Requirements.docx Requirements: Font: Times New Roman I NEED 7 APA Style reference and In-text citation Spacing: SINGLE All the number of words are included next to the questions. __________________________________________________________________________________ BSBLDR511 - Develop and use emotional intelligence Questions: 1. Explain emotional intelligence principles and strategies (100 words) 2. Describe the relationship between emotionally effective people and the attainment of business objectives (100 words) 3. Explain how to communicate with a diverse workforce which has varying cultural expressions of emotion (100 words) 4. List at least five (5) examples of emotional strengths and weaknesses. Explain all. (100 words) 5. Identify at least three (3) examples of emotional states you might identify in co-workers in the workplace, and outline the common cues for each. (100 words) 6. Why is it essential to consider varying cultural expressions of emotions when working and responding to emotional cues in a diverse workforce? (100 words) 7. There are a variety of opportunities you may provide in your workplace for others to express their thoughts and feelings. List two (2). ( 100 words) 8. Why is it important to assist others to understand the effect of their behavior and emotions on others in the workplace? ( 100 words) 9. What information will you need to consider to ensure you use the strengths of workgroup members to achieve workplace outcomes? (100 words) Quiz 8 Notes Scatterplots, Correlation and Regression We are turning to our last quiz topic; regression. To get to regression, we need to understand several concepts first. To start with, we will be working with two quantitative variables. The goal is to see if there is a relationship/association between the two variables. As one variable increases, what does the second variable do? If the second variable makes a consistent change then a relationship may exist. MAJOR POINT: saying a relationship exists does NOT mean there is Causation. The greatest abuse of statistical work is here, when a person runs a regression then says Variable A causes Variable B to change. You must have experimental results to establish causation. Looking at the two variables that will be in a regression you need to know that each variable plays a specific role. One of the variables, X, will be the independent/explanatory variable and the other, Y, will be the dependent/ response variable. In a regression we are looking to see if changes in, Y; occur as X changes. It is very important that you establish at the beginning which of your variables will be X and which will be Y. Swapping the places for the two variables may not work. Let’s do an example. In economics, we discuss the relationship of the quantity demand and the price of a good. Which one would be the X in a regression, and which would be, Y? The Law of Demand says, “as the price of a good increases, the quantity demanded decreases”. Which is allow.

1. Requirements.docx
Requirements:
Font: Times New Roman
I NEED 7 APA Style reference and In-text citation
Spacing: SINGLE
All the number of words are included next to the questions.
_____________________________________________________
_____________________________
BSBLDR511 - Develop and use emotional intelligence
Questions:
1. Explain emotional intelligence principles and strategies (100
words)
2. Describe the relationship between emotionally effective
people and the attainment of business objectives (100 words)
3. Explain how to communicate with a diverse workforce which
has varying cultural expressions of emotion (100 words)
4. List at least five (5) examples of emotional strengths and
weaknesses. Explain all. (100 words)
5. Identify at least three (3) examples of emotional states you
might identify in co-workers in the workplace, and outline the
common cues for each. (100 words)
6. Why is it essential to consider varying cultural expressions of
emotions when working and responding to emotional cues in a
diverse workforce? (100 words)
7. There are a variety of opportunities you may provide in your
workplace for others to express their thoughts and feelings. List
two (2). ( 100 words)
8. Why is it important to assist others to understand the effect

2. of their behavior and emotions on others in the workplace? (
100 words)
9. What information will you need to consider to ensure you use
the strengths of workgroup members to achieve workplace
outcomes? (100 words)
Quiz 8 Notes
Scatterplots, Correlation and Regression
We are turning to our last quiz topic; regression. To get to
regression, we need to understand several concepts first.
To start with, we will be working with two quantitative
variables. The goal is to see if there is a relationship/association
between the two variables. As one variable increases, what does
the second variable do? If the second variable makes a
consistent change then a relationship may exist. MAJOR
POINT: saying a relationship exists does NOT mean there is
Causation. The greatest abuse of statistical work is here, when a
person runs a regression then says Variable A causes Variable B
to change. You must have experimental results to establish
causation.
Looking at the two variables that will be in a regression you
need to know that each variable plays a specific role. One of the
variables, X, will be the independent/explanatory variable and
the other, Y, will be the dependent/ response variable. In a
regression we are looking to see if changes in, Y; occur as X
changes. It is very important that you establish at the beginning
which of your variables will be X and which will be Y.
Swapping the places for the two variables may not work. Let’s
do an example.
In economics, we discuss the relationship of the quantity
demand and the price of a good. Which one would be the X in a
regression, and which would be, Y? The Law of Demand says,
“as the price of a good increases, the quantity demanded
decreases”. Which is allowed to change on its own and which
the follows? If you said, price is allowed to change you are

