1.If the the 95% confidence interval for the slope is between6.9and9.9

then you are 95% confident that increasing x by 1 will increase y by between 6.9 and 9.9 on average

Be careful with this question select all the correct options

a.

The corresponding pvalue will be less than 0.05

b.

There is evidence of a negative linear relationship

c.

There is not strong evidence of a relationship

d.

The corresponding Pvalue will not be less than 0.05

e.

There is evidence of a positive linear relationship

2.If the the 95% confidence interval for the slope is between -6.5 and 12.9

then you are 95% confident that increasing x by 1will increase y by between-6.5 and 12.9

Be careful with this question select all the correct options

a.

There is evidence of a positive linear relationship

b.

The corresponding Pvalue will be less than 0.05

c.

There is evidence of a negative linear relationship

d.

There is not strong evidence of a relationship

e.

The corresponding Pvalue will not be less than 0.05

3.If the the 95% confidence interval for the slope is betweenand -9.4 and -4.9

then you are 95% confident that increasing x by 1 will DECREASE y by between 4.9 and 9.4 on average

Be careful with this question select all the correct options

a.

The corresponding pvalue will be less than 0.05

b.

There is evidence of a positive linear relationship

c.

The corresponding Pvalue will not be less than 0.05

d.

There is evidence of a negative linear relationship

e.

There is not strong evidence of a relationship

4.You want to use dataset with two quantitative variables X (advertising)and Y (sales)

so you can predict future values of Y (sales) using X (advertising)

You obtain the following sample statistics

The average of Xis 7.7 with standard deviation 0.24

The average of Y is 9.6 with standard deviation 0.74

correlation coefficient r = 0.73 so if a future value of x is 1 standard deviationabove average so then then the predicted value of y is 0.73 standard deviations above average.

click here for a guide

The following information is not required to do the question

The units are not mentioned, To be realistic you could say that x is measured in millions of dollars so if x=3 then 3 million dollars was spent on adverstising

y could be the number sold could be measured in terms of number of cars sold in thousand so if y=3 then 3 thousand cars were sold.

a.

if afuture value of X is 7.940 (this is 1 standard deviation above average} the predicted Y is 1.340

b.

If afuture value of X is 7.7 the predicted Y is 6.9000

c.

if afuture value of X is 7.9400 (this is 1 standard deviation above average} the predicted Y is 10.1402

d.

If afuture value of X is 7.7 the predicted Y is 18.4000

e.

If afuture value of X is 7.7 the predicted Y is 9.6

f.

if afuture value of X is 7.9400 (so the future value is 1 standard deviation above average} the predicted Y is 12.8402

5.If the p-value of slope is 0.875 and you are 95% confident the slope is between -10 and 9

a.

The p value is less than 0.05 so there is strong evidence of a linear relationship between the variables

b.

The p value is not less than 0.05 so there is not strong evidence of a linear relationship between the variables

6.Suppose you are testing that there is strong evidence there is a linear relationship between 2 quantitative variables and you find the p-value of the slope is 7.5E-4 and you are 95% confident the slope is between 10 and 12

a.

7.5E-4 is the same as 0.000750

b.

Since the p-value is less than 0.05 there is not strong evidence there is a linear relationship between the variables

c.

Since the p-value is less than 0.05 there is strong evidence there is a linear relationship

d.

7.5E-4 is the same as 3.50

7.typethe following dataset into thedataset summarizer

(if pasting works this is fine but it depends on the browser)

XY

1 10

2. 14.5

2 25.5

3 30

What is the p-value of the slope ?

8.Consider the following regression outputof heightX andweight Y for

60 people

coefficientstandard error

Intercept 72.3 10.1

height 1.4 0.1

so the regression line is y=72.3+1.4x

R square 0.74

What is the interpretation of the gradient (this is the slope)

a.

none of the options are correct

b.

all of the options are correct

c.

Increasing height by 1 will increase weight by 1.40

d.

0.74% of the variation of weightis explained by regression line

e.

Increasing height by 72.30 will increase weight by 1.40

f.

10% of the variation in Weight is explained by height

g.

When height is 10 weight is 72.30