Consider a sample of paired data which gives a linear correlation coefficient r. Use the table to determine whether there is a significant linear correlation.The linear correlation is ____________ (significant, not significant) n = 15, r = -0.732, 5% levela = 0.05 a = 0.0140.9500.9990.8780.9596.0.8110.9170.7540.8758.0.7070.8340.6660.798100.6320.7650.7350.708110.602120.576130.5530.684140.5320.661150.5140.641160.4970.623170.4820.606180.4680.590190.4560.575200.4440.561250.3960.505300.3610.4630.430350.335400.3120.402450.2940.378500.2790.361600.2540.330700.2360.2200.305800.286900.2070.2691000.1960.256The linear correlation is(significant, not significant).

Correlation Coefficient : It is denotes by 'r'. Correction coefficient is used to estimate the…

Consider a sample of paired data which gives a linear correlation coefficient r. Use the table to determine whether there is a significant linear correlation. The linear correlation is ____________ (significant, not significant)

Consider a sample of paired data which gives a linear correlation coefficient r. Use the table to determine whether there is a significant linear correlation. The linear correlation is ____________ (significant, not significant)

Glencoe Algebra 1, Student Edition, 9780079039897, 0079039898, 2018

18th Edition

ISBN:9780079039897

Author:Carter

Publisher:Carter

Chapter10: Statistics

Section10.1: Measures Of Center

Problem 4GP

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Concept explainers

Correlation

Correlation defines a relationship between two independent variables. It tells the degree to which variables move in relation to each other. When two sets of data are related to each other, there is a correlation between them.

Linear Correlation

A correlation is used to determine the relationships between numerical and categorical variables. In other words, it is an indicator of how things are connected to one another. The correlation analysis is the study of how variables are related.

Regression Analysis

Regression analysis is a statistical method in which it estimates the relationship between a dependent variable and one or more independent variable. In simple terms dependent variable is called as outcome variable and independent variable is called as predictors. Regression analysis is one of the methods to find the trends in data. The independent variable used in Regression analysis is named Predictor variable. It offers data of an associated dependent variable regarding a particular outcome.

Question

n = 15, r = -0.732, 5% level
a = 0.05 a = 0.01
4
0.950
0.999
0.878
0.959
6.
0.811
0.917
0.754
0.875
8.
0.707
0.834
0.666
0.798
10
0.632
0.765
0.735
0.708
11
0.602
12
0.576
13
0.553
0.684
14
0.532
0.661
15
0.514
0.641
16
0.497
0.623
17
0.482
0.606
18
0.468
0.590
19
0.456
0.575
20
0.444
0.561
25
0.396
0.505
30
0.361
0.463
0.430
35
0.335
40
0.312
0.402
45
0.294
0.378
50
0.279
0.361
60
0.254
0.330
70
0.236
0.220
0.305
80
0.286
90
0.207
0.269
100
0.196
0.256
The linear correlation is
(significant, not significant).

Definition Definition Statistical measure used to assess the strength and direction of relationships between two variables. Correlation coefficients range between -1 and 1. A coefficient value of 0 indicates that there is no relationship between the variables, whereas a -1 or 1 indicates that there is a perfect negative or positive correlation.

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