3. correct. So, the price of the good is the X variable and we look
to see how much the quantity demanded changes, Y. It would
not make sense to an economist if you switched the two
variables.
When we go to visualize the two quantitative variables, we are
building what is called a scatterplot. In a scatterplot we put the
independent variable on the X axis (hence the name X variable)
and the dependent variable on the Y axis (hence the name Y
variable). There 5 aspects to a scatterplot that we need to
discuss. This discussion will start to build a common
terminology and common frame of reference.
5 Aspects of a Scatterplot Association
1. Direction – are the points on the scatterplot lining up in a
specific direction?
The left scatterplot above is showing a negative direction
(downward sloping to the right), as the X variable increases, the
Y variable is decreasing. The right scatterplot above is showing
a positive direction (upward sloping to the right), as the X
variable increases so does the Y variable. There is a third
option, no relationship. It will look like what I call a shotgun
pattern, the scatterplot below illustrates.
v
v
v
v

4. v
v
v
2. Form – does the pattern in the scatterplot show a linear or
non-linear relationship? The two scatterplots on the last page
would be good candidates to show a linear relationship. A non-
linear pattern should have a definite “U” shape or “N” shape or
a sideways “S” pattern.
3. Strength – how close are the point on the scatterplot? If they
are very close it is called a “strong” relationship. Somewhat
close, “moderate”; and loosely together, weak.
Strong Moderate
Weak
4. Outliers – is (are) a there point(s) that stick out from the
rest? Outliers can come in three forms:
a. “X” Outliers –

5. The point on the right side of the scatterplot
sticks out in the X direction but not the Y
direction (Up or down). X outliers can
artificially increase the strength of the
relationship.
b. “Y” Outliers –
The point at the top of the scatterplot sticks
out in the Y direction but not in the X Comment by 18154:
Direction (Left or right). Y outliers can shift
the intercept and may be the slope.
c. X & Y outliers – these are outliers that stick out in
both directions. They are known as
“influential” outliers because they can
completely alter the relationship.
vv
vv

6. vv
vv
vv
vv
vv
As you can see, the point up and to the right is the outlier. The
regression model will we work with will try to put a linear line
using all the points, so it might look like the red line (Upward
sloping). When in reality the relationship is more negative like
the blue line (Downward sloping).
5. Trends – does the collections of points suggest a trend
beyond the range of points or a trend within the range of points.
Extending a trend beyond the last point in a range can be very
dangerous because the world is not linear. Going a little beyond
is okay but too far can lead to very incorrect predictions. Within
a range where for some reason an empty space exists is usually
not a problem, as long as the strength is moderate or greater.

7. Yes May be
No
Correlation
Correlation measures the direction and strength of a relationship
between two quantitative variables. Scatterplots can be
manipulated to change the perceived view of a relationship. The
correlation coefficient value, “r”, removes that ability to
manipulate the axes and change the view of the relationship.
Correlation changes all the points on the scatterplot into X and
Y standardized coordinates. Mentally, imagine placing cross
hairs on top of a scatterplot and then calculate the distance for
each point from where the cross hairs intersect. If you use
standard deviations to measure you are doing what the
correlation does.
r = Σ ZX ZY/n - 1
Correlation Properties:
1. “r” will always be between -1 and 1
2. A -1 means a perfect line sloping down to the right. 1 means
a perfect line sloping up to the right.
3. The closer to -1 or 1 the stronger the relationship
4. “r” = 0 means no relationship
5. Correlations have no units
6. Outliers can change the “r” so do correlations with and
without outliers
7. Watch out for Spurious relationships. A spurious relationship
is a false relationship. If you just pick
two quantitative variables that have nothing in common you
may get a high “r” value by chance
8. Correlation does not equal Causation
Ranges for “r”
1. Strong = .7 to 1
2. Moderate = .4 to .7

