Question
In: Statistics and Probability
The data below shows the high temperatures and the times (in minutes) runners who won a...
The data below shows the high temperatures and the times (in minutes) runners who won a marathon. Answer parts a-c.
|
Temperature (x) |
5858 |
63 |
50 |
62 |
72 |
73 |
50 |
57 |
|
|
Time (y) |
1145.882 |
146.683 |
145.931 |
146.604 |
147.965 |
144.267 |
148.503 |
147.643 |
a. Find the value of the linear correlation coefficient r.
b. Find the critical values of r from the table showing the critical values for the Pearson correlation coefficient using
alpha=0.05
c. Is there sufficient evidence to conclude that there is a linear correlation between the two variables
Solutions
orchestra answered 2 years agoRelated Solutions
10. The data below shows the high temperatures and the times (in minutes) runners who won...
10. The data below shows the high temperatures and the times
(in minutes) runners who won a marathon. Construct a scatterplot,
find the value of the linear correlation coefficient r, and find
the P-value using a=0.05. Is there sufficient evidence to conclude
that there is a linear correlation between temperature and winning
times?
Temperature_(x)
Time_(y)
56
148.824
60
145.072
45
144.768
63
148.851
70
148.546
74
148.019
52
147.065
57
146.845
What are the null and alternative hypotheses?
Construct the...
High daily temperatures and times (in minutes) were given for women who won the New York...
High daily temperatures and times (in minutes) were given for
women who won the New York City marathon in recent years. Results
are listed in the table below. Is here a correlation between
temperature and winning time? Use α=.05. What is the best predicted
finishing time for a day with a high temperature of 70° F?
x(° F) 55 61 49 62 70 73 51 57
y(minutes) 145.3 148.7 148.3 148.1 147.6 146.4 144.7 147.5
r:
critical value:
Significant linear...
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before test number 1 and for these same students who were also
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6. Listed below are actual high temperatures and the high temperatures that were forecast five days...
6. Listed below are actual high temperatures and the high
temperatures that were forecast five days earlier. Use a .05
significance level to test the claim of a zero mean difference
between the actual high temperatures and the high temperatures that
were forecast one day earlier. Use our 4-step procedure. This is a
matched or paired samples hypothesis test. Actual high: 80 77 81 85
73 High forecast one day earlier: 78 75 81 85 76
Step 1: Ho____. Ha:...
Given below is simple random sample data for wait times, in minutes, for a call center....
Given below is simple random sample data for wait times, in
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11.3
The data below represent commute times (in minutes) and scores on a well-being survey. Complete parts...
The data below represent commute times (in minutes) and scores
on a well-being survey. Complete parts (a) through (d)
below.
Commute Time (minutes), x
5
15
25
40
60
84
105
Well-Being Index Score, y
69.2
68.1
67.2
66.5
65.3
64.7
62.8
(a) Find the least-squares regression line treating the
commute time, x, as the explanatory variable and the index score,
y, as the response variable.
y = [ ] x + [ ]
(b) Interpret the slope and y-intercept,...
Two data sets are given below, each representing times (in minutes) to complete a given task...
Two data sets are given below, each representing times (in
minutes) to complete a given task for two different samples of
students.
Data Set A: 3.1, 4.7, 7.3, 8.1, 8.1, 8.7, 8.8, 9.1, 9.1, 9.2
Data Set B: 5.2, 5.6, 5.8, 6.9, 7.0, 7.7, 7.9, 8.5, 8.8,
12.5
Complete the back-to-back stem plot for data set B by adding the
numbers that belong in each class below, using a class width of
1. Do not enter spaces between the
numbers. If...
The data below represent commute times (in minutes) and scores on a well-being survey. Complete parts...
The data below represent commute times (in minutes) and scores
on a well-being survey. Complete parts (a) through (d)
below.
Commute Time (minutes), x
55
1515
3030
3535
5050
8484
105105
Well-Being Index Score, y
69.169.1
67.867.8
66.266.2
65.865.8
64.564.5
62.362.3
59.959.9
(a) Find the least-squares regression line treating the
commute time, x, as the explanatory variable and the index score,
y, as the response variable.
ModifyingAbove y with caretyequals=nothingxplus+left parenthesis
nothing right parenthesis
(Round to three decimal places as...
The data below represent commute times (in minutes) and scores on a well-being survey. Complete parts...
The data below represent commute times (in minutes) and scores
on a well-being survey. Complete parts (a) through (d) below.
Commute Time (minutes), x 5 15 30 40 50 84 105 Well-Being Index
Score, y 68.9 67.6 65.8 64.9 64.0 61.5 58.8 (a) Find the
least-squares regression line treating the commute time, x, as the
explanatory variable and the index score, y, as the response
variable. ModifyingAbove y with caret equalsnothingxplusleft
parenthesis nothing right parenthesis (Round to three decimal
places...
The wait times (in minutes) at a pharmacy are shown below. It is claimed that the...
The wait times (in minutes) at a pharmacy are shown below. It
is claimed that the average waiting time is 65 minutes using a 5%
significance level.
73 63 31 18 61 48 76 111 157 76 137 42 42 40 48 62 61 72 31
39.
the value of the statistical test would be + 0. 008?
Calculate the statistical test and answer the question whether
this result is true or false.
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