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you can find attached the file you have to analyze and the materials that we covered in class to know how analyze and what you have to use
Project 1 Overview
INFO 1010: Project 1: Sales
Data
Analysis
Congratulations! You have been hired by Lisa Jones to help run her documentary production company. In this spreadsheet you will find the sales amounts of her sales force. Lisa would like you to analyze the data and provide her with an understanding of how her sales representatives are currently performing. Please be sure to demonstrate bar charts, pie charts, and histograms in your analysis. Your analysis will include a word document with your managerial report and all charts and graphs must be in the word document. You will also submit your Excel spreadsheet. Be sure to watch formatting and look for trends or other analysis you can find in the data. Be sure to upload both your word managerial report and your excel spreadsheet. If you have trouble uploading change your browser – Canvas does not always work well with Chrome – I suggest FireFox.
Data
| Sales Representative | Year | January | February | March | April | May | |||||||||||||||||
| Lebron | 2020 | $ 870,000 | $ 677,000 | $ 665,000 | $ 627,000 | $ 570,000 | |||||||||||||||||
| Wade | $ 840,000 | $ 647,000 | $ 755,000 | $ 797,000 | $ 860,000 | ||||||||||||||||||
| Dirk | $ 470,000 | $ 1,017,000 | $ 1,165,000 | $ 1,287,000 | $ 1,510,000 | ||||||||||||||||||
| Manning | $ 770,000 | $ 897,000 | $ 905,000 | $ 777,000 | |||||||||||||||||||
| Brady | $ 980,000 | $ 917,000 | $ 995,000 | $ 467,000 | $ 940,000 | ||||||||||||||||||
| Halliday | $ 747,000 | $ 805,000 | $ 927,000 | $ 960,000 | |||||||||||||||||||
| Britney | $ 1,000,000 | $ 1,105,000 | $ 1,007,000 | $ 850,000 | |||||||||||||||||||
| Lindsay | $ 857,000 | $ 985,000 | $ 827,000 | $ 990,000 | |||||||||||||||||||
| Paris | $ 1,080,000 | $ 997,000 | $ 855,000 | $ 947,000 | $ 830,000 | ||||||||||||||||||
| JLO | $ 1,060,000 | $ 1,027,000 | $ 1,160,000 | ||||||||||||||||||||
| Emma | $ 1,170,000 | $ 957,000 | $ 1,065,000 | ||||||||||||||||||||
| Melo | $ 1,200,000 | $ 1,295,000 | $ 1,277,000 | $ 1,090,000 | |||||||||||||||||||
| KD | $ 1,157,000 | $ 1,055,000 | $ 1,177,000 | $ 1,320,000 | |||||||||||||||||||
| Flynn | $ 1,140,000 | $ 1,237,000 | $ 1,035,000 | $ 676,000 | |||||||||||||||||||
| Rodgers | $ 1,290,000 | $ 1,175,000 | $ 1,407,000 | ||||||||||||||||||||
| 2021 | $ 955,000 | $ 578,000 | $ 353,000 | $ 345,000 | |||||||||||||||||||
| $ 922,000 | $ 552,500 | $ 865,000 | $ 601,000 | $ 760,000 | |||||||||||||||||||
| $ 515,000 | $ 867,000 | $ 1,625,000 | $ 1,769,000 | ||||||||||||||||||||
| $ 845,000 | $ 765,000 | $ 1,261,000 | $ 1,271,000 | $ 1,362,000 | |||||||||||||||||||
| $ 1,076,000 | $ 782,000 | $ 1,387,000 | $ 575,000 | $ 1,530,000 | |||||||||||||||||||
| $ 637,500 | $ 1,121,000 | $ 587,000 | |||||||||||||||||||||
| $ 1,098,000 | $ 790,500 | $ 667,000 | $ 521,000 | $ 468,000 | |||||||||||||||||||
| $ 933,000 | $ 731,000 | $ 1,373,000 | $ 967,000 | ||||||||||||||||||||
| $ 1,186,000 | $ 1,191,000 | $ 509,000 | $ 466,000 | ||||||||||||||||||||
| $ 1,164,000 | $ 707,000 | ||||||||||||||||||||||
| $ 1,285,000 | $ 1,487,000 | $ 1,485,000 | $ 1,087,000 | ||||||||||||||||||||
| $ 1,318,000 | $ 762,000 | ||||||||||||||||||||||
| $ 1,120,000 | $ 986,000 | $ 1,471,000 | $ 1,473,000 | ||||||||||||||||||||
| $ 1,252,000 | $ 1,054,000 | $ 1,443,000 | $ 659,000 | ||||||||||||||||||||
| $ 1,420,000 | $ 1,639,000 | $ 1,665,000 |
PROJECT 1 RUBRICS
|
Assumptions Clearly Stated: 10 points |
|
Appropriate Use of Frequency Tables, Bar Charts, Histograms, Conditional Formatting, Sparklines: 30 points |
|
Managerial Report Style—Professionalism and Formatting (including Tables and Charts being properly labeled, titled, and formatted: 30 points |
|
Managerial Report Analysis—Quality and Quantity (including Executive Summary and Next Steps): 30 points |
Note: The professor will be glad to review Managerial Reports/Memos and Excel files up until 5:00 pm of the due date. Please use email for reviews. Do not post to Canvas.
