4. Carry out and state the conclusion of your test on the correlation.
Glencoe Algebra 1, Student Edition, 9780079039897, 0079039898, 2018
18th Edition
ISBN:9780079039897
Author:Carter
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Chapter4: Equations Of Linear Functions
Section4.5: Correlation And Causation
Problem 2AGP
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Contingency Table
A contingency table can be defined as the visual representation of the relationship between two or more categorical variables that can be evaluated and registered. It is a categorical version of the scatterplot, which is used to investigate the linear relationship between two variables. A contingency table is indeed a type of frequency distribution table that displays two variables at the same time.
Binomial Distribution
Binomial is an algebraic expression of the sum or the difference of two terms. Before knowing about binomial distribution, we must know about the binomial theorem.
Topic Video
Question
Part 4
![ld influence the structure of your statistical test.
5. Carry out the statistical test and state your conclusions.
Question 5
A study was carried out to determine the influence of a trace element
found in soil on the yield of potato plants grown in that soil, defined as
the weight of potatoes produced at the end of the season. A large field
was divided up into 14 smaller sections for this experiment. For each
section, the experimenter recorded the amount of the trace element
found (in milligrams per metre squared) and the corresponding weight
of the potatoes produced (in kilograms). This information is presented
in the worksheet 'Dataset5' in the Excel document. Define X as the
trace element amount and Y as the yield.
1. Draw a scatterplot of your data set.
Page 5
2. Set up the framework for an appropriate statistical test to estab-
lish if there is a correlation between the amount of the trace ele-
ment and the yield. Explain how having the scatterplot referred
to above and having the value of r in advance would influence the
structure of your statistical test.
3. Calculate the correlation coefficient for the paired data values.
4. Carry out and state the conclusion of your test on the correlation.](/v2/_next/image?url=https%3A%2F%2Fcontent.bartleby.com%2Fqna-images%2Fquestion%2F5c14568d-3a54-4b34-a6a1-7e302c204fdc%2F89f5925d-3228-4dc9-b5e3-ee695d1a5de0%2F99trof_processed.jpeg&w=3840&q=75)
Transcribed Image Text:ld influence the structure of your statistical test.
5. Carry out the statistical test and state your conclusions.
Question 5
A study was carried out to determine the influence of a trace element
found in soil on the yield of potato plants grown in that soil, defined as
the weight of potatoes produced at the end of the season. A large field
was divided up into 14 smaller sections for this experiment. For each
section, the experimenter recorded the amount of the trace element
found (in milligrams per metre squared) and the corresponding weight
of the potatoes produced (in kilograms). This information is presented
in the worksheet 'Dataset5' in the Excel document. Define X as the
trace element amount and Y as the yield.
1. Draw a scatterplot of your data set.
Page 5
2. Set up the framework for an appropriate statistical test to estab-
lish if there is a correlation between the amount of the trace ele-
ment and the yield. Explain how having the scatterplot referred
to above and having the value of r in advance would influence the
structure of your statistical test.
3. Calculate the correlation coefficient for the paired data values.
4. Carry out and state the conclusion of your test on the correlation.
![ataset 5
Additive
Yield
62.59
91.81
64.45
90.96
61.43
93.39
58.36
96.08
56 |
96.86
59.24
95.76
56.45
96.16
59.96
93.45
63.15
91.06
60.11
93.67
57.29
94.88
60.44
94.79
61.82
95.17
59.5
95.36
Dataset 1
Dataset 2
Dataset 3
Data](/v2/_next/image?url=https%3A%2F%2Fcontent.bartleby.com%2Fqna-images%2Fquestion%2F5c14568d-3a54-4b34-a6a1-7e302c204fdc%2F89f5925d-3228-4dc9-b5e3-ee695d1a5de0%2F7yk83d_processed.jpeg&w=3840&q=75)
Transcribed Image Text:ataset 5
Additive
Yield
62.59
91.81
64.45
90.96
61.43
93.39
58.36
96.08
56 |
96.86
59.24
95.76
56.45
96.16
59.96
93.45
63.15
91.06
60.11
93.67
57.29
94.88
60.44
94.79
61.82
95.17
59.5
95.36
Dataset 1
Dataset 2
Dataset 3
Data
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