23. Correlation is used to discover relationships between variables. Evaluate the correlation between the variables in the following DATA: That is the correlation?

variable1

variable2

-0.21582

0.89369

0.56997

-0.72620

-0.54850

-0.09185

-0.12385

0.50086

0.06975

-0.73607

0.16327

0.88498

-0.72595

-0.27512

0.22500

0.62647

-0.40463

0.92432

0.67652

0.56368

-0.82322

0.73005

0.06747

-0.74824

0.74055

0.79412

-0.71577

-0.04509

-0.82231

-0.70951

-0.47603

0.01573

0.58094

0.51169

-0.58573

0.10376

0.19003

-0.90089

-0.49528

0.04767

0.93083

-0.16886

0.61389

-0.65529

-0.91742

0.25296

-0.60957

-0.24747

a.-0.991

b.-0.008

c.None of the answers are correct

d.0.310

e.0.984

24. The equation of the regression line is Y = a + bX.Help me figure out how to Match the following symbols to the description:

___ R

___ b

___ Y

___ R

___ a

___ X

1.Denoted the variable plotted on the horizontal axis and called the explanatory or independent variable.

2.Denotes the variable plotted on the vertical axis and is called the response or dependent variable.

3.The regression result = the change in Y for a change in X of +1, and called the slope.

4.The proportion of variability of Y that is explained by or accountable to X.

5.The strength and direction of the linear relationship between X and Y.

6.The regression result = elevation of the line at X = 0, and called the intercept.

25. Being required to setup a predictive equation involving variable 1 and variable 2. First, we should plot the following DATA: to determine if linear regression applies: Which would we decide:

variable1

variable2

-0.21582

0.89369

0.56997

-0.72620

-0.54850

-0.09185

-0.12385

0.50086

0.06975

-0.73607

0.16327

0.88498

-0.72595

-0.27512

0.22500

0.62647

-0.40463

0.92432

0.67652

0.56368

-0.82322

0.73005

0.06747

-0.74824

0.74055

0.79412

-0.71577

-0.04509

-0.82231

-0.70951

-0.47603

0.01573

0.58094

0.51169

-0.58573

0.10376

0.19003

-0.90089

-0.49528

0.04767

0.93083

-0.16886

0.61389

-0.65529

-0.91742

0.25296

-0.60957

-0.24747

a.You need more information before deciding to use linear regression

b.Linear regression is not applicable because the point pattern is curvilinear (has a curve)

c.Linear regression is not useful because the points have no discernible pattern

d.Linear regression is not applicable because it appears that there are two linear patterns indicating that the data come from two populations.

e.The linear regression equation will be very useful because the points have a strong linear pattern.

26. An important application of regression in manufacturing is the estimation of cost of production. Based on the follow DATA from Ajax Widgets relating to cost (Y) to volume (X), what would be the cost per widget?

Production Volume (units)

Total Cost ($)

400

3430

450

4080

550

4878

600

4884

700

5913

750

6402

425

4273

475

4362

575

5089

625

5446

725

6017

775

6591

a.8.75

b.8.21

c.7.54

d.None of the answers are correct

e.7.38

27. An important application of regression in manufacturing is the estimation of cost of production. Based on the follow DATA from Ajax Widgets relating to cost (Y) to volume (X), what would be the cost of producing 600 widgets?

Production Volume (units)

Total Cost ($)

400

4384

450

4722

550

5233

600

6091

700

6664

750

6734

425

4423

475

4905

575

5746

625

5709

725

7081

775

7094

a.None of the answers are correct.

b.6954

c.6312

d.5206

e.5826