8. 3. Weak = .1 to .4
4. No relationship = 0 to .1
Same is true in the negative direction
What is a “good” “r” value? It depends on the field of study, to
some degree. Fields that can control for outside variables will
want higher “r” and fields that cannot control as much will
accept lower “r” values.
Examples:
(Rough idea of the differences)
Chemistry wants over .5
Biology wants over .3
Social Sciences over .2
Examples of “r”
1. “r” = .623, you would say something like: It is a moderately,
positive relationship
2. “r” = -.427, you would say a weakly moderate, negative
relationship.
You can add as many adjectives as you need to better describe
what the value is representing. Yes, people express slightly
different views of the same value.
Regression
Correlation tells:
1. That there is an association/relationship
2. It tells what the direction and strength of the
association/relationship is
Correlation does not tell what that association/relationship is.
Regression tries to answers that last part, what is the
association/relationship between the two variables.
We will be using a simple, linear regression. Simple means only

9. one X variable. Linear means we will be fitting a linear line
through the data on the scatterplot. The line will be the “line of
best fit”.
“Line of best Fit” means that the computer will place a line
through the scatterplot points that has the least amount of error
(distance from the line for the data points This is also called the
“sum of the least squares”. The computer is measuring the
distance of each point from the line then squaring them to
remove the negatives. The line that has the smallest sum of the
error is also the line that best fits the data.
Best fit can be misleading. If your data is weak and shotgun
patterned, your error could be very high, and the line may be a
bad representation of the data, but the computer will give you
the line that is the best possibility.
The Model:
Since we are doing a linear regression will be using a linear
equation:
You know this equation as Y = mX + b.
We will adjust that equation for the regression:
Ŷ = β0 + β1X β = Beta
Where ŷ equals the predicted “Y”. Where β0 equals the Y
intercept. Where β1 equals the slope of the line. Using this
equation we are idealizing the data to get a “predicted y” for
every X. The “predicted y” could be close to the actual Y or
Y’s; or quite a way apart.
When a regression is run, the computer will generate the
equation and the “r” and “r2”. “r2” is the correlation coefficient
squared but it explains something very different. “r2” explains
how much of the change in your Y variable occurs as your X
variable changes. The stronger the relationship is, the higher
your “r2” should be. Your regression is capturing more of the
change in Y.
Well, that is that is the basic of scatterplot, correlation and
regression. Now on to the fun stuff, let’s run a regression. I will

10. be following the steps from your computer packet. In the packet
is says to split the data by gender, this was assuming we were
still doing the project. We are not, so you just have to pick two
quantitative variables. Please, use your variables from the
survey.
You do have to determine beforehand which variable will be the
independent/explanatory variable and which one will be the
dependent/response variable.
The steps for the regression are different than we have been
using as you can see from Quiz 8. I am using variables from the
T/R class: Hours Worked and Hours Slept.
Everything in red is either explanation or comment. Also,
remember that copy/paste helps when redoing a test
Step 1: State your Problem
We will run a regression to see if there is a relationship between
the variables, “Average Hours a Week Worked” and “Average
hours of Sleep”. We will make “Worked” our X variable and
“Sleep” our Y variable. (I am saying that the number of hours
worked impacts the number of hours slept. I expect a negative
relationship but who knows?)
Step 2: Input and Visualize Your Data (Produce a Scatterplot)
I just realized I did not put the steps to get a scatterplot in the
packet. In StatCrunch click on “Graph” then click on
“Scatterplot”. New box: Choose your X variable and Y variable
then click “compute”. Copy/paste the scatterplot like the one

11. above.
Step 3: Check the Association and Comment on Each Aspect
1. Direction: Negative (You could argue no relationship)
2. Form: Not Non-linear (Key point here; if the data is truly
non-linear you cannot run the test. I have had only one student
with truly non-linear data. Using “Not Non-linear” is not saying
it is linear, only that a linear line could work)
3. Strength: Weak (You could say very weak)
4. Outliers: Yes at (25, 9) and (65, 5) (These are the points at
the top of the scatterplot and at the lower righthand corner)
(Once you have stated at least one outlier any additional
outliers are totally your choice. If you want to argue there are
no outliers that is okay, but you must explain why) (Pet peeve:
make sure that you have the X value first. I do not want to
spend time figuring out what points you are talking about)
5. Trends: No (This aspect should only have a yes if the
strength is at least moderate)
Step 4: Check the Conditions
1. Random – random enough, student signed up independently
2. Linear – Not Non-linear (Violate this condition you cannot
run the test)
3. Equal Variance Condition: No pattern in the Residual plot
(When you run the regression I have you also producing a
scatterplot of the residuals (error). This checks to see if there is
a pattern in the leftover error once the part explained by the
regression is removed. If a pattern exists that implies another
variable may be impacting your Y variable. In this particular
case there is no pattern. Patterns that need to be checked for; a
funnel shape, a fan tail shape or a non-linear shape. Below is an
example of a funnel shape.)

