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Finding Critical r Values Table A-6 lists critical values of r for selected values of n and a. More generally, critical r values can be found by using the formula

\(r = \frac{t}{{\sqrt {{t^2} + n - 2} }}\)

where the t value is found from the table of critical t values (Table A-3) assuming a two-tailed case with n - 2 degrees of freedom. Use the formula for r given here and in Table A-3 (with n - 2 degrees of freedom) to find the critical r values corresponding to \({H_1}:\rho \ne 0\), \(\alpha \)= 0.02, and n = 27.

Short Answer

Expert verified

The critical values of r calculated using the given formula are -0.445 and 0.445.

Step by step solution

01

Given information

The critical value of r needs to be computed using the given formula.

The degrees of freedom are given to be equal to n-2. The sample size (n) is equal to 27 and the level of significance \(\left( \alpha \right)\) is equal to 0.02. \(\)

02

Computation of the critical value

The critical value can be computed using the formula given below.

\(r = \frac{t}{{\sqrt {{t^2} + n - 2} }}\)

Where

t is the critical value obtained from t distribution table with n-2 degrees of freedom and\(\alpha = 0.02\)

n is the sample size and has a value equal to 27.

Referring to the t distribution table, the critical value of t with 25 degrees of freedom for 0.02 significance level is 2.4851.

The critical value is computed as shown below:

\(\begin{aligned} r &= \frac{t}{{\sqrt {{t^2} + n - 2} }}\\ &= \frac{{2.4851}}{{\sqrt {{{\left( {2.4851} \right)}^2} + 27 - 2} }}\\ &= 0.445\end{aligned}\)

Thus, the critical r values are -0.445 and 0.445.

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Most popular questions from this chapter

Testing for a Linear Correlation. In Exercises 13鈥28, construct a scatterplot, and find the value of the linear correlation coefficient r. Also find the P-value or the critical values of r from Table A-6. Use a significance level of A = 0.05. Determine whether there is sufficient evidence to support a claim of a linear correlation between the two variables. (Save your work because the same data sets will be used in Section 10-2 exercises.)

Pizza and the Subway The 鈥減izza connection鈥 is the principle that the price of a slice of pizza in New York City is always about the same as the subway fare. Use the data listed below to determine whether there is a significant linear correlation between the cost of a slice of pizza and the subway fare.

Year

1960

1973

1986

1995

2002

2003

2009

2013

2015

Pizza Cost

0.15

0.35

1

1.25

1.75

2

2.25

2.3

2.75

Subway Fare

0.15

0.35

1

1.35

1.5

2

2.25

2.5

2.75

CPI

30.2

48.3

112.3

162.2

191.9

197.8

214.5

233

237.2

Exercises 13鈥28 use the same data sets as Exercises 13鈥28 in Section 10-1. In each case, find the regression equation, letting the first variable be the predictor (x) variable. Find the indicated predicted value by following the prediction procedure summarized in Figure 10-5 on page 493.

Using the listed lemon/crash data, find the best predicted crash fatality rate for a year in which there are 500 metric tons of lemon imports. Is the prediction worthwhile?

Confidence Intervals for a Regression Coefficients A confidence interval for the regression coefficient b1 is expressed

\(\begin{array}{l}{b_1} - E < {\beta _1} < {b_1} + E\\\end{array}\)

Where

\(E = {t_{\frac{\alpha }{2}}}{s_{{b_1}}}\)

The critical t score is found using n 鈥(k+1) degrees of freedom, where k, n, and sb1 are described in Exercise 17. Using the sample data from Example 1, n = 153 and k = 2, so df = 150 and the critical t scores are \( \pm \)1.976 for a 95% confidence level. Use the sample data for Example 1, the Stat diskdisplay in Example 1 on page 513, and the Stat Crunchdisplay in Exercise 17 to construct 95% confidence interval estimates of \({\beta _1}\) (the coefficient for the variable representing height) and\({\beta _2}\) (the coefficient for the variable representing waist circumference). Does either confidence interval include 0, suggesting that the variable be eliminated from the regression equation?

Outlier Refer to the accompanying Minitab-generated scatterplot. a. Examine the pattern of all 10 points and subjectively determine whether there appears to be a correlation between x and y. b. After identifying the 10 pairs of coordinates corresponding to the 10 points, find the value of the correlation coefficient r and determine whether there is a linear correlation. c. Now remove the point with coordinates (10, 10) and repeat parts (a) and (b). d. What do you conclude about the possible effect from a single pair of values?

Effects of an Outlier Refer to the Minitab-generated scatterplot given in Exercise 11 of

Section 10-1 on page 485.

a. Using the pairs of values for all 10 points, find the equation of the regression line.

b. After removing the point with coordinates (10, 10), use the pairs of values for the remaining 9 points and find the equation of the regression line.

c. Compare the results from parts (a) and (b).

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