INFO 1010
Possible Data Analysis Process for Class Project 1
YOU NEED TO BE VERY FAMILIAR WITH THE DATA BEFORE YOU START TRYING TO FIGURE OUT WHAT QUESTIONS SHOULD BE ASKED!!!
Before you start trying to determine what questions should be asked:
1. Explore and get to know the data
· Make sure you understand what each column stands for.
· Identify the data type and unit ($, lbs., etc.) for each column.
· Choose a few records (rows) and go through the data and see how the columns may or may not be related to each other.
· Then use frequency tables, bar charts, pie charts, and histograms (at a minimum) to analyze the data
2. Explain what results you found interesting and why they are interesting.
· The memo should contain:
· Executive Summary
· Purpose of Memo/Analysis
· Interesting/Useful Analysis/Findings
· Recommendations of Possible Actions/Next Steps
·
In your memo, use tables and charts to support and illustrate your findings!
· Format the reports, tables, and charts so that the reader focuses on what you are trying to say and is not distracted by lack of formatting, poor formatting, over-formatting, etc.
· Remember that most of the time your manager is going to be looking only at your report and not at your actual Excel files.
· Put your name on the report
· Make sure that the report is professional looking.
· You can create your own memo format, but Microsoft Word has many Memo templates from which to choose.
The most common error people make when starting data analysis is coming up with questions to ask before they are intimately familiar with the data!!!
· Remember: Getting the answer is the easy part. The hard part is trying to figure out the right questions to ask.
· Data analysis is an iterative and incremental task.
· You will not be able to do it well if you just make one pass at the data.
INFO 1010
Possible
Data Analysis
Process
for Class Project 1
YOU NEED TO BE VERY FAMILIAR WITH THE DATA BEFORE YOU START TRYING TO FIGURE OUT WHAT
QUESTIONS SHOULD BE ASKED!!!
Before you start trying to determine what questions should be asked:
1.
Explore and get
to know the data
·
Make sure you understand what each
column stands for.
o
Identify the data type
and unit ($, lbs., etc.)
for each column.
·
Choose a few records (rows) and go through the data and see how the columns
may
or
may not
be
related to each other.
·
Then use frequency tables, bar charts, pie charts, and
histograms (at a minimum) to analyze the
data
2
. Explain what results you found interesting and why they are interesting.
·
The memo should contain:
o
Executive
Summary
o
Purpose of Memo/Analysis
o
Intere
sting/Useful Analysis/Findings
o
Recommendations of Possible Actions/Next Steps
·
In your memo, use
tables and charts to support and illustrate your findings!
·
Format the reports, tables, and charts so that the read
er focuses on what you are trying to say
and is not distracted by lack of formatting, poor formatting, over
–
formatting, etc.
o
Remember that most of the time your manager is going to be looking only at your
report and not at your actual Excel files.
o
Put your
name on the report
§
Make sure that
the report
is professional looking.
·
You can create your own memo format, but Microsoft Word has many
Memo
templates from
which to choose.
The most common error
people make when starting data analysis is coming up with qu
estions to ask
before they are intimately familiar with the data!!!
·
Remember: Getting the answer is the easy part. The hard part is trying to figure out the right
questions to ask.
·
Data analysis is an iterative and incremental task.
o
You will not be able
to do it well if you just make one pass at the data.
INFO 1010
Possible Data Analysis Process for Class Project 1
YOU NEED TO BE VERY FAMILIAR WITH THE DATA BEFORE YOU START TRYING TO FIGURE OUT WHAT
QUESTIONS SHOULD BE ASKED!!!
Before you start trying to determine what questions should be asked:
1. Explore and get to know the data
Make sure you understand what each column stands for.
o Identify the data type and unit ($, lbs., etc.) for each column.
Choose a few records (rows) and go through the data and see how the columns may or may not
be related to each other.
Then use frequency tables, bar charts, pie charts, and histograms (at a minimum) to analyze the
data
2. Explain what results you found interesting and why they are interesting.
The memo should contain:
o Executive Summary
o Purpose of Memo/Analysis
o Interesting/Useful Analysis/Findings
o Recommendations of Possible Actions/Next Steps
In your memo, use tables and charts to support and illustrate your findings!
