School

University of Phoenix **We aren't endorsed by this school

Course

DAT 565

Subject

Statistics

Date

Aug 27, 2023

Pages

11

Uploaded by DoctorCamelMaster898 on coursehero.com

Basic Business Statistics
DCOVA Framework
To minimize errors, you use a framework that organizes the set
of tasks that you follow to apply statistics properly. The five tasks that comprise the
DCOVA framework
are:
•
D
efine the data that you want to study to solve a problem or meet an
objective.
•
C
ollect the data from appropriate sources.
•
O
rganize the data collected, by developing tables.
•
V
isualize the data collected, by developing charts.
•
A
nalyze the data collected, to reach conclusions and present those results.
You must always do the
D
efine and
C
ollect tasks before doing the other three. The
order of the
other three varies and sometimes all three are done concurrently. In this book, you
will learn
more about the
D
efine and
C
ollect tasks in Chapter 1 and then be introduced to the
O
rganize
and
V
isualize tasks in Chapter 2. Beginning with Chapter 3, you will learn methods
that help
complete the
A
nalyze task.
1
Defining and Collecting Data 16
(
D
efine &
C
ollect)
2
Organizing and Visualizing Variables 41
(
O
rganize &
V
isualize)
3
Numerical Descriptive Measures 120
(
A
nalyze from hereafter)
4
Basic Probability 168
5
Discrete Probability Distributions 199
6
The Normal Distribution and Other Continuous Distributions 223
7
Sampling Distributions 252
8
Confidence Interval Estimation 275
9
Fundamentals of Hypothesis Testing: One-Sample Tests 311
10
Two-Sample Tests 351
11
Analysis of Variance 398
12
Chi-Square and Nonparametric Tests 440
13
Simple Linear Regression 484
14
Introduction to Multiple Regression 536
15
Multiple Regression Model Building 592
16
Time-Series Forecasting 629
17
Business Analytics 678
18
Getting Ready to Analyze Data in the Future 704
19
Statistical Applications in Quality Management (
online
) 19-1
20
Decision Making (
online
) 20-1
1
Defining and Collecting Data 16
1.1
Defining Variables 17
Classifying Variables by Type 17
Measurement Scales 18
1.2
Collecting Data 19
Populations and Samples 20
Data Sources 20

1.3
Types of Sampling Methods 21
Simple Random Sample 22
Systematic Sample 22
Stratified Sample 23
Cluster Sample 23
1.4
Data Cleaning 24
Invalid Variable Values 25
Coding Errors 25
Data Integration Errors 25
Missing Values 26
Algorithmic Cleaning of Extreme Numerical Values 26
1.5
Other Data Preprocessing Tasks 26
Data Formatting 26
Stacking and Unstacking Data 27
Recoding Variables 27
1.6
Types of Survey Errors 28
Coverage Error 28
Nonresponse Error 28
Sampling Error 28
Measurement Error 29
Ethical Issues About Surveys 29
2
Organizing and Visualizing Variables 41
2.1
Organizing Categorical Variables 42
The Summary Table 42
The Contingency Table 43
2.2
Organizing Numerical Variables 46
The Frequency Distribution 47
Classes and Excel Bins 49
The Relative Frequency Distribution and the
Percentage Distribution 49
The Cumulative Distribution 51
2.3
Visualizing Categorical Variables 54
The Bar Chart 54
The Pie Chart and the Doughnut Chart 55
The Pareto Chart 56
Visualizing Two Categorical Variables 58
2.4
Visualizing Numerical Variables 61
The Stem-and-Leaf Display 61
The Histogram 61
The Percentage Polygon 63
The Cumulative Percentage Polygon (Ogive) 64
2.5
Visualizing Two Numerical Variables 67
The Scatter Plot 67
The Time-Series Plot 68
2.6
Organizing a Mix of Variables 70
Drill-down 71
2.7
Visualizing a Mix of Variables 72
Colored Scatter Plot 72
Bubble Charts 73
PivotChart (Excel) 73
Treemap (Excel, JMP) 73
Sparklines (Excel) 74
2.8 Filtering and Querying Data 75
Excel Slicers 75
2.9
Pitfalls in Organizing and Visualizing Variables 77
Obscuring Data 77

Creating False
3
Numerical Descriptive Measures 120
3.1
Measures of Central Tendency 121
The Mean 121
The Median 123
The Mode 124
The Geometric Mean 125
3.2
Measures of Variation and Shape 126
The Range 126
The Variance and the Standard Deviation 127
The Coefficient of Variation 130
Z
Scores 130
Shape: Skewness 132
Shape: Kurtosis 132
3.3
Exploring Numerical Variables 137
Quartiles 137
EXHIBIT: Rules for Calculating the Quartiles from a Set
of Ranked Values 137
The Interquartile Range 139
The Five-Number Summary 139
The Boxplot 141
3.4
Numerical Descriptive Measures for a Population 143
The Population Mean 144
The Population Variance and Standard Deviation 144
The Empirical Rule 145
Chebyshev's Theorem 146
3.5
The Covariance and the Coefficient of Correlation 148
The Covariance 148
The Coefficient of Correlation 148
3.6
Descriptive Statistics: Pitfalls and Ethical Issues 152
The greater the spread or dispersion of the data, the larger the range, variance, and standard
deviation.
The smaller the spread or dispersion of the data, the smaller the range, variance, and standard
deviation.
If the values are all the same (no variation in the data), the range, variance, and standard
deviation will all equal zero.
Measures of variation (the range, variance, and standard deviation) are never negative.
You constructed boxplots to visualize the distribution of the data.
You also learned how the coefficient of correlation describes the relationship between two
numerical variables.
Type of Analysis
Methods
Central tendency
Mean, median, mode
Variation and shape
Quartiles, range, interquartile range, variance, standard
deviation, coefficient of variation, Z scores, skewness,

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