Question

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

Answer #1

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.

Find the equation of the regression line for the given data.
Then construct a scatter plot of the data and draw the regression
line.? (The pair of variables has a significant? correlation.) Then
use the regression equation to predict the value of y for each of
the given? x-values, if meaningful. The table shows the shoe size
and heights? (in) for 6 men.
font size decreased by 1 font size increased by 1 Shoe size comma
x
6.0
9.5
10.0...

Find the equation of the regression line for the given data.
Then construct a scatter plot of the data and draw the regression
line. (The pair of variables has a significant correlation.) Then
use the regression equation to predict the value of y for each of
the given x-values, if meaningful. The table shows the shoe size
and heights (in) for 6 men.
Shoe size, x
6.0
8.5
9.0
12.0
13.0
13.5
(a)
x=size
9.5
(b)
x=size
7.5
Height, y...

a. If r is a negative number, then b (in the line of regression
) is negative.
true or false
b.The line of regression is use to predict the theoric average
value of y that we expect to occur when we know the value of x.
true or false
c. We can predict no matter the strength of the correlation
coefficient.
true or false
d. The set of all possible values of r is, {r: -1< r <
1
treu...

Find the equation of the regression line for the given data.
Then construct a scatter plot of the data and draw the regression
line. (The pair of variables have a significant correlation.)
Then use the regression equation to predict the value of y for each
of the given x-values, if meaningful. The table below shows the
heights (in feet) and the number of stories of six notable
buildings in a city. Height, x: 758, 621, 518, 510, 492, 483
Stories,...

Find the equation of the regression line for the given data.
Then construct a scatter plot of the data and draw the regression
line. (The pair of variables have a significant correlation.)
Then use the regression equation to predict the value of y for each
of the given x-values, if meaningful. The table below shows the
heights (in feet) and the number of stories of six notable
buildings in a city.
Height x= 772 628 518 508 496 483
Stories,...

Find the equation of the regression line for the given data.
Then construct a scatter plot of the data and draw the regression
line. (The pair of variables have a significant correlation.)
Then use the regression equation to predict the value of y for each
of the given x-values, if meaningful. The table below shows the
heights (in feet) and the number of stories of six notable
buildings in a city. Height comma x 764 625 520 510 492 484...

Find the equation of the regression line for the given data.
Then construct a scatter plot of the data and draw the regression
line. (The pair of variables have a significant correlation.)
Then use the regression equation to predict the value of y for each
of the given x-values, if meaningful. The table below shows the
heights (in feet) and the number of stories of six notable
buildings in a city.
Height comma xHeight, x
772
628
518
508
496...

Find the equation of the regression line for the given data.
Then construct a scatter plot of the data and draw the regression
line. (Each pair of variables has a significant correlation.)
Then use the regression equation to predict the value of y for each
of the given x-values, if meaningful. The caloric content and the
sodium content (in milligrams) for 6 beef hot dogs are shown in
the table below.
Calories, x Sodium, y
150 420
170 470...

Find the equation of the regression line for the given data.
Then construct a scatter plot of the data and draw the regression
line. (The pair of variables has a significant correlation.) Then
use the regression equation to predict the value of y for each of
the given x-values, if meaningful. The table shows the shoe size
and heights (in) for 6 men.
font size decreased by 1 font size increased by 1 Shoe size
comma xShoe size, x
8.58.5...

Find the equation of the regression line for the given data.
Then construct a scatter plot of the data and draw the regression
line. (Each pair of variables has a significant correlation.)
Then use the regression equation to predict the value of y for each
of the given x-values, if meaningful. The caloric content and the
sodium content (in milligrams) for 6 beef hot dogs are shown in
the table below.
(a) x=160 calories
(b) x=100 calories
(c) x=130 calories...

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