a. State the Pearson correlation trend between the two variables b. List the necessary steps to determine if there is a linear relationship between two variables. Length-weight relationship of a fish species is given as, W = al", which is a power function. Using the following data set and a scatter plotgiven, answer the following questions:{Hubungan panjang-berat bagi spesies ikan diberi sebagai, W = aLº, yang merupakan fungsi kuasa. Dengan menggunakan set data dan plot taburandata yang diberi, jawab soalan berikut:}%3DLengthPanjang 4(cm)WeightBerat(g)78.6.10 11 12 1314110 913 27 2129 2540LengthPanjang 15(cm)WeightBerat(g)16 171819 20212223242538 443574 85101116130155171203250200150100505.1015202530Length; Panjang (cm)Weight; Berat (g)5 Square-roottransformationFormulaOriginal dataInverseLogTransformationTransformasiFormulaData asalTransformationPunca kuasaTransformasidualogaritmaTranasformasisongsangΣL13411.9333.41151.02EL?1570311.0957.6613413191.9824.4181.4855110.2428.21319ELW269520.3739.8628.27

Given the data as Formula Orginal data Inverse Log Transformation Square-root transformation…

Answered: a. State the Pearson correlation trend…

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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An economist wants to determine whether there is a linear relationship between a country's gross domestic product (GDP) and carbon dioxide emissions. The data are shown in the table below. c. Compute and interpret the correlation coefficient. d. Compute and interpret the coefficient of determination. e. Test for the significance of the linear relationship. Use a 0.05 level of significance. State your conclusion. Hint: Your conclusion is either of the following. • There is a significant linear relationship between a country's gross domestic product (GDP) and carbon dioxide emissions. • There is no significant linear relationship between a country's gross domestic product (GDP) and carbon dioxide emissions. GDP 1.6 3.6 4.9 1.1 0.9 2.9 2.7 2.3 1.6 1.5 (trillion dollars) Carbon Dioxide Emissions 428.2 828.8 1214.2 444.6 264 415.3 571.8 454.9 358.7 573.5 (millions of metric tons)
A researcher measures GPA and height for a group of high school students. What kind of correlation is likely to be obtained for these two variables?
A random sample of college students was surveyed about how they spend their time each week. The scatterplot below displays the relationship between the number of hours each student typically works per week at a part- or full-time job and the number of hours of television each student typically watches per week. The correlation between these variables is r = –0.63, and the equation we would use to predict hours spent watching TV based on hours spent working is as follows: Predicted hours spent watching TV = 17.21 – 0.23(hours spent working) Since we are using hours spent working to help us predict hours spent watching TV, we’d call hours spent working a(n) __________________ variable and hours spent watching TV a(n) __________________ variable. The correlation coefficient, along with what we see in the scatterplot, tells us that the relationship between the variables has a direction that is _________________ and a strength that is ______________________. According to the…
Which of the following is true about the correlation coefficient? A) It ranges from 0 to 1. B) It indicates the strength and direction of a linear relationship between two variables. C) It can be greater than 1. D) It is used to measure the central tendency of data.
The maximum weights (in kilograms) for which one repetition of a half-squat can be performed and the jump heights (in centimeters) for 12 international soccer players are given in the accompanying table. The correlation coefficient, rounded to three decimal places, is r=0.692. At a =0.05, is there enough evidence to conclude that there is a significant linear correlation between the variables? E Click the icon to view the soccer player data. Determine the null and alternative hypotheses. Ho: p o Maximum Weights and Jump Heights Ha: p o Maximum Jump height, y Determine the critical value(s). weight, x 190 60 to = (Round to three decimal places as needed. Use a comma to separate answers as needed.) 185 56 155 55 Determine the standardized test statistic. 180 59 175 55 t= (Round to three decimal places as needed.) 170 65 What is the conclusion? 150 52 160 52 Ho. There V enough evidence at the 5% level of significance to conclude that there is a significant linear correlation between the…
e. Compute the covariance and correlation coefficient r between the right tire pressure. f. How do you know the relationship between pressure in the left and right tires is positive. g. What value would a correlation coefficient (r) be close to if there was no (linear) relationship between the 2 tire pressures?
Which of the following measures of correlation is limited in applicability to linear relationships? Select one: а. Intraclass correlation O b. Spearman's Rho Ос. Kendall's Tau O d. Pearson
Consider the bivariate data set with n = 7 observations X -4 1 -6 9. -8 -4 -4 Y 20 60 -10 99 -3 16 18 where a linear relationship between X and Y is investigated. The equation relating Y to X is formulated by Y = Bo + B1X + e and is called the simple linear regression equation. Use your calculator to answer the following questions. Give the values of the least squares estimates Bo Round your answer to the nearest integer. Round your answer to 3 decimal places.
Complete the following instructions:i. Identify the independent and dependent variables.ii. Calculate and interpret the Pearson correlation coefficient r for the paired data. Be sure to indicate if the correlation is positive or negative, and whether it is strong, moderate, or weak, or if there does not appear to be any significant correlation. A researcher for a gasoline retailer examines the relationship between the volume of gasoline purchased in kL(i.e. 1000 L) compared to the price of gasoline (in $/L), over a course of several days, to see if there is a correlation between the data: Amount of Gasoline Purchased Price of Gasoline 2.257 1.079 3.126 1.015 2.793 1.046 4.671 0.973 2.137 1.021 5.916 0.939 2.775 0.995 4.88 0.927 3.162 0.964 7.498 0.899 2.971 0.982 3.596 0.945 1.372 1.129 3.961 1.059 i. Independent Variable: Dependent Variable: ii. Pearson correlation coefficient (r): Round to 3 decimal…
Listed below are annual data for various years. The data are weights (metric tons) of imported lemons and car crash fatality rates per 100,000 population. Construct a scatterplot and find the value of the linear correlation coefficient r. Using a = 0.05, is there sufficient evidence to conclude that there is a linear correlation between lemon imports and crash fatality rates? Do the results suggest that imported lemons cause car fatalities? Lemon Imports Crash Fatality Rate Construct a scatterplot. Choose the correct graph below. O A. ^) 17- 16- 15+ 14+ 0 0 8 Q O O O 200 400 600 Q 232 15.9 OB. Ay 17- 16- 15+ 14+ 0 -o q 264 15.6 0 200 400 600 Q ✔ 357 15.5 481 15.3 C O C. A) 17+ 16- 15- 14+ 534 14.9 O -0 0 200 400 The linear correlation coefficient r is. (Round to three decimal places as needed.) There sufficient evidence to conclude that there is a linear correlation between lemon imports and crash fatality rates. Do the results suggest that imported lemons cause car fatalities? O A.…
If the correlation coefficient obtained from data pairs of variables X and Y is 0.98 the strength and direction of the relationship between X and Y is which of the following: (i) very strong positive (ii) moderate positive (iii) moderate negative (iv) very weak negative

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