12. ------------------------------------------------------------
-------------------------
v
The dotted line will NOT appear in your residual plot. I put it
there to help illustrate the funnel pattern. The human mind is
trained to look for patterns but here finding patterns is not the
objective. When you look you must take into account ALL the
points, not just the ones that may support a pattern. I have had
many students think they see a pattern until I point a couple
points that are outside what they think is a pattern.
4. Nearly Normal Condition: (A regression test assumes that the
distribution of your “Y’s”, if you were to have multiple Y’s for
each X value is distributed Normally. You check this condition
by looking at the histogram I have you produce when you do the
regression.)
This histogram is not normal and means there is a violation.
(You cannot change this histogram, just live with it)
5. Outlier Condition: Yes, outliers exist at Yes at (25, 9) and
(65, 5) (This means I will be running with the outliers and
without the outliers, like we did for the 2-mean testing.)
Step 5: Run the Regression

13. Following the directions in the computer packet. Click on
“Stat”, then click on “Regression”, etc.
(Be sure to highlight “Histogram of Residuals and Residuals vs.
X-values. Histogram is for the Nearly Normal Condition and the
Residuals is the scatterplot for the Equal Variance Condition)
(you should get outputs that look like those below)(Also, I
always choose to make the sign for the Intercept Ha a positive
because most intercepts are and I made the Ha for the Slope
negative because that is what I stated in the Aspect Step for
Direction)
Simple linear regression results:
Dependent Variable: var3
Independent Variable: var9
var3 = 6.9254592 - 0.011584884 var9 = regression equation
Sample size: 22
R (correlation coefficient) = -0.16849666 = “r”
R-sq = 0.028391125 = “r2”
Estimate of error standard deviation: 1.0476051
Parameter estimates:
Parameter
Estimate
Std. Err.
Alternative
DF
T-Stat
P-value
Intercept
6.9254592
0.43919667
≠ 0
20
15.768469

14. <0.0001
Slope
-0.011584884
0.015154135
≠ 0
20
-0.76447021
0.4535
Analysis of variance table for regression model:
Source
DF
SS
MS
F-stat
P-value
Model
1
0.64138133
0.64138133
0.5844147
0.4535
Error
20
21.949528
1.0974764
Total
21
22.590909

15. (The part above that starts with, “Analysis of Variance….” is
not needed but you can leave if you copy it in.) (I added the
stuff in red and changed the color for the word, slope and its
value)
The scatterplot above has the regression line added. It does
show a slightly negative relationship
The Residual vs. X-values Scatterplot can be left here or moved
back to the condition section under the Equal Variance
Condition. It shows no pattern, I added the arrows to illustrate
that there is no funnel, fan tail or non-linear pattern.
I put the histogram here also because it comes when you do the
regression. You can leave it here or put in back in the Condition
section like I did. Repeating what I said earlier this is not
normal as the red curve shows normal. This is a violation that
needs to be discussed in the conclusion.
Step 6 State Your Conclusions
We ran the regression and found a “r” = -.1685 and a “r2” =
.0284. The “r” = -.1685 means we have a weak negative
relationship. The “r2” = .0284 means that 2.84% of the change
in the average number of hours slept occurs as the average
number of hours worked changes (Very little change is
explained). The regression equation states that for every 1
additional hour worked, sleep decreases by .012 hours. The p-
value for the slope of the regression is .4535 which means there
is insufficient evidence to say there is relationship between the
variables; “Hours” and “Sleep”. (This p-value would be the p-
value if we ran a hypothesis test on the slope of the regression.
We will discuss this in more detail further on)
We had a violation in the Nearly Normal Condition which
means that the data may not as accurate as needed for the test