Format the reports, tables, and charts so that the reader focuses on what you are trying to say
and is not distracted by lack of formatting, poor formatting, over-formatting, etc.
o Remember that most of the time your manager is going to be looking only at your
report and not at your actual Excel files.
o Put your name on the report
Make sure that the report is professional looking.
You can create your own memo format, but Microsoft Word has many Memo templates from
which to choose.
The most common error people make when starting data analysis is coming up with questions to ask
before they are intimately familiar with the data!!!
Remember: Getting the answer is the easy part. The hard part is trying to figure out the right
questions to ask.
Data analysis is an iterative and incremental task.
o You will not be able to do it well if you just make one pass at the data.
INFO 1010
DATA MANAGEMENT AND ANALYSIS
‹#›
1
General Stat Concepts
Statistics
Descriptive Statistics
Statistical Inferences
Sample vs. Population
Problem:
Data Deluge
Data Quality
1st Step
Determine Scales of Measurement of Variables
‹#›
Scales Of Measurement
Scales determine the amount of information in the data
Nominal
Ordinal
Interval
Ratio
‹#›
Quantitative vs Categorical
Categorical: Labels or names used to identify an attribute of each element. Can be numeric or nonnumeric
Quantitative: Indicates how much or how many. Always numeric. Can be discrete or continuous.
The statistical analysis that is appropriate depends on whether the data for the variable are categorical or quantitative.
In general, there are more alternatives for statistical analysis when the data are quantitative.
‹#›
Scales of Measurement
Categorical
Quantitative
Numeric
Numeric
Non-numeric
Data
Nominal
Ordinal
Nominal
Ordinal
Interval
Ratio
‹#›
Statistical Inference
Population
Sample
Statistical inference
Census
Sample survey
– the set of all elements of interest in a
particular study
– a subset of the population
– the process of using data obtained
from a sample to make estimates
and test hypotheses about the
characteristics of a population
– collecting data for the entire population
– collecting data for a sample
‹#›
Process of Statistical Inference
1. Population
consists of all tune-
ups. Average cost of
parts is unknown.
2. A sample of 50
engine tune-ups
is examined.
The sample data
provide a sample
average parts cost
of $79 per tune-up.
4. The sample average
is used to estimate the
population average.
‹#›
Ethical Guidelines for Statistical Practice
In a statistical study, unethical behavior can take a
variety of forms including:
Improper sampling
Inappropriate analysis of the data
Development of misleading graphs
Use of inappropriate summary statistics
Biased interpretation of the statistical results
You should strive to be fair, thorough, objective, and
neutral as you collect, analyze, and present data.
As a consumer of statistics, you should also be aware
of the possibility of unethical behavior by others.
‹#›
Ethical Guidelines for Statistical Practice
The American Statistical Association developed the
report “Ethical Guidelines for Statistical Practice”.
Professionalism
Responsibilities to Funders, Clients, Employers
Responsibilities in Publications and Testimony
Responsibilities to Research Subjects
Responsibilities to Research Team Colleagues
The report contains 67 guidelines organized into
eight topic areas:
Responsibilities to Other Statisticians/Practitioners
Responsibilities Regarding Allegations of Misconduct
Responsibilities of Employers Including Organizations,
Individuals, Attorneys, or Other Clients
‹#›
INFO 1010
CHARTS AND DESCRIPTIVE STATISTICS
‹#›
1
Joe’s Diner Example
Guests eating at Joe’s Diner were asked to rate the
quality of their meal as being excellent,
above average, average, below average, or poor. The
ratings provided by a sample of 20 customers are:
Below Average
Above Average
Above Average
Average
Above Average
Average
Above Average
Average
Above Average
Below Average
Poor
Excellent
Above Average
Average
Above Average
Above Average
Below Average
Poor
Above Average
Average
Frequency Distribution
‹#›
Frequency Distribution
Poor
Below Average
Average
Above Average
Excellent
2
3
5
9
1
Total 20
Rating
Frequency
‹#›
Using Excel’s COUNTIF Function
to Construct a Frequency Distribution
‹#›
Using Excel’s COUNTIF Function
to Construct a Frequency Distribution
‹#›
Relative Frequency and
Percent Frequency Distributions
Poor
Below Average
Average
Above Average
Excellent
.10
.15
.25
.45
.05
Total 1.00
10
15
25
45
5
100
Relative
Frequency
Percent
Frequency
Rating
.10(100) = 10
1/20 = .05
‹#›
Using Excel to Construct Relative Frequency and Percent Frequency Distributions
‹#›
Using Excel to Construct Relative Frequency and Percent Frequency Distributions
‹#›
Poor
Below
Average
Average
Above
Average
Excellent
Frequency
Rating
Bar Chart
1
2
3
4
5
6
7
8
9
10
‹#›
Histogram
Another common graphical display of quantitative data is a histogram.
The variable of interest is placed on the horizontal axis.
A rectangle is drawn above each class interval with its height corresponding to the interval’s frequency, relative frequency, or percent frequency.
