To make a prediction for the percentage of bird population remaining after 20 weeks, we can use a trend line or regression analysis to estimate the data.
By looking at the data provided, it appears that the percentage of bird population remaining is decreasing over time. This suggests a linear regression model may be appropriate for this data.
Using the data points provided, we can calculate a regression line to estimate the percentage remaining after 20 weeks.
Based on the data given and the trend line analysis, the best prediction for the percentage of bird population remaining after 20 weeks is 65.9%.
Therefore, the closest option to this prediction is 65.9%.
Park rangers are tracking the population size of a bird each week. The table below shows the percentage of the bird population remaining after each week.
Time (weeks)
1
3
4
6
9
11
12
14
16
% of Population
99
95
93
92
88
84
81
76
72
What is the best prediction of the percentage of bird population remaining in the park after 20 weeks?
Park rangers are tracking the population size of a bird each week. The table below shows the percentage of the bird population remaining after each week.
Time (weeks)
1
3
4
6
9
11
12
14
16
% of Population
99
95
93
92
88
84
81
76
72
What is the best prediction of the percentage of bird population remaining in the park after 20 weeks?
64.5%
60.8%
65.3%
65.9%
7 answers
The table below shows the diameter and height of various trees in a state park.
Diameter
6
3
10
2.5
9
8
7
6.5
4
3
Height
20
15
25
5
24
24
23
22
9
7
What is the correlation coefficient that describes the relationship between diameter and height?
The table below shows the diameter and height of various trees in a state park.
Diameter
6
3
10
2.5
9
8
7
6.5
4
3
Height
20
15
25
5
24
24
23
22
9
7
What is the correlation coefficient that describes the relationship between diameter and height?
r=0.89
r=0.94
r=0.97
r=0.91
Diameter
6
3
10
2.5
9
8
7
6.5
4
3
Height
20
15
25
5
24
24
23
22
9
7
What is the correlation coefficient that describes the relationship between diameter and height?
The table below shows the diameter and height of various trees in a state park.
Diameter
6
3
10
2.5
9
8
7
6.5
4
3
Height
20
15
25
5
24
24
23
22
9
7
What is the correlation coefficient that describes the relationship between diameter and height?
r=0.89
r=0.94
r=0.97
r=0.91
To determine the correlation coefficient that describes the relationship between diameter and height, we can calculate the correlation coefficient using the data provided.
Using a statistical software or calculator, we can input the values for diameter and height to calculate the correlation coefficient.
After running the calculations, the correlation coefficient between diameter and height is approximately 0.97.
Therefore, the closest option to this correlation coefficient is r = 0.97.
Using a statistical software or calculator, we can input the values for diameter and height to calculate the correlation coefficient.
After running the calculations, the correlation coefficient between diameter and height is approximately 0.97.
Therefore, the closest option to this correlation coefficient is r = 0.97.
What does the correlation coefficient tell us about the relationship between the variables in the table?
x
210
100
160
185
200
125
145
170
135
140
y
5
15
20
24
4
18
20
22
19
20
What does the correlation coefficient tell us about the relationship between the variables in the table?
x
210
100
160
185
200
125
145
170
135
140
y
5
15
20
24
4
18
20
22
19
20
Strong positive correlation
Strong negative correlation
Weak positive correlation
Weak negative correlation
x
210
100
160
185
200
125
145
170
135
140
y
5
15
20
24
4
18
20
22
19
20
What does the correlation coefficient tell us about the relationship between the variables in the table?
x
210
100
160
185
200
125
145
170
135
140
y
5
15
20
24
4
18
20
22
19
20
Strong positive correlation
Strong negative correlation
Weak positive correlation
Weak negative correlation
To determine what the correlation coefficient tells us about the relationship between the variables in the table, we need to calculate the correlation coefficient using the data provided.
After calculating the correlation coefficient with statistical software or a calculator, we find that the correlation coefficient for the relationship between variables x and y is approximately 0.94.
A correlation coefficient close to +1 indicates a strong positive correlation, which means that as one variable increases, the other variable also increases.
Therefore, in this case, with a correlation coefficient of 0.94, the relationship between variables x and y can be described as a strong positive correlation. This means that as the values of variable x increase, the values of variable y also tend to increase.
After calculating the correlation coefficient with statistical software or a calculator, we find that the correlation coefficient for the relationship between variables x and y is approximately 0.94.
A correlation coefficient close to +1 indicates a strong positive correlation, which means that as one variable increases, the other variable also increases.
Therefore, in this case, with a correlation coefficient of 0.94, the relationship between variables x and y can be described as a strong positive correlation. This means that as the values of variable x increase, the values of variable y also tend to increase.
Students recorded the total number of pages and chapters in each of several books on the scatterplot. Use this information to answer the attached questions.
3
1 point
Question at position 3
Which regression type best suits the data in the table?
Which regression type best suits the data in the table?
Exponential
Linear
Quadratic
3
1 point
Question at position 3
Which regression type best suits the data in the table?
Which regression type best suits the data in the table?
Exponential
Linear
Quadratic
From the information provided, it is not possible to determine which regression type best suits the data in the table without seeing the actual data points in the scatterplot.
Depending on how the data is distributed in the scatterplot, either a linear, exponential, or quadratic regression model may be the best fit for the data.
A linear regression model is best suited for data that shows a straight-line relationship, while an exponential regression model is best suited for data that shows a rapid increase or decrease. A quadratic regression model is best suited for data that shows a curved relationship.
To determine which regression type best suits the data in the table, it would be necessary to analyze the scatterplot visually or perform regression analysis on the data points.
Depending on how the data is distributed in the scatterplot, either a linear, exponential, or quadratic regression model may be the best fit for the data.
A linear regression model is best suited for data that shows a straight-line relationship, while an exponential regression model is best suited for data that shows a rapid increase or decrease. A quadratic regression model is best suited for data that shows a curved relationship.
To determine which regression type best suits the data in the table, it would be necessary to analyze the scatterplot visually or perform regression analysis on the data points.
1 answer