Question

# A residual is: The difference between a data point and the regression line. A value that...

A residual is:

The difference between a data point and the regression line.

A value that can be 1 or zero.

A value that is always negative because it is a difference

The difference between two different lines.

The properties of r include:

r is sensitive to very high quantities

The value of r is not affected if the values of either variable are converted into a different scale

You must define the independent and dependent variables

All of the above

A simple regression model uses a straight line to make predictions about future events.

True

False

The coefficient of determination:

Represents the percentage of the data that can be explained by the correlation

Is equal to the ratio of the explained variation to the total variation

Is calculated by squaring the correlation coefficient.

All of the above

The assumptions we use to determine the validity of predictions include:

For every specific value of y, the value of x must be normally distributed about the regression line.

The sample was collected carefully

The standard deviation of each dependent variable must be the same for each independent variable

All of the above

Because some people are unable to stand to have their height measured, doctors use the height from the floor to the knee to approximate their patients’ height (in cm).

 Height of Knee Overall Height 57 192 47 153 43 146 44 160 55 171 54 176

a. Use Excel to determine the correlation coefficient of this data

b. Use Excel to determine the regression equation of this data

c. Find the overall height from a knee height of 45.3 cm

d. Find the overall height from a knee height of 52.7 cm

Choose one • 20 points

a. r = 0.73220213

b. Equation: y = 2.0217x + 67.746

c. 159.32901

d. 174.28959

a. r = 0.82544241

b. Equation: y = 2.5109x + 40.79

c. 154.53377

d. 173.11443

a. r = 0.53611996

b. Equation: y = 2.0217x + 67.746

c. 159.32901

d. 174.28959

a. r = 0.908553861

b. Equation: y = 2.5109x + 40.79

c. 154.53377

d. 173.11443

Outliers are usually?

Easy to spot on a scatter plot

Hard to spot on a scatter plot

Not meant to be included on a scatter plot

The last three data points to the right

A positive straight line relationship:

Shows that as the values of y increase, the values of x decrease

Shows that as the values of x increases, the values of y decreases

Shows that as the values of y decreases, the values of x remain constant

Shows that as the values of x increase, the values of y increase

The independent variable is also known as the response variable.

True

False

Once we have a simple regression line, we can use it to predict values for the independent variable X.

True

False

Solution(a)

Its answer is A. I..e A residual is the different e between data points and a regression line.

Solution(b)

Its answer is D. I e all of the above. r is sensitive to very high quantities, The value of r is not affected if the values of either variable are converted into a different scale, You must define the independent and dependent variables, so its answer is D.

Solution (C)

This statement is true that simple regression model uses a straight line to make predictions about future events.

Solution(d)

Its answer is D. I.e. all of the above are correct. Coefficient of determination can be calculated as squaring correlation coefficient, it can be calculated as explained variance divided by total variance. So its answer is D.

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