Unlike a bar graph, a histogram has no natural separation between rectangles of adjacent classes.
‹#›
Histogram
Days on Market for Home Sales
2
4
6
8
10
12
14
16
18
Frequency
10-19 20-29 30-39 40-49 50-59 60-69
When the Format Data Series dialog box appears Set the Gap Width to 0
‹#›
Symmetric
Histograms Showing Skewness
Relative Frequency
.05
.10
.15
.20
.25
.30
.35
0
Left tail is the mirror image of the right tail
Examples: Heights of People
‹#›
Histograms Showing Skewness
Moderately Skewed Left
A longer tail to the left
Example: Exam Scores
Relative Frequency
.05
.10
.15
.20
.25
.30
.35
0
‹#›
Moderately Right Skewed
Histograms Showing Skewness
A Longer tail to the right
Example: Housing Values
Relative Frequency
.05
.10
.15
.20
.25
.30
.35
0
‹#›
Histograms Showing Skewness
Highly Skewed Right
A very long tail to the right
Example: Executive Salaries
Relative Frequency
.05
.10
.15
.20
.25
.30
.35
0
‹#›
Distribution Shape: Skewness
An important measure of the shape of a distribution is called skewness.
The formula for the skewness of sample data is
Skewness =
‹#›
Distribution Shape: Skewness
Symmetric (not skewed)
Relative Frequency
.05
.10
.15
.20
.25
.30
.35
0
Skewness = 0
Skewness is zero.
Mean and median are equal.
‹#›
Relative Frequency
.05
.10
.15
.20
.25
.30
.35
0
Distribution Shape: Skewness
Moderately Skewed Left
Skewness = – .31
Skewness is negative.
Mean will usually be less than the median.
‹#›
Distribution Shape: Skewness
Moderately Skewed Right
Relative Frequency
.05
.10
.15
.20
.25
.30
.35
0
Skewness = .31
Skewness is positive.
Mean will usually be more than the median.
‹#›
Distribution Shape: Skewness
Highly Skewed Right
Relative Frequency
.05
.10
.15
.20
.25
.30
.35
0
Skewness = 1.25
Skewness is positive (often above 1.0).
Mean will usually be more than the median.
‹#›
Seventy efficiency apartments were randomly
sampled in a college town. The monthly rent prices
for the apartments are listed below in ascending order.
Distribution Shape: Skewness
Example: Apartment Rents
525
530
530
535
535
535
535
535
540
540
540
540
540
545
545
545
545
545
550
550
550
550
550
550
550
560
560
560
565
565
565
570
570
572
575
575
575
580
580
580
580
585
590
590
590
600
600
600
600
610
610
615
625
625
625
635
649
650
670
670
675
675
680
690
700
700
700
700
715
715
‹#›
Relative Frequency
.05
.10
.15
.20
.25
.30
.35
0
Skewness = .92
Distribution Shape: Skewness
Example: Apartment Rents
‹#›
A
B
C
D
1
Quality Rating
Quality Rating
Frequency
2
Above Average
Poor
=COUNTIF($A$2:$A$21,C2)
3
Below Average
Below Average
=COUNTIF($A$2:$A$21,C3)
4
Above Average
Average
=COUNTIF($A$2:$A$21,C4)
5
Average
Above Average
=COUNTIF($A$2:$A$21,C5)
6
Average
Excellent
=COUNTIF($A$2:$A$21,C6)
7
Above Average
Total
=SUM(D2:D6)
8
Above Average
A
B
C
D
1
Quality Rating
Quality Rating
Frequency
2
Above Average
Poor
2
3
Below Average
Below Average
3
4
Above Average
Average
5
5
Average
Above Average
9
6
Average
Excellent
1
7
Above Average
Total
20
8
Above Average
C
D
E
F
1
Quality Rating
Frequency
Relative
Frequency
Percent
Frequency
2
Poor
=COUNTIF($A$2:$A$21,C2)
=D2/$D$7
=E2*100
3
Below Average
=COUNTIF($A$2:$A$21,C3)
=D3/$D$7
=E3*100
4
Average
=COUNTIF($A$2:$A$21,C4)
=D4/$D$7
=E4*100
5
Above Average
=COUNTIF($A$2:$A$21,C5)
=D5/$D$7
=E5*100
6
Excellent
=COUNTIF($A$2:$A$21,C6)
=D6/$D$7
=E6*100
7
Total
=SUM(D2:D6)
=SUM(E2:E6)
=SUM(F2:F6)
8
C
D
E
F
1
Quality Rating
Frequency
Relative
Frequency
Percent
Frequency
2
Poor
2
0.10
10
3
Below Average
3
0.15
15
4
Average
5
0.25
25
5
Above Average
9
0.45
45
6
Excellent
1
0.05
5
7
Total
20
1.00
100
8
INFO 1010
SINGLE VARIABLE
CENTRAL TENDENCY MEASURES
‹#›
1
Measures of Location
If the measures are computed
for data from a sample, they
are called sample statistics.
