187.1 Tdo ito Ido 130 200 z1o 186.9 177.5 171.5 181.5 Height of father, x (in centimeters) 190.5 195.7 201.0…
The following data are the monthly salaries y and the grade point averages x for students who obtained a bachelor's degree in business administration. GPA 2.6 3.4 3.6 3.2 3.5 2.9 Monthly Salary ($) 3,500 3,900 4,200 3,800 4,200 3,900 The estimated regression equation for these data is ý = 1,970.72 + 608.11x and MSE = 18,671.17. (a) Develop a point estimate of the starting salary (in dollars) for a student with a GPA of 3.1. (Round your answer to the nearest cent). (b) Develop a 95% confidence interval for the mean starting salary (in dollars) for all students with a 3.1 GPA. (Round your answers to the nearest cent). to $ (c) Develop a 95% prediction interval (in dollars) for Ryan Dailey, a student with a GPA of 3.1. (Round your answers to the nearest cent). to $
(e) Find the line of best fit (or, the regression line) for the following data points: (3,13)(3,13), (4,18)(4,18), (5,23)(5,23), and (7,24)(7,24).
The table below gives the age and bone density for five randomly selected women. Using this data, consider the equation of the regression line, yˆ=b0+b1x, for predicting a woman's bone density based on her age. Keep in mind, the correlation coefficient may or may not be statistically significant for the data given. Remember, in practice, it would not be appropriate to use the regression line to make a prediction if the correlation coefficient is not statistically significant. Age 39 43 46 61 64 Bone Density 352 346 321 314 312 Table Step 1 of 6 : Find the estimated slope. Round your answer to three decimal places
Q3: From the following data: 2 5 y 5 4 6 3 1) Draw scatter plot. 2) Determine the regression line y = a + b x. 3) Predict the value of y for the value of x = 4.6. 4) Calculate the correlation coefficient. 4 8 3 5
I. Show complete solutions. Simple Linear Regression and Correlation: a.) Results of a study on the occurrence of sodium and chloride in surface streams in a coastal area. The following data are chloride concentration (y) in mg/liter and road-way area in the watershed (x) in %. Y 0.24 4.6 1. What is the value of the correlation coefficient? 2. What is the interpretation for this case? 3. What is the estimated chloride concentration if road way 0.3 6.4 0.45 9.8 area in the watershed is 1.30% 0.75 10.7 4. What is the equation of the prediction line? 0.57 10.6 0.75 10.9 0.4 11.7 0.8 12.5 0.6 14 0.78 14.7 0.85 15.1 0.68 18.3 0.69 19.2 1.4 23.1 1.15 27.3 1.05 27.7 1.69 32 1.62 38.4
The mortality rate of a sample of the population as a function of daily tobacco consumption can be seen below: Number of cigarettes "x": 3 5 6 15 20 Mortality rate "y" : 0.2 0.3 0.4 0.5 0.7 With these data, solve the following: a. Determine and interpret the correlation coefficient. b. Find the regression line of y on x. c. What will be the mortality rate for a consumer of 40 cigarettes per day?
Consider the following data,Study Hours (Y): 2, 4 ,6 ,8 ,10 ,13, 7Sleeping Hours (X): 10, 9, 8, 7,6 ,7, 5 i) Calculate and analyze the fitted regression line between the number of study hours and the number of sleeping hours of different intakes of CSE students.ii) Find the coefficient of determination and interpret your data.iii) Predict study hour when he/she sleeps 11 hours.
a) Determine the correlation coefficient, r, and interpret this value in the context of the problem. (Round 4 decimal places)b) Find the equation of the regression line for the given data. (Round the slope and y-intercept 4 decimal places).c) Give a practical interpretation of the slope of the regression line.
The following table shows students’ number of absences, x, and the student’s final grade,, y.# of absences x 6 2 15 9 12 5 8 Final grade y 82 86 43 74 58 90 78a) Calculate r, the correlation coefficient ________________b) Find the equation of the regression line __________________________________c) If a student is absent 4 times,, what grade does your regression line predict?__________________

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Q1. Consider the following data: 4 6 6 4 Assignment 5 5 2 4 3 6 Find out the following: (i) Karl Pearson correlation coefficient (r) (ii) Regression equation (9) Q2. Calculate Coefficient of determination (R²) from the following data: