Employment Law Please respond to the followingFrom the e-Act.docx

"Employment Law" Please respond to the following:
From the e-Activity, examine the effectiveness of the two (2)
employment law that you deem to be the most influential.
Support your response with two (2) current case involving the
chosen law.
From the e-Activity, select two (2) negative or adverse cases
filed against any organization. Then, decide on a better method
that the organization could have used to prevent the negative or
adverse outcome. Explain the rationale for your described
method.
3/10/2016 Topic Five Homework
http://www.webassign.net/web/Student/Assignment-Responses/l
ast?dep=13397276 2/21
1. –/1.11 pointsMIntroStat8 9.E.045.
As part of the study on ongoing fright symptoms due to exposur
e to horror movies at a young
age, the following table was presented to describe the lasting im
pact these movies have had
during bedtime and waking life:
Waking
symptoms
Bedtime symptoms Yes No
Yes 36 33

No 32 18
(a) What percent of the students have lasting waking-life sympt
oms? (Round your answer
to two decimal places.)
%
(b) What percent of the students have both waking-life and bedt
ime symptoms? (Round
your answer to two decimal places.)
%
(c) Test whether there is an association between waking-life and
bedtime symptoms.
State the null and alternative hypotheses. (Use α = 0.01.)
Null Hypothesis:
H0: Waking symptoms cause bedtime symptoms.
H0: There is no relationship between waking and bedtime sympt
oms.
H0: There is a relationship between waking and bedtime sympto
ms.
H0: Bedtime symptoms cause waking symptoms.
Alternative Hypothesis:
Ha: There is a relationship between waking and bedtime sympto
ms.
Ha: Waking symptoms cause bedtime symptoms.

Ha: Bedtime symptoms cause waking symptoms.
Ha: There is no relationship between waking and bedtime sympt
oms.
State the χ2 statistic and the P-value. (Round your answers for χ
2 and the P-value to
three decimal places.)
ast?dep=13397276 3/21
χ2 =
df =
P =
Conclusion:
We do not have enough evidence to conclude that there is a rela
tionship.
We have enough evidence to conclude that there is a relationshi
p.
2. –/1.11 pointsMIntroStat8 9.E.501.XP.
Here are the row and column totals for a two-way table with two
rows and two columns.
a b 200

c d 200
200 200 400
Find two different sets of counts a, b, c, and d for the body of th
e table. This demonstrates that
the relationship between two variables cannot be obtained solel
y from the two marginal
distributions of the variables. (Start with the given value of a.)
a b c d
Set 1 15
Set 2 145
ast?dep=13397276 4/21
For each of the following situations give the degrees of freedom
and an appropriate bound on the
P-value (give the exact value if you have software available) for
the χ2 statistic for testing the
null hypothesis of no association between the row and column v
ariables.
(a) A 2 by 2 table with χ2 = 0.85.
df =

P-value =
(b) A 4 by 4 table with χ2 = 17.32.
df =
P-value =
(c) A 2 by 8 table with χ2 = 22.51.
df =
P-value =
(d) A 5 by 3 table with χ2 = 12.93.
df =
P-value =
ast?dep=13397276 5/21
A study examined patterns and characteristics of volunteer servi
ce for young people from high
school through early adulthood. Here are some data that can be
used to compare males and
females on participation in unpaid volunteer service or communi
ty service and motivation for
participation.
Participants

Motivation
Gender Strictly Voluntary Court-ordered Other Non-participants
Men 33.3% 2.7% 5.0% 59.0%
Women 44.4% 1.1% 7.7% 46.8%
Note that the percents in each row sum to 100%.
(a) Graphically compare the volunteer-service profiles for men
and women. Describe any
differences that are striking.
Women have a noticeably higher percentage in the strictly volun
tary category.
Women dominate every category.
Men dominate every category.
There are no striking differences.
(b) Find the proportion of men who volunteer. Do the same for
women. Compute the
relative risk of being a volunteer for females versus males. (Rou
nd your answer for
relative risk to two decimal places.)
Men
Women
Relative risk
Write a clear sentence contrasting females and males using relat

ive risk as your
numerical summary.
Men and women participate equally.
A higher percentage of women were participants.
A higher percentage of men were participants.
ast?dep=13397276 6/21
none of the above
A recent study of undergraduates looked at gender differences i
n dieting trends. There were 186
women and 109 men who participated in the survey. The table b
elow summarizes whether a
student tried a low-fat diet or not by gender:
Gender
Tried low-fat diet Women Men
Yes 36 9
No
(a) Fill in the missing cells of the table.

Gender
Tried low-fat diet Women Men
Yes 36 9
No
(b) Summarize the data numerically. What percent of each gend
er has tried low-fat diets?
(Round your answers to two decimal places.)
women %
men %
(c) Test that there is no association between gender and the likel
ihood of trying a low-fat
diet. (Round your χ2 to three decimal places, and round your P-
value to four decimal
places.)
χ2 =
df =
P-value =
Summarize the results.
There is no evidence at the 5% level that gender and the likeliho
od of trying a low-
fat diet are related.
There is strong evidence at the 5% level that gender and the like
lihood of trying a

low-fat diet are related.
ast?dep=13397276 7/21
In what ways do advertisers in magazines use sexual imagery to
appeal to youth? One study
classified each of 1509 full-page or larger ads as "not sexual" or
"sexual," according to the
amount and style of the dress of the male or female model in the
ad. The ads were also classified
according to the target readership of the magazine. Here is the t
wo-way table of counts.
Magazine readership
Model dress Women Men General interest Total
Not sexual 350 509 248 1107
Sexual 215 86 101 402
Total 565 595 349 1509
(a) Summarize the data numerically and graphically. (Compute t
he conditional distribution
of model dress for each audience. Round your answers to three
decimal places.)
Women Men General

Not sexual
Sexual
(b) Perform the significance test that compares the model dress
for the three categories
of magazine readership. Summarize the results of your test and
give your conclusion.
(Use α = 0.01. Round your value for χ2 to two decimal places, a
nd round your P-value to
four decimal places.)
χ2 =
P-value =
Conclusion
Reject the null hypothesis. There is not significant evidence of
an association
between target audience and model dress.
Fail to reject the null hypothesis. There is significant evidence
of an association
Fail to reject the null hypothesis. There is not significant eviden
ce of an association
ast?dep=13397276 8/21

Reject the null hypothesis. There is significant evidence of an a
ssociation between
target audience and model dress.
(c) All of the ads were taken from the March, July, and Novemb
er issues of six magazines
in one year. Discuss this fact from the viewpoint of the validity
of the significance test
and the interpretation of the results.
This is not an SRS. This gives us reason to believe our conclusi
ons might be
suspect.
This is an SRS. This gives us reason to believe our conclusions
might be
suspect.
This is an SRS. This gives us no reason to believe our conclusio
ns are suspect.
This is not an SRS. This gives us no reason to believe our concl
usions are suspect.
ast?dep=13397276 9/21
In what ways do advertisers in magazines use sexual imagery to

appeal to youth? One study
classified each of 1500 full-page or larger ads as "not sexual" or
"sexual," according to the
amount and style of the dress of the male or female model in the
ad. The ads were also classified
according to the age group of the intended readership. Here is a
summary of the data.
Magazine readership age group
Model dress Young adult Mature adult
Not sexual (percent) 72.6% 75.8%
Sexual (percent) 27.4% 24.2%
Number of ads 1000 500
Perform the significance test that compares the model dress for
the age groups of magazine
readership. Summarize the results of your test. (Use α = 0.05. R
ound your χ2 to three decimal
places and round your P-value to four decimal places.)
χ2 =
P-value =
Give your conclusion.
Reject the null hypothesis. There is not significant evidence of
an association between
model dress and age group.
Reject the null hypothesis. There is significant evidence of an a
ssociation between model

dress and age group.
Fail to reject the null hypothesis. There is significant evidence
of an association between
model dress and age group.
Fail to reject the null hypothesis. There is not significant eviden
ce of an association
between model dress and age group.
ast?dep=13397276 10/21
A research project based on a study of older adults examined th
e relationship between physical
activity and pet ownership. The data collected included informat
ion concerning pet owner
characteristics and the type of pet owned. Here are data giving t
he relationship between pet
ownership status and gender.
Pet ownership status
Gender Non-pet owners Dog owners Cat owners
Female 1019 158 86
Male 908 172 81
Analyze the data. (Round your χ2 to three decimal places and yo

ur P-value to four decimal
places.)
χ2 =
df =
P-value =
Summarize your results and conclusions.
The relationship between gender and pet ownership is significan
t.
The relationship between gender and pet ownership is not signif
icant.
ast?dep=13397276 11/21
9. –/1.11 pointsMIntroStat8 10.E.024.
Suppose that there is a linear relationship between the number o
f students x in a school system
and the annual budget y. Write a population regression model to
describe this relationship.
(a) Which parameter in your model is the fixed cost in the budg
et (for example, the
salary of the principals and some administrative costs) that does
not change as x
increases?

b0
β1
εi
β0
b1
(b) Which parameter in your model shows how total cost change
s when there are more
students in the system?
b0
β1
εi
b1
β0
Do you expect this number to be greater than 0 or less than 0?
greater than 0
less than 0
(c) Actual data from various school systems will not fit a straig
ht line exactly. What term
in your model allows variation among schools of the same size
x?
= b0 + b1xi + εi

yi = β0 + β1xi
yi = β0 + β1xi + εi
yi = β0 + β1xi + β2xi + εi
= b0 + b1xi
ast?dep=13397276 12/21
β1
β0
εi
b0
b1
ast?dep=13397276 13/21
10.–/1.11 pointsMIntroStat8 10.E.032.
The index of biotic integrity (IBI) is a measure of the water qua

lity in streams. IBI and land-use
measures for a collection of streams in the Ozark Highland ecor
egion of Arkansas were collected
as part of a study. The data data226.dat gives the data for IBI an
d the area of the watershed in
square kilometers for streams in the original sample with area le
ss than or equal to 70km2.
(a) Use numerical and graphical methods to describe the variabl
e IBI. Do the same for
area. (Round your answers for x to two decimal places and your
answers for s to three
decimal places.)
IBI:
x =
s =
area:
x =
s =
(b) Run the simple linear regression and summarize the results.
(Let x = area and y =
IBI. Round your slope, intercept, and r to three decimal places.
Round F to two decimal
places and your P-value to four decimal places.)
y = + x
F =
P =

r =
Is area of watershed a good predictor for IBI at the 5% significa
nce level?
Yes, the area of watershed is a good predictor for IBI.
No, there is insufficient evidence to say the area of watershed is
a good predictor
for IBI.
Interpret the intercept.
The area of watershed when IBI value is 0
The value of IBI when watershed area is 0
The increase in the watershed area when IBI is increased by one
unit
The increase in IBI when the watershed area is increased by one
unit
Interpret the slope.
The increase in the watershed area when IBI is increased by one
unit
The increase in IBI when the watershed area is increased by one
unit
http://www.webassign.net/mintrostat/data/10_4/data226.dat
ast?dep=13397276 14/21

The value of IBI when watershed area is 0
The area of watershed when IBI value is 0
Interpret the P-value.
If the P-value is less than 0.05 then we can conclude that the wa
tershed area is a
bad predictor for IBI.
If the P-value is greater than 0.05 then we can conclude that the
watershed area is
a good predictor for IBI.
If the P-value if less than 0.05 then we can conclude that the int
ercept is 0
If the P-value is less than 0.05 then we can conclude that the wa
tershed area is a
good predictor for IBI.
The Leaning Tower of Pisa is an architectural wonder. Engineer
s concerned about the tower's
stability have done extensive studies of its increasing tilt. Meas
urements of the lean of the tower
over time provide much useful information. The following table
gives measurements for the years
1975 to 1987. The variable "lean" represents the difference betw
een where a point on the tower
would be if the tower were straight and where it actually is. The
data are coded as tenths of a
millimeter in excess of 2.9 meters, so that the 1975 lean, which
was 2.9642 meters, appears in
the table as 642. Only the last two digits of the year were entere

d into the computer.
(data394.dat)
(a) Plot the data. Consider whether or not the trend in lean over
time appears to be
linear. (Do this on paper. Your instructor may ask you to turn in
this graph.)
(b) What is the equation of the least-squares line? (Round your
answers to two decimal
places.)
y = + x
What percent of the variation in lean is explained by this line? (
Round your answer to one
decimal place.)
%
(c) Give a 99% confidence interval for the average rate of chang
e (tenths of a millimeter
per year) of the lean. (Round your answers to two decimal place
s.)
( , )
ast?dep=13397276 15/21
The SAT and the ACT are the two major standardized tests that

colleges use to evaluate
candidates. Most students take just one of these tests. However,
some students take both. The
data data315.dat gives the scores of 60 students who did this. H
ow can we relate the two tests?
(a) Plot the data with SAT on the x axis and ACT on the y axis.
Describe the overall
pattern and any unusual observations.
(b) Find the least-squares regression line and draw it on your pl
ot. Give the results of the
significance test for the slope. (Round your regression slope and
intercept to three
decimal places, your test statistic to two decimal places, and yo
ur P-value to four decimal
places.)
ACT = + (SAT)
t =
P =
(c) What is the correlation between the two tests? (Round your a
nswer to three decimal
places.)
13.–/1.11 pointsMIntroStat8 10.E.501.XP.
Find a 95% confidence interval for the slope in each of the follo
wing settings. (Round your
answers to three decimal places.)
(a) n = 15, = 1.7 + 12.70x, and

(b) n = 15, = 17.0 + 4.70x, and
(c) n = 100, = 1.7 + 12.70x, and
SEb1 = 68.60
,
SEb1 = 68.60
,
SEb1 = 68.60
,
ast?dep=13397276 16/21
Returns on common stocks in the United States and overseas ap
pear to be growing more closely
correlated as economies become more interdependent. Suppose t
hat the following population
regression line connects the total annual returns (in percent) on
two indexes of stock prices:
MEAN OVERSEAS RETURN = 4.8 + 0.61 × U.S. RETURN
(a) What is β0 in this line?
β0 is the population intercept, 0.61.

β0 is the population slope, 0.61.
What does this number say about overseas returns when the U.S.
market is flat (0%
return)?
This says that the mean overseas return is
% when the U.S. return is
0%.
(b) What is β1 in this line?
What does this number say about the relationship between U.S.
and overseas returns?
This says that when the U.S. return changes by 1%, the mean ov
erseas return
changes by %.
(c) We know that overseas returns will vary in years having the
same return on U.S.
common stocks. Write the regression model based on the popula
tion regression line given
above.

What part of this model allows overseas returns to vary when U.
S. returns remain the
same?
yi = + xi + εi,
where yi and xi are observed overseas and U.S. returns in a give
n year, and εi are
independent N(0, σ) variables.
ast?dep=13397276 17/21
σi
εi
Can a pretest on mathematics skills predict success in a statistic
s course? The 82 students in an
introductory statistics class took a pretest at the beginning of th
e semester. The least-squares
regression line for predicting the score y on the final exam from
the pretest score x was = 9.4
+ 0.77x. The standard error of b1 was 0.41.
(a) Test the null hypothesis that there is no linear relationship b
etween the pretest score
and the score on the final exam against the two-sided alternative
. (Round your test

statistic to three decimal places and your P-value to four decima
l places.)
t =
df =
P =
Conclusion
We reject H0 at the 5% significance level.
We do not reject H0 at the 5% significance level.
(b) Would you reject this null hypothesis versus the one-sided a
lternative that the slope is
positive? Explain your answer.
P =
Conclusion
We could reject H0 at the 5% significance level.
We could not reject H0 at the 5% significance level.
yi
xi
ast?dep=13397276 18/21

