I need an introduction with interpretation to understand the attached file. Thanking you Nonparametric CorrelationsCorrelationsX1X2X3Spearman's rhoX1Correlation Coefficient1.000-.152.140Sig. (2-tailed).049.068170170170X2Correlation Coefficient-.1521.000.529Sig. (2-tailed).049.000170170170X3Correlation Coefficient.140.5291.000Sig. (2-tailed).068.000N170170170*. Correlation is significant at the 0.05 level (2-tailed).** Correlation is significant at the 0.01 level (2-tailed).

Name: I need an introduction with interpretation to understand the attached
Uploaded: 2024-03-20T07:07:12.000Z
Channel: Bartleby
Description: I need an introduction with interpretation to understand the attached file. Thanking you Nonparametric CorrelationsCorrelationsX1X2X3Spearman's rhoX1Correlation Coefficient1.000-.152.140Sig. (2-tailed).049.068170170170X2Correlation Coefficient-.1521.

To determine the relationship between the variables X1, X2, and X3, a Spearman's rank-order…

Math

Statistics

Nonparametric Correlations Correlations X2 -152 X1 X3 Correlation Coefficient Sig. (2-tailed) Spearman's rho X1 1.000 .140 .049 .068 N 170 170 170 X2 Correlation Coefficient -152 1.000 .529 Sig. (2-tailed) .049 .000 170 170 170 X3 Correlation Coefficient .140 .529 1.000 Sig. (2-tailed) .068 .000 N 170 170 170 *. Correlation is significant at the 0.05 level (2-tailed). Correlation is significant at the 0.01 level (2-tailed).

Nonparametric Correlations Correlations X2 -152 X1 X3 Correlation Coefficient Sig. (2-tailed) Spearman's rho X1 1.000 .140 .049 .068 N 170 170 170 X2 Correlation Coefficient -152 1.000 .529 Sig. (2-tailed) .049 .000 170 170 170 X3 Correlation Coefficient .140 .529 1.000 Sig. (2-tailed) .068 .000 N 170 170 170 *. Correlation is significant at the 0.05 level (2-tailed). Correlation is significant at the 0.01 level (2-tailed).

MATLAB: An Introduction with Applications

6th Edition

ISBN:9781119256830

Author:Amos Gilat

Publisher:Amos Gilat

Chapter1: Starting With Matlab

Section: Chapter Questions

Problem 1P

See similar textbooks

Similar questions

using karl pearsonn coeffecient of correlation
What is the null hypothesis of a test for correlation? There is correlation in the population. There is positive correlation in the population. There is no correlation in the population. There is negative correlation in the population.
A researcher interested in establishing the relationship between Statistics final examination results of 24 students and their attitude towards their statistics lecturer obtained the following statistical results. i). Giving reasons, comment on the established correlation between the two variables. ii). State the null (H0) and the alternative (H1) hypothesis if the researcher was interested in establishing whether there is a significant correlation. iii). Based on a set alpha level of 0.05, state the conclusion of the hypothesis test?
How do we decide if the correlation coefficient is close enough to ±±1 to declare that there is correlation?
The linear correlation coefficient is r= (Round to three decimal places as needed.) Determine the null and alternative hypotheses. H0:ρ __ ___ H1:ρ ____ ____ (Type integers or decimals. Do not round.) The test statistic is t= (Round to two decimal places as needed.) The P-value is (Round to three decimal places as needed.) Because the P-value of the linear correlation coefficient is: the significance level, there: sufficient evidence to support the claim that there is a linear correlation between Internet users and scientific award winners.
What is meant by perfect “positive correlation,” “perfect negativecorrelation,” and “zero correlation”?
The correlation coefficient r is a sample statistic. What does it tell us about the value of the population correlation coefficient p(Greek letter rho)? You do not know how to build the formal structure of hypothesis tests of p yet. However, there is a quick way to determine if the sample evidence based on p is strong enough to conclude that there is some population correlation between the variables. In other words, we can use the value of r to determine if p ≠ 0. We do this by comparing the value |r| to an entry in the correlation table. The value of a in the table gives us the probability of concluding that p ≠ 0 when, in fact, p = 0 and there is no population correlation. We have two choices for a: a = 0.05 or a = 0.01. (a) Look at the data below regarding the variables x= age of a Shetland pony and y = weight of that pony. Is the value of |r| large enough to conclude that weight and age of Shetland ponies are correlated? Use a = 0.05. (Round your answer for rto four decimal…
I have transportation data with a year variable, categorical and continuous variables. I tried to create a cross section by concatenating all the categorical variables together then regress on the continuous variables with revenue as my dependent variable using Pooled OLS. Unfortunately, i have serial correlation in my model and i am not able to remove the serial correlation. Alternatively, i was thinking of looking at just one year of data or treating the data as a single cross section, including year dummy variables and selecting specific categorical variables as additional dummy variables to determine the effect on revenue. Any issues with that approach or alternative recommendations? Also, its worth noting that i had to remove 15% of my data to ensure each cross section that i created had a year from 2011-2017 to allow me to run the proc panel in SAS.
Compute the correlation coefficient. (Multiple choice)
Describe about Pearson Correlation Coefficient.
The correlation coefficient r is a sample statistic. What does it tell us about the value of the population correlation coefficient ρ (Greek letter rho)? You do not know how to build the formal structure of hypothesis tests of ρ yet. However, there is a quick way to determine if the sample evidence based on ρ is strong enough to conclude that there is some population correlation between the variables. In other words, we can use the value of r to determine if ρ ≠ 0. We do this by comparing the value |r| to an entry in the correlation table. The value of α in the table gives us the probability of concluding that ρ ≠ 0 when, in fact, ρ = 0 and there is no population correlation. We have two choices for α: α = 0.05 or α = 0.01. (a) Look at the data below regarding the variables x = age of a Shetland pony and y = weight of that pony. Is the value of |r| large enough to conclude that weight and age of Shetland ponies are correlated? Use α = 0.05. (Use 3 decimal places.) x 3 6 12 22 26…
Researchers want to know if there is a correlation between BMI and systolic blood pressure in males over 50. A random sample of 9 males is selected and their data is recorded below. a) Calculate the Pearson Correlation Coefficient for these two variables and b) test whether the correlation is significantly different from 0. Run the test at a 5% level of significance. Give each of the following: 1) the appropriate null and alternative hypotheses; 2) the appropriate test; 3) the decision rule; 4) the calculation of the test statistic; and 5) your conclusion including a comparison to alpha or the critical value. You MUST show your work ID BMI Systolic blood pressure 1 18.5 122 2 20.0 112 3 22.3 120 4 25.4 136 5 26.9 140 6 28.7 117 7 30.4 152 8 32.7 165 9 33.4 162