16. and this may be impacting the results. We also had 2 outliers so
we will rerun the test without the outliers. (If there had been a
violation of the Equal Variance Condition the language would
be as the following. There was a violation in the Equal Variance
Condition, pattern in the residuals, which means another
variable may be impacting the Y variable.)
Without the Outliers
Step 2 Redo the Scatterplot
Step 3: Check the Association and Comment on Each Aspect
1. Direction: No direction (You could argue negative or
positive)
2. Form: Not Non-linear
3. Strength: Weak to none
4. Outliers: no, outliers have been removed
5. Trends: No
Step 4: Check the Conditions
1. Random – random enough, student signed up independently
2. Linear – Not Non-linear
3. Equal Variance Condition: No pattern in the Residual plot
shown below
4. Nearly Normal Condition: Based on the histogram below,
there is still a violation. The data is not as normally distributed
as needed
5. Outliers: No, outliers have been removed
Step 5: Run the Regression (First try I left Ha for the Slope a
negative. It is wrong, the relationship is slightly positive. I need
to run again. I would not keep this first run but I wanted to
show you that things do change)

17. Simple linear regression results:
Dependent Variable: var3
Independent Variable: var9
var3 = 6.5362284 + 0.0027787202 var9
Sample size: 20
R (correlation coefficient) = 0.040879315
R-sq = 0.0016711184
Estimate of error standard deviation: 0.89057107
Parameter estimates:
Parameter
Estimate
Std. Err.
Alternative
DF
T-Stat
P-value
Intercept
6.5362284
0.41788691
> 0
18
15.641142
<0.0001
Slope
0.0027787202
0.016008173
< 0
18
0.17358134
0.5679
Analysis of variance table for regression model:

18. Source
DF
SS
MS
F-stat
P-value
Model
1
0.023896993
0.023896993
0.030130483
0.8641
Error
18
14.276103
0.79311683
Total
19
14.3
Second Run using a positive sign for Ha in Slope
Simple linear regression results:
Dependent Variable: var3
Independent Variable: var9
var3 = 6.5362284 + 0.0027787202 var9
Sample size: 20
R (correlation coefficient) = 0.040879315
R-sq = 0.0016711184
Estimate of error standard deviation: 0.89057107

19. Parameter estimates:
Parameter
Estimate
Std. Err.
Alternative
DF
T-Stat
P-value
Intercept
6.5362284
0.41788691
> 0
18
15.641142
<0.0001
Slope
0.0027787202
0.016008173
> 0
18
0.17358134
0.4321
Analysis of variance table for regression model:
Source
DF
SS
MS
F-stat
P-value
Model
1
0.023896993

20. 0.023896993
0.030130483
0.8641
Error
18
14.276103
0.79311683
Total
19
14.3
Step 6 State Your Conclusions
We ran the regression and found a “r” = .0409 and a “r2” =
.0017. The “r” = .0409 means we probably have no relationship.
The “r2” = .0017 means that 0.17% of the change in the average
number of hours slept occurs as the average number of hours
worked changes (Very little change is explained). He regression
equation states that for every 1 additional hour worked, sleep
increases by .003 hours. The p-value for the slope of the
regression is .4321 which means there is insufficient evidence
to say there is relationship between the variables; “Hours” and
“Sleep”.
We still had a violation in the Nearly Normal Condition which

21. means that they data may not as accurate as needed for the test
and this may be impacting the results.
After removing the outliers there were some changes:
“r” went from -.1685 to .0409, a change from negative to
positive but also a decrease to point there is probably no
relationship
“r2” went from .0284 to .0017, from capturing a small
percentage of “Sleep’s” change to almost none
Slope for the Regression – went from negative (-.012) to a
positive (.003)
P-value – went from .4535 to .4321, very little change
Overall removing the outliers makes it appear the relationship
went from a negative to a positive relationship. In reality, the
relationship was weak to start with and became weaker after
removing the outliers. With the p-values both above 40% there
is little evidence to say there was a relationship in either run.
(Do not be surprised if you get similar results, especially if you
use a small sample size, one class size of data. If you get
confusing results, contact me and we can work through it.)
It is a lot of work but some of it is repeating and copy/paste
should help.
Notes specific to the Quiz 8
Step 5 (Run the Regression), the first two values in the section
labelled, “Parameter estimates” are the values for the intercept
and slope. Remember this for Questions 7 -10.
High values for “r” and “r2” do not always mean a relationship
has to exist. There must be some logic as to why you are testing
two variables.

22. Bus Stats 205
Quiz 8
Scatterplots, Correlation and Regression
Multiple Choice. Choose the one alternative that best completes
the statement or answers the question
1. Suppose you were to collect data for the pair of given
variables in order to form a scatterplot. Determine if each
variable is independent/explanatory, dependent/response or
whether it could be both.
Profits Levels for the year and Corporate Pay Bonus Rates
for the year
A) Profit Levels for the year – independent/explanatory
Corporate Pa Bonus Rates for the year – dependent/response
B) Profit Levels for the year – dependent/response
Corporate Pa Bonus Rates for the year –
independent/explanatory
C) Profit Levels for the year – either one
Corporate Pa Bonus Rates for the year – either one
Determine whether the scatterplot shows any association, what
direction, if it is linear or not, and how strong is the
association. Choose the MOST COMPLETE ANSWER THAT IS
STILL A CORRECT ANSWER. Most complete being the
answer that will give the greatest description. Start with
direction, then linear, then strength.
2.
vv
vv
vv
vv
vv

23. vv
vv
vv
A) Negative association, Linear association
B) Linear association, strong association
C) Positive association, non-linear association, moderately
strong association
D) Positive association, linear association, moderately strong
association
3.
vv
vv
vv
vv
vv

24. vv
vv
vv
A) Negative association, Linear association, weak association
B) Negative association
C) Linear association, moderately strong association
D) Positive association, linear association, moderately strong
association
4. Determine if the residual plot is appropriate for a linear
model to fit the data. Is there a violation of the Equal Variance
Condition or not?
vv
vv
vv
vv
vv

25. vv
vv
vv
A) The model is appropriate, no pattern exist
B) The model is not appropriate. There is a non-linear pattern
C) The model may not be appropriate. A fan tail pattern exists
5. Which of the points on the scatterplot would be probable
outliers?
A
vv
vv

26. B
vv
vv
vv
vv
C
vv
D
vv
E
A) Points A, C, and E
B) Points A, B, and D
C) Points A, D, and E
D) Points A and C
6. A regression was run on the variables, “Life Expectancy” and
“Computer Ownership” for several nations. Stated below are the
regression equation, the “r” and the “r2” from the regression
test. What is the best conclusion to draw from analysis? (First:
Spurious vs. True relationship, then the reason if a true
relationship exists.
Regression Equation: ŷ = 69.701 + 0.0254x (Y = Years of Life

27. Expectancy; X = Computer per 1000
“r” = .9574
“r2” = .9166
A) Computer ownership promotes health and long life, probably
due to the greater access that computer owners have to health
information on the internet.
B) Persons who live longer buy more computers over the course
of their longer lives.
C) Although the “r” and “r2” are strong, computer ownership
probably does not promote longevity. Instead national per capita
wealth is a lurking variable that drives bot life expectancy and
computer ownership.
D) Clearly, there is some as-yet unknown health benefit
associated with using computers.
SHORT ANSWER: Give a written answer to the following
questions
Using the graph below, answer questions 7 – 10. The graph has
wins a year on the y-axis and millions of dollars spent on salary
Wins
vv

28. vv
vv
vv
vv
vv
vv
vv
Millions of $
“r2” = .94
Variable
Values
Intercept
0.81
Slope
7.65
7. Is a linear model appropriate in this case, yes or no; and

29. why? (3pts)
8. Interpret the meaning of the “r2” value in the context of the
two variables. (3pts)
9. State what the correlation value (‘r”) would be between the
variables. (2pts)
10. State the linear equation by using the data provided in the
box above. (2pts)
11. Create and solve your own original Regression problem
using 2 quantitative variables in StatCrunch. You must have
computer print outs for steps 2, 4 and 5. This problem must start
from raw data; a good place to get this data is from your survey
data if you have it; otherwise you will have to create the data. If
outliers exist, you must run with and without outliers.
1. State your Problem (3pts)
2. Input and Visualize your data (Scatterplot) (3pts)

30. 3. Check the Association and comment on each aspect (5pts)
Direction
Form
Strength
Outliers
Trend
4. Check the Conditions (5pts)
Random
Linear
Equal Variance
Nearly Normal
Outlier
5. Set up and run your test (8pts)
Run the Regression
Print Out should include
Regression Boxes
Scatterplot with the Regression Line
Histogram of Residuals
Residual Scatterplot
6. State your Conclusions (6pts)
Conclusion should discuss:
“r”, “r2”, Slope of the regression, p-value and if any
outliers exist
(You MUST run again if outliers exist)