Chapter 18 Data Analysis Overview Statistics for Managers using Microsoft Excel 6 th Global Edition 18 2 Learning Objectives In this chapter you learn The steps involved in choosing what statistical methods to use to conduct a data analysis ID: 264770
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Chapter 18Data Analysis Overview
Statistics for Managers using Microsoft Excel
6
th
Global EditionSlide2
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Learning Objectives
In this chapter, you learn:
The steps involved in choosing what statistical methods to use to conduct a data analysisSlide3
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Good Data Analysis Requires Choosing The Proper Technique(s)Choosing the proper technique(s) to use requires the consideration of:
The purpose of the analysis
The type of variable being analyzed
Numerical
Categorical
The assumptions about the variable you are willing to makeSlide4
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Questions To Ask When Analyzing Numerical Variables
Do you seek to:
Describe the characteristics of the variable (possibly broken into several groups)
Draw conclusions about the mean and standard deviation of the variable in a population
Determine whether the mean and standard deviation of the variable differs depending on the group
Determine which factors affect the value of the variable
Predict the value of the variable based on the value of other variables
Determine whether the values of the variable are stable over timeSlide5
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How to Describe the Characteristics of a Numerical Variable
Develop tables and charts and compute descriptive statistics to describe the variable’s characteristics:
Tables and charts
Stem-and-leaf display, percentage distribution, histogram, polygon, boxplot, normal probability plot
Statistics
Mean, median, mode, quartiles, range, interquartile range, standard deviation, variance, and coefficient of variationSlide6
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How to draw conclusions about the population mean or standard deviation
Confidence interval for the mean based on the t-distribution
Hypothesis test for the mean (t-test)Slide7
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How to determine whether the mean or standard deviation differs by group
Two independent groups studying central tendency
Normally distributed numerical variables
Pooled t-test if you can assume variances are equal
Separate-variance t-test if you cannot assume variances are equal
Both tests assume the variables are normally distributed and you can examine this assumption by developing boxplots and normal probability plots
To decide if the variances are equal you can conduct an F-test for the ratio of two variances
Numerical variables not normally distributed
Wilcoxon rank sum testSlide8
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How to determine whether the mean or standard deviation differs by group
Two groups of matched items or repeated measures studying central tendency
Paired differences normally distributed
Paired t-test
Two independent groups studying variability
Numerical variables normally distributed
F-test
continuedSlide9
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How to determine whether the mean or standard deviation differs by group
Three or more independent groups and studying central tendency
Numerical variables normally distributed
One Way Analysis of Variance
Numerical variables not normally distributed
Kruskal-Wallis Rank Test
continuedSlide10
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How to determine which factors affect the value of the variable
Two factors to be examined
Two-factor factorial designSlide11
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How to predict the value of a variable based on the value of other variables
One independent variable
Simple linear regression model
Two or more independent variables
Multiple regression model
Data taken over a period of time and you want to forecast future time periods
Moving averages
Exponential smoothing
Least-squares forecasting
Autoregressive modelingSlide12
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How to determine whether the values of a variable are stable over time
Studying a process and have collected data over time
Develop R and chartsSlide13
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Questions To Ask When Analyzing Categorical Variables
Do you seek to:
Describe the proportion of items of interest in each category (possibly broken into several groups)
Draw conclusions about the proportion of items of interest in a population
Determine whether the proportion of items of interest differs depending on the group
Predict the proportion of items of interest based on the value of other variables
Determine whether the proportion of items of interest is stable over timeSlide14
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How to describe the proportion of items of interest in each category
Summary tables
Charts
Bar chart
Pie chart
Pareto chart
Side-by-side bar chartsSlide15
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How to draw conclusions about the proportion of items of interest
Confidence interval for proportion of items of interest
Hypothesis test for the proportion of items of interest (Z-test)Slide16
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How to determine whether the proportion of items of interest differs depending on the group
Categorical variable has two categories
Two independent groups
Two proportion Z-test
for the difference between two proportions
Two groups of matched or repeated measurements
McNemar test
More than two independent groups
for the difference among several proportions
More than two categories and more than two groups
of independence
2
-
test
2
-
test
2
-
testSlide17
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How to determine whether the proportion of items of interest is stable over time
Studying a process and data is taken over time
Collected items of interest over time
p-chartSlide18
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Data Analysis TreeNumerical & Categorical Variables
Numerical
Variables
Categorical
Variables
Possible Questions
How to describe the characteristics of the variable (possibly broken into several groups)?
How to draw conclusions about the mean and standard deviation of the variable in the population?
How to determine whether the mean and standard deviation of the variable differs depending on the group?
How to determine which factors affect the value of the variable?
How to predict the value of the variable based on the value of other variables?
How to determine whether the values of the variable are stable over time?
How to describe the proportion of items of interest in each category (possibly broken into several groups)?
How to draw conclusions about the proportion of items of interest in a population?
How to determine whether the proportion of items of interest differs depending on the group?
How to predict the proportion of items of interest based on the value of other variables?
How to determine whether the proportion of items of interest is stable over time?Slide19
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Data Analysis TreeNumerical Variables
How to describe the characteristics of the variable (possibly broken into several groups)?
How to draw conclusions about the mean and standard deviation of the variable in the population?
How to determine whether the mean and standard deviation of the variable differs depending on the group?
continued
Create Tables & Charts
Calculate Statistics
Mean
Mean
Variance
Stem-and-leaf display, percentage distribution,
histogram, polygon, boxplot, normal probability plot
Mean, median, mode, quartiles, range,
interquartile range, standard deviation, variance,
coefficient of variation
Confidence interval for mean (t or z)
Hypothesis test for mean (t or z)
Pooled t test
(both variables must be normal, variances equal)
Separate variance t test
(
both variables must be normal)
Wilcoxon rank sum test
(
variables do not have to be normal)
F-test
(
both variables must be normal)
Paired t test
(differences must be normal)
One Way Anova
(variable must be normal)
Kruskal-Wallis Rank Test
(variable doesn’t need to be normal)
2 independent
groups
2 matched
groups
>2 independent
groupsSlide20
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Data Analysis TreeNumerical Variables
continued
How to determine which factors affect the value of the variable?
How to predict the value of the variable based on the value of other variables?
How to determine whether the values of the variable are stable over time?
Two factors
to be examined
One independent
variable
Two or more
Independent variables
Data taken over time to
forecast the future
Studied a process and
taken data over time
Two factor factorial design
Simple linear regression
Multiple regression model
Moving averages
Exponential smoothing
Least squares forecasting
Autoregressive modeling
Develop and R chartsSlide21
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Data Analysis TreeCategorical Variables
continued
How to describe the proportion of items of interest in each category (possibly broken into several groups)
How to draw conclusions about the proportion of items of interest in a population
How to determine whether the proportion of items of interest differs depending on the group
Summary tables
Bar charts
Pie charts
Pareto charts
Side-by-side charts
Confidence interval for the proportion of items of interest
Hypothesis test for the proportion of items of interest
Two proportion Z test
test for the difference between two proportions
McNemar test
test for the difference among several proportions
test of independence
Two categories & two independent groups
Two categories & two matched groups
Two categories & more than two independent groups
More than two categories & more than two groupsSlide22
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How to determine whether the proportion of items of interest is stable over time
Data Analysis Tree
Categorical Variables
continued
p-chart
Studying a process and collected items of interest over time Slide23
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Chapter SummaryDiscussed how to choose the appropriate technique(s) for data analysis for both numerical and categorical variables
Discussed potential questions and the associated appropriate techniques for numerical variables
Discussed potential questions and the associated appropriate techniques for categorical variablesSlide24
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