If the measures are computed for
data from a population, they are
called population parameters.
A sample statistic is referred to as
the point estimator of the
corresponding population parameter.
Mean
Median
Mode
Percentiles
Quartiles
Weighted Mean
Geometric Mean
‹#›
Sample Mean
Number of
observations
in the sample
Sum of the values
of the n observations
‹#›
Population Mean m
Number of
observations in
the population
Sum of the values
of the N observations
‹#›
Weighted Mean
Denominator:
sum of the
weights
Numerator:
sum of the weighted
data values
If data is from
a population,
m replaces .
where:
xi = value of observation i
wi = weight for observation i
‹#›
Geometric Mean
= [(x1)(x2)…(xn)]1/n
Excel’s geometric mean function is:
=GEOMEAN(data cell range)
‹#›
Excel Formula Worksheet
Using Excel to Compute
the Mean, Median, and Mode
A
B
C
D
E
1
Apart-
ment
Monthly
Rent ($)
2
1
545
Mean
=AVERAGE(B2:B71)
3
2
715
Median
=MEDIAN(B2:B71)
4
3
530
Mode
=MODE.SNGL(B2:B71)
5
4
690
6
5
535
‹#›
Percentiles
A percentile provides information about how the data are spread over the interval from the smallest value to the largest value.
Admission test scores for colleges and universities are frequently reported in terms of percentiles.
The pth percentile of a data set is a value such that at least p percent of the items take on this value or less and at least (100 – p) percent of the items take on this value or more.
PERCENTILE.EXC(data range, p/100)
‹#›
Quartiles
Quartiles are specific percentiles.
First Quartile = 25th Percentile
Second Quartile = 50th Percentile = Median
Third Quartile = 75th Percentile
QUARTILE.EXC (data range, quartile number)
‹#›
INFO 1010
SINGLE VARIABLE MEASURES OF VARIABILITY
‹#›
1
Measures of Variability
It is often desirable to consider measures of variability (dispersion), as well as measures of location.
For example, in choosing supplier A or supplier B we might consider not only the average delivery time for each, but also the variability in delivery time for each.
‹#›
Measures of Variability
Range
Interquartile Range
Variance
Standard Deviation
Coefficient of Variation
‹#›
Interquartile Range
The interquartile range of a data set is the difference between the third quartile and the first quartile.
It is the range for the middle 50% of the data.
It overcomes the sensitivity to extreme data values.
‹#›
525
530
530
535
535
535
535
535
540
540
540
540
540
545
545
545
545
545
550
550
550
550
550
550
550
560
560
560
565
565
565
570
570
572
575
575
575
580
580
580
580
585
590
590
590
600
600
600
600
610
610
615
625
625
625
635
649
650
670
670
675
675
680
690
700
700
700
700
715
715
Interquartile Range
3rd Quartile (Q3) = 625
1st Quartile (Q1) = 545
IQR = Q3 – Q1 = 625 – 545 = 80
‹#›
Variance
The variance is a measure of variability that utilizes all the data.
It is based on the difference between the value of each observation (xi) and the mean ( for a sample, m for a population).
The variance is useful in comparing the variability of two or more variables.
=VAR.S(data cell range)
‹#›
Standard Deviation
The standard deviation of a data set is the positive square root of the variance.
It is measured in the same units as the data, making it more easily interpreted than the variance.
STDEV.S(data cell range)
‹#›
Standard
deviation is
about 9%
of the mean
Variance
Standard Deviation
Coefficient of Variation
Sample Variance, Standard Deviation,
And Coefficient of Variation
Apartment Rents
s2 = = 2,996.16
s =
% =
‹#›
Measures of Distribution
Distribution Shape
z-Scores
Chebyshev’s Theorem
Empirical Rule
Detecting Outliers
‹#›
Distribution Shape: Skewness
An important measure of the shape of a distribution is called skewness.
The formula for the skewness of sample data is
Skewness =
‹#›
Distribution Shape: Skewness
Symmetric (not skewed)
Relative Frequency
.05
.10
.15
.20
.25
.30
.35
0
Skewness = 0
Skewness is zero.
Mean and median are equal.
‹#›
Relative Frequency
.05
.10
.15
.20
.25
.30
.35
0
Distribution Shape: Skewness
Moderately Skewed Left
Skewness = – .31
Skewness is negative.
Mean will usually be less than the median.
‹#›
Distribution Shape: Skewness
Moderately Skewed Right
Relative Frequency
.05
.10
.15
.20
.25
.30
.35
0
Skewness = .31
Skewness is positive.
Mean will usually be more than the median.
