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1.) When evaluating a dataset for correlations between variables, we must make a

ID: 261181 • Letter: 1

Question

1.) When evaluating a dataset for correlations between variables, we must make a number of assumptions.
Which of these are assumptions made for correlation testing? SELECT ALL THAT APPLY

The sample of individuals is a random sample of the population.

The relationship between variables X and Y is linear.

The frequency distribution of at least one of the two variables (X and Y) is linearly distributed.

The cloud of points in a scatter plot of X and Y has a circular or elliptical shape.

2.)

Covariance and correlation measures of how 2 variables related to the same sample change together.

True

False

3.)

What is the relationship between these two variables?

seed_mass

seedling_height

5.06

6.91

5.06

7.25

5.50

6.29

5.91

7.91

5.60

5.93

4.54

6.65

6.60

7.47

4.79

6.87

6.15

6.18

5.00

6.14

5.77

6.34

4.33

5.22

5.38

6.70

4.25

6.22

5.41

6.29

5.01

5.66

6.26

6.73

5.07

6.56

5.50

5.89

5.23

7.14

4.24

5.48

5.89

8.36

5.32

6.56

5.56

6.04

4.95

6.97

it is a positive relationship

it is a negative relationship

they are unrelated

they are independent

4.)

The table below displays data from an experiment focused on nutrient addition and plant diversity.

The explanatory variable is _________; the response variable is__________ .

Number of

Nutrients Added

Number of

Plant Species

5.)

Only datasets having a linear relationship between variables can be assessed using regression analyses.

True

False

The sample of individuals is a random sample of the population.

The relationship between variables X and Y is linear.

The frequency distribution of at least one of the two variables (X and Y) is linearly distributed.

The cloud of points in a scatter plot of X and Y has a circular or elliptical shape.

2.)

Covariance and correlation measures of how 2 variables related to the same sample change together.

True

False

3.)

What is the relationship between these two variables?

seed_mass

seedling_height

5.06

6.91

5.06

7.25

5.50

6.29

5.91

7.91

5.60

5.93

4.54

6.65

6.60

7.47

4.79

6.87

6.15

6.18

5.00

6.14

5.77

6.34

4.33

5.22

5.38

6.70

4.25

6.22

5.41

6.29

5.01

5.66

6.26

6.73

5.07

6.56

5.50

5.89

5.23

7.14

4.24

5.48

5.89

8.36

5.32

6.56

5.56

6.04

4.95

6.97

it is a positive relationship

it is a negative relationship

they are unrelated

they are independent

4.)

The table below displays data from an experiment focused on nutrient addition and plant diversity.

The explanatory variable is _________; the response variable is__________ .

Plot #

Number of

Nutrients Added

Number of

Plant Species

1 0 36 2 0 36 3 0 32 4 1 34 5 2 33 6 3 30 7 1 20 8 3 23 9 4 21 10 4 16

5.)

Only datasets having a linear relationship between variables can be assessed using regression analyses.

True

False

Explanation / Answer

Please find the answers below:

Answer 1: Choices 1, 2 (regression and correlation are based upon the assumptions that the population size or sample size is large enough and they have a linear correlation with each other)

Answer 2: True (covariance and correlation both demonstrate the trend of change of one data set with respect to the other, either in positive or negative sense)

Answer 3: is a positive relationship (as the seed mass increases, an increase in plant height is observed in maximum number of cases and vice-versa, thus making it a positive relationship)

Answer 4: Number of nutrients added; number of plant species (the increase in number of plant species depends upon the number of nutrients added)