How are returns on common stocks in overseas markets related t
o returns in U.S. markets?
Measure U.S. returns by the annual rate of return on the Standar
d & Poor's 500-Stock Index and
overseas returns by the annual rate of return on the Morgan Stan
ley EAFE (Europe, Australasia,
Far East) index. Both are recorded in percents. Regress the EAF
E returns on the S&P 500 returns
for the 24 years 1976 to 2000. Here is part of the output for this
regression. The regression
equation is EAFE = 4.33 + 0.706 S&P. (Round your answer for
F to two decimal places and your
answers for SS and MS to one decimal place.) for
Analysis of Variance
Source DF SS MS F
Regression 1 1462.3 1462.3
Residual
Error
Total 23 13383.5
Complete the analysis of variance table by filling in the missing
boxes. (Round your answer for s
to two decimal places and your answer for r2 to three decimal pl
aces.)
What is s?
What is r2?

ast?dep=13397276 19/21
How are returns on common stocks in overseas markets related t
o returns in U.S. markets?
Measure U.S. returns by the annual rate of return on the Standar
d & Poor's 500-Stock Index and
overseas returns by the annual rate of return on the Morgan Stan
ley EAFE (Europe, Australasia,
Far East) index. Both are recorded in percents. Regress the EAF
E returns on the S&P 500 returns
for the 30 years 1971 to 2000. Here is part of the Minitab output
for this regression. The
regression equation is EAFE = 4.76 + 0.663 S&P.
"Analysis of Variance"
Source DF SS MS F P
Regression 1 3445.9 3445.9 9.5 0.005
Residual Error
Total 29 13598.3
What are the values of the regression standard error s and the sq
uared correlation r 2 ? (Enter
your answers to four decimal places.)

s =
r 2 =
ast?dep=13397276 20/21
We assume that our wages will increase as we gain experience a
nd become more valuable to our
employers. Wages also increase because of inflation. By examin
ing a sample of employees at a
given point in time, we can look at part of the picture. How doe
s length of service (LOS) relate to
wages? The table below gives data on the LOS in months and w
ages for 60 women who work in
Indiana banks. Wages are yearly total income divided by the nu
mber of weeks worked. We have
multiplied wages by a constant for reasons of confidentiality.
Plot wages versus LOS. There is one woman with relatively hig
h wages for her length of service.
Analyze the data with that point excluded. Now analyze the data
with the outlier included.
(a) How does this change the estimates of the parameters β0, β1,
and σ?
The intercept and σ change very little, but the estimate of the sl
ope increases with
the outlier included.

The intercept and σ change very little, but the estimate of the sl
ope decreases with
The slope and σ change very little, but the estimate of the interc
ept increases with
The intercept and slope change very little, but the estimate of σ
increases with the
outlier included.
The intercept and slope change very little, but the estimate of σ
decreases with the
outlier included.
(b) What effect does the outlier have on the results of the signifi
cance test for the slope?
With the outlier, the t statistic increases, and the P-value decrea
ses and is no
ast?dep=13397276 21/21
longer significant at the 5% level.
With the outlier, the t statistic decreases, and the P-value increa
ses but is still
significant at the 5% level.
With the outlier, the t statistic decreases, and the P-value increa
ses and is no

longer significant at the 5% level.
With the outlier, the t statistic increases, and the P-value decrea
ses but is still
With the outlier, the t statistic decreases, and the P-value decrea
ses but is still
(c) How has the width of the 95% confidence interval changed?
The interval width increases by the same factor by which t decre
ased with the
outlier included.
The interval width decreases by the same factor by which t incre
ased with the
outlier included.
The interval width increases by the same factor by which s incre
ased with the
outlier included.
The interval width decreases by the same factor by which s incr
eased with the
outlier included.
The interval width decreases by the same factor by which s decr
eased with the
outlier included.

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Employment Law Please respond to the followingFrom the e-Act.docx

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