‹#›
Distribution Shape: Skewness
Highly Skewed Right
Relative Frequency
.05
.10
.15
.20
.25
.30
.35
0
Skewness = 1.25
Skewness is positive (often above 1.0).
Mean will usually be more than the median.
‹#›
Seventy efficiency apartments were randomly
sampled in a college town. The monthly rent prices
for the apartments are listed below in ascending order.
Distribution Shape: Skewness
Example: Apartment Rents
525
530
530
535
535
535
535
535
540
540
540
540
540
545
545
545
545
545
550
550
550
550
550
550
550
560
560
560
565
565
565
570
570
572
575
575
575
580
580
580
580
585
590
590
590
600
600
600
600
610
610
615
625
625
625
635
649
650
670
670
675
675
680
690
700
700
700
700
715
715
‹#›
Relative Frequency
.05
.10
.15
.20
.25
.30
.35
0
Skewness = .92
Distribution Shape: Skewness
Example: Apartment Rents
‹#›
Empirical Rule
When the data are believed to approximate a
bell-shaped distribution …
The empirical rule is based on the normal
distribution, which is covered in Chapter 6.
The empirical rule can be used to determine the
percentage of data values that must be within a
specified number of standard deviations of the
mean.
‹#›
Empirical Rule
For data having a bell-shaped distribution:
of the values of a normal random variable
are within of its mean.
68.26%
+/- 1 standard deviation
of the values of a normal random variable
are within of its mean.
95.44%
+/- 2 standard deviations
of the values of a normal random variable
are within of its mean.
99.72%
+/- 3 standard deviations
‹#›
Empirical Rule
x
m – 3s
m – 1s
m – 2s
m + 1s
m + 2s
m + 3s
m
68.26%
95.44%
99.72%
‹#›
z-Scores
Standardized Values for Apartment Rents
‹#›
The z-score is often called the standardized value.
It denotes the number of standard deviations a data
value xi is from the mean.
z-Scores
Excel’s STANDARDIZE function can be used to
compute the z-score.
=
‹#›
z-Scores
A data value less than the sample mean will have a
z-score less than zero.
A data value greater than the sample mean will have
a z-score greater than zero.
A data value equal to the sample mean will have a
z-score of zero.
An observation’s z-score is a measure of the relative
location of the observation in a data set.
‹#›
Z-Scores and Distributions
‹#›
Detecting Outliers
An outlier is an unusually small or unusually large
value in a data set.
A data value with a z-score less than -3 or greater
than +3 might be considered an outlier.
It might be:
an incorrectly recorded data value
a data value that was incorrectly included in the
data set
a correctly recorded data value that belongs in
the data set
‹#›
Chebyshev’s Theorem
At least (1 – 1/z2) of the items in any data set will be within z standard
deviations of the mean, where z is any value greater than 1.
Chebyshev’s theorem requires z > 1, but z need not be an integer.
At least of the data values must be
within of the mean.
75%
z = 2 standard deviations
At least of the data values must be
within of the mean.
89%
z = 3 standard deviations
At least of the data values must be
within of the mean.
94%
z = 4 standard deviations
‹#›
Five-Number Summaries
and Box Plots
Summary statistics and easy-to-draw graphs can be
used to quickly summarize large quantities of data.
Two tools that accomplish this are five-number
summaries and box plots.
‹#›
Five-Number Summary
1
Smallest Value
First Quartile
Median
Third Quartile
Largest Value
2
3
4
5
‹#›
Box Plot
A box plot is a graphical summary of data that is
based on a five-number summary.
A key to the development of a box plot is the
computation of the median and the quartiles Q1 and
Q3.
Box plots provide another way to identify outliers.
‹#›
Box Plot
Whiskers (dashed lines) are drawn from the ends of the box to the smallest and largest data values
inside the limits.
500
525
550
575
600
625
650
675
700
725
Smallest value
inside limits = 525
Largest value
inside limits = 715
‹#›
Data Dashboards:
Adding Numerical Measures
to Improve Effectiveness
The addition of numerical measures, such as the mean
and standard deviation of KPIs, to a data dashboard
is often critical.
Drilling down refers to functionality in interactive
dashboards that allows the user to access information
and analyses at increasingly detailed level.
Dashboards are often interactive.
Data dashboards are not limited to graphical displays.
