The data show the number of viewers for television stars with certain salaries. Find the regression equation, letting salary be the independent (x) variable. Find the best predicted number of viewers for a television star with a salary of $3 million. Is the result close to the actual number of viewers, 7.0 million? Use a significance level of 0.05. Salary (millions of $) Viewers (millions) 99 2 6 4.4 8.3 5 4. 9 15 4.1 2.2 1.4 8.8 4.9 4.3 E Click the icon to view the critical values of the Pearson correlation coefficient r. A Critical Values of the Pearson Correlation Coefficient r What is the regression equation? y=+ x (Round to three decimal places as needed.) Critical Values of the Pearson Correlation Coefficient r NOTE: To test H, p=0 against H,: p0, reject H, What is the best predicted number of viewers for a television star with a salary of $3 million? a =0.05 a= 0.01 4. 0.950 0.990 5 The best predicted number of viewers for a television star with a salary of $3 million is million. (Round to one decimal place as needed.) 0.878 0.959 6. 17 if the absolute value of ris greater than the critical value in the table. 0.811 0.917 0.754 0.875 Is the result close to the actual number of viewers, 7.0 million? 0.707 0.666 0.632 8 0.834 0.798 O A. The result is not very close to the actual number of viewers of 7.0 million. 10 11 12 13 0.765 0.735 0.708 O B. The result is exactly the same as the actual number of viewers of 7.0 million. 0.602 0.576 OC. The result is very close to the actual number of viewers of 7.0 million. 0.684 0.553 0.532 0.514 14 0.661 O D. The result does not make sense given the context of the data. 0.641 0.623 0.606 15 16 0.497 17 0.482 0.590 0.575 0.561 0.505 0.463 0.468 0.456 18 19 20 0.444 25 (0.396 Click to select your answer(s). 0.361 0.335 0.312 0.294 In279 30 35 0.430 40 0.402 45 50 0.378 0.361
The data show the number of viewers for television stars with certain salaries. Find the regression equation, letting salary be the independent (x) variable. Find the best predicted number of viewers for a television star with a salary of $3 million. Is the result close to the actual number of viewers, 7.0 million? Use a significance level of 0.05. Salary (millions of $) Viewers (millions) 99 2 6 4.4 8.3 5 4. 9 15 4.1 2.2 1.4 8.8 4.9 4.3 E Click the icon to view the critical values of the Pearson correlation coefficient r. A Critical Values of the Pearson Correlation Coefficient r What is the regression equation? y=+ x (Round to three decimal places as needed.) Critical Values of the Pearson Correlation Coefficient r NOTE: To test H, p=0 against H,: p0, reject H, What is the best predicted number of viewers for a television star with a salary of $3 million? a =0.05 a= 0.01 4. 0.950 0.990 5 The best predicted number of viewers for a television star with a salary of $3 million is million. (Round to one decimal place as needed.) 0.878 0.959 6. 17 if the absolute value of ris greater than the critical value in the table. 0.811 0.917 0.754 0.875 Is the result close to the actual number of viewers, 7.0 million? 0.707 0.666 0.632 8 0.834 0.798 O A. The result is not very close to the actual number of viewers of 7.0 million. 10 11 12 13 0.765 0.735 0.708 O B. The result is exactly the same as the actual number of viewers of 7.0 million. 0.602 0.576 OC. The result is very close to the actual number of viewers of 7.0 million. 0.684 0.553 0.532 0.514 14 0.661 O D. The result does not make sense given the context of the data. 0.641 0.623 0.606 15 16 0.497 17 0.482 0.590 0.575 0.561 0.505 0.463 0.468 0.456 18 19 20 0.444 25 (0.396 Click to select your answer(s). 0.361 0.335 0.312 0.294 In279 30 35 0.430 40 0.402 45 50 0.378 0.361
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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
Transcribed Image Text:The data show the number of viewers for television stars with certain salaries. Find the regression equation, letting salary be the independent (x) variable. Find the best predicted number of viewers for a television star with a salary of $3 million. Is
the result close to the actual number of viewers, 7.0 million? Use a significance level of 0.05.
Salary (millions of $)
Viewers (millions)
99
7
4
9
15
4.1
2.2
1.4
4.4
8.3
8.8
4.9
4.3
= Click the icon to view the critical values of the Pearson correlation coefficient r.
Critical Values of the Pearson Correlation Coefficient r
What is the regression equation?
y=+ x (Round to three decimal places as needed.)
Critical Values of the Pearson Correlation Coefficient r
a =0.05
0.950
What is the best predicted number of viewers for a television star with a salary of $3 million?
a= 0.01
NOTE: To test H p=0
0.990
against H, p#0, reject H,
if the absolute value of r is
greater than the critical
value in the table.
14
The best predicted number of viewers for a television star with a salary of $3 million is million.
(Round to one decimal place as needed.
0.878
0.959
0.811
0.754
0.707
0.666
6
0.917
17
0.875
Is the result close to the actual number of viewers, 7.0 million?
0.834
9
0.798
0.765
10
11
12
O A. The result is not very close to the actual number of viewers of 7.0 million.
0.632
0.602
0.735
O B. The result is exactly the same as the actual number of viewers of 7.0 million.
0.576
0.553
0.532
0.514
0.708
OC. The result is very close to the actual number of viewers of 7.0 million.
13
14
0.684
0.661
0.641
0.623
0.606
O D. The result does not make sense given the context of the data.
15
16
0.497
17
0.482
0.468
0.456
0.444
0.590
0.575
0.561
0.505
18
19
20
25
0.396
Click to select your answer(s).
30
0.361
0.463
35
0.335
0.430
0.312
0.294
40
0.402
0.378
0361
45
50
0279
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