‹#›
‹#›
-1.20
-1.11
-1.11
-1.02
-1.02
-1.02
-1.02
-1.02
-0.93
-0.93
-0.93
-0.93
-0.93
-0.84
-0.84
-0.84
-0.84
-0.84
-0.75
-0.75
-0.75
-0.75
-0.75
-0.75
-0.75
-0.56
-0.56
-0.56
-0.47
-0.47
-0.47
-0.38
-0.38
-0.34
-0.29
-0.29
-0.29
-0.20
-0.20
-0.20
-0.20
-0.11
-0.01
-0.01
-0.01
0.17
0.17
0.17
0.17
0.35
0.35
0.44
0.62
0.62
0.62
0.81
1.06
1.08
1.45
1.45
1.54
1.54
1.63
1.81
1.99
1.99
1.99
1.99
2.27
2.27
INFO 1010
DATA MANAGEMENT
AND
ANALYSIS
OVERVIEW
‹#›
1
COMPETING IN THE
INFORMATION AGE
Fact – The confirmation or validation of an event or object
Information age – The present time, during which infinite quantities of facts are widely available to anyone who can use a computer
‹#›
2
COMPETING IN THE
INFORMATION AGE
The core drivers of the information age
Data
Information
Business intelligence
Knowledge
‹#›
3
Data
Data – Raw facts that describe the characteristics of an event or object
‹#›
4
Information
Information – Data converted into a meaningful and useful context
‹#›
5
Business Intelligence
Business intelligence – Information collected from multiple sources such as suppliers, customers, competitors, partners, and industries that analyzes patterns, trends, and relationships for strategic decision making
‹#›
6
Knowledge
Knowledge – Skills, experience, and expertise coupled with information and intelligence that creates a person’s intellectual resources
Knowledge worker – Individual valued for their ability to interpret and analyze information
‹#›
7
THE MIS SOLUTION
‹#›
8
SYSTEMS THINKING
Systems thinking – A way of monitoring the entire system by viewing multiple inputs being processed or transformed to produce outputs while continuously gathering feedback on each part
‹#›
9
SYSTEMS THINKING
Management Information Systems (MIS) – A business function, like accounting and human resources, which moves information about people, products, and processes across the company to facilitate decision-making and problem-solving
‹#›
10
IDENTIFYING COMPETITIVE ADVANTAGES
‹#›
11
IDENTIFYING COMPETITIVE ADVANTAGES
Competitive advantage – A product or service that an organization’s customers place a greater value on than similar offerings from a competitor
First-mover advantage – Occurs when an organization can significantly impact its market share by being first to market with a competitive advantage
‹#›
12
Ethical Guidelines for Statistical Practice
In a statistical study, unethical behavior can take a
variety of forms including:
Improper sampling
Inappropriate analysis of the data
Development of misleading graphs
Use of inappropriate summary statistics
Biased interpretation of the statistical results
You should strive to be fair, thorough, objective, and
neutral as you collect, analyze, and present data.
As a consumer of statistics, you should also be aware
of the possibility of unethical behavior by others.
‹#›
CHAPTER TWO
DECISIONS AND PROCESSES
VALUE DRIVEN BUSINESS
© The McGraw-Hill Companies, All Rights Reserved
‹#›
1
SECTION
2
.1
DECISION SUPPORT SYSTEMS
© The McGraw-Hill Companies, All Rights Reserved
‹#›
2
MAKING ORGANIZATIONAL BUSINESS DECISIONS
Managerial decision-making challenges
Analyze large amounts of information
Apply sophisticated analysis techniques
Make decisions quickly
‹#›
3
The Decision-Making Process
The six-step decision-making process
Problem identification
Data collection
Solution generation
Solution test
Solution selection
Solution implementation
‹#›
4
The Decision-Making Process
‹#›
5
Decision-Making Essentials
Decision-making and problem-solving occur at each level in an organization
‹#›
6
Decision-Making Essentials
Operational decision making – Employees develop, control, and maintain core business activities required to run the day-to-day operations
Structured decisions – Situations where established processes offer potential solutions
OPERATIONAL
‹#›
7
Decision-Making Essentials
Managerial decision making – Employees evaluate company operations to identify, adapt to, and leverage change
Semistructured decisions – Occur in situations in which a few established processes help to evaluate potential solutions, but not enough to lead to a definite recommended decision
MANAGERIAL
‹#›
8
Decision-Making Essentials
Strategic decision making – Managers develop overall strategies, goals, and objectives
Unstructured decisions – Occurs in situations in which no procedures or rules exist to guide decision makers toward the correct choice
STRATEGIC
‹#›
9
MEASURING ORGANIZATIONAL BUSINESS DECISIONS
Project – A temporary activity a company undertakes to create a unique product, service, or result
Metrics – Measurements that evaluate results to determine whether a project is meeting its goals
‹#›
10
MEASURING ORGANIZATIONAL BUSINESS DECISIONS
Critical success factors (CSFs) – The crucial steps companies make to perform to achieve their goals and objectives and implement strategies
Create high-quality products
Retain competitive advantages
Reduce product costs
Increase customer satisfaction
Hire and retain the best professionals
‹#›
11
MEASURING ORGANIZATIONAL BUSINESS DECISIONS
Key performance indicators (KPIs) – The quantifiable metrics a company uses to evaluate progress toward critical success factors
Turnover rates of employees
Number of product returns
Number of new customers
Average customer spending
‹#›
12
MEASURING ORGANIZATIONAL BUSINESS DECISIONS
‹#›
13
MEASURING ORGANIZATIONAL BUSINESS DECISIONS
External KPI
Market share – The portion of the market that a firm captures (external)
Internal KPI
Return on investment (ROI) – Indicates the earning power of a project
‹#›
14
Efficiency and Effectiveness Metrics
Efficiency MIS metrics – Measure the performance of MIS itself, such as throughput, transaction speed, and system availability
Effectiveness MIS metrics – Measures the impact MIS has on business processes and activities, including customer satisfaction and customer conversation rates
‹#›
15
The Interrelationship Between Efficiency and Effectiveness Metrics
Ideal operation occurs in the upper right corner
‹#›
16
USING MIS TO MAKE BUSINESS DECISIONS
Model – A simplified representation or abstraction of reality
Models help managers to
Calculate risks
Understand uncertainty
Change variables
Manipulate time to make decisions
‹#›
17
USING MIS TO MAKE BUSINESS DECISIONS
Types of Decision Making MIS Systems
‹#›
18
Operational Support Systems
Transaction processing system (TPS) – Basic business system that serves the operational level and assists in making structured decisions
Online transaction processing (OLTP) – Capturing of transaction and event information using technology to process, store, and update
Source document – The original transaction record
‹#›
19
Operational Support Systems
Systems Thinking View of a TPS
‹#›
20
Managerial Support Systems
Online analytical processing (OLAP) – Manipulation of information to create business intelligence in support of strategic decision making
Decision support system (DSS) – Models information to support managers and business professionals during the decision-making process
‹#›
21
Managerial Support Systems
Four quantitative models used by DSSs include
What-if analysis
Sensitivity analysis
Goal-seeking analysis
Optimization analysis
‹#›
22
Managerial Support Systems
Systems Thinking View of a DSS
‹#›
23
Managerial Support Systems
Interaction Between a TPS and DSS
‹#›
24
Strategic Support Systems
Information Levels Throughout An Organization
‹#›
25
Strategic Support Systems
Executive information system (EIS) – A specialized DSS that supports senior level executives within the organization
Granularity
Visualization
Digital dashboard
‹#›
26
Strategic Support Systems
Most EISs offering the following capabilities
Consolidation
Drill-down
Slice-and-dice
Pivot
‹#›
27
Artificial intelligence (AI) – Simulates human intelligence such as the ability to reason and learn
Intelligent system – Various commercial applications of artificial intelligence
USING AI TO MAKE BUSINESS DECISIONS
‹#›
28
CHAPTER TWO
DECISIONS AND PROCESSES
VALUE DRIVEN BUSINESS
© The McGraw-Hill Companies, All Rights Reserved
‹#›
1
SECTION
2
.2
BUSINESS PROCESSES
© The McGraw-Hill Companies, All Rights Reserved
‹#›
2
MANAGING BUSINESS PROCESSES
Businesses gain a competitive edge when they minimize costs and streamline business processes
‹#›
3
MANAGING BUSINESS PROCESSES
Customer facing process – Results in a product or service that is received by an organization’s external customer
Business facing process – Invisible to the external customer but essential to the effective management of the business
‹#›
4
BUSINESS PROCESS MODELING
Business process modeling (or mapping) – The activity of creating a detailed flow chart or process map of a work process showing its inputs, tasks, and activities, in a structured sequence
Business process model – A graphic description of a process, showing the sequence of process tasks, which is developed for a specific
As-Is process model
To-Be process model
‹#›
5
BUSINESS PROCESS MODELING
‹#›
6
BUSINESS PROCESS MODELING
‹#›
7
BUSINESS PROCESS MODELING
‹#›
8
BUSINESS PROCESS MODELING
‹#›
9
BUSINESS PROCESS MODELING
‹#›
10
USING MIS TO IMPROVE BUSINESS PROCESSES
Types of change an organization can achieve, along with the magnitudes of change and the potential business benefit
‹#›
11
OPERATIONAL BUSINESS PROCESSES AUTOMATION
Customers are demanding better products and services
Business process improvement – Attempts to understand and measure the current process and make performance improvements accordingly
Automation – The process of computerizing manual tasks
‹#›
12
MANAGERIAL BUSINESS PROCESSES STREAMLINING
Streamlining – Improves business process efficiencies by simplifying or eliminating unnecessary steps
Bottleneck – Occur when resources reach full capacity and cannot handle any additional demands
Redundancy – Occurs when a task or activity is unnecessarily repeated
‹#›
13
STRATEGIC BUSINESS PROCESSES REENGINEERING
A company can improve the way it travels the road by moving from foot to horse and then horse to car
BPR looks at taking a different path, such as an airplane which ignore the road completely
‹#›
14
