Chapter Goals
After completing this chapter, you should be
able to:
Construct a payoff table and an opportunity-loss table
Define and apply the expected value criterion for decision
making
Compute the value of perfect information
.
Describe the decision e
Chapter Goals
After completing this chapter, you should be
able to:
Find probabilities using a normal distribution table
Apply the normal distribution to business problems
.
Convert values from any normal distribution to a
standardized z-score
Recognize w
Chapter Goals
After completing this chapter, you should be able to:
Apply the binomial distribution to applied problems
Compute probabilities for the Poisson and hypergeometric
distributions
.
Calculate and interpret the expected value of a probability
di
Chapter Goals
After completing this chapter, you should be
able to:
Explain three approaches to assessing
probabilities
Apply common rules of probability, including the
Addition Rule and the Multiplication Rule
Use Bayes Theorem for conditional probabi
Chapter Goals
After completing this chapter, you should be able to:
Compute the range, variance, and standard deviation and
know what these values mean
Construct and interpret a box and whisker graph
Compute and explain the coefficient of variation and
z
Chapter Goals
After completing this chapter, you should be
able to:
Construct and interpret a histogram
Create and interpret bar charts, pie charts, and
stem-and-leaf diagrams
.
Construct a frequency distribution both manually
and with a computer
Present
Chapter Goals
After completing this chapter, you should be
able to:
Describe key data collection methods
Know key definitions:
Population vs. Sample
Qualitative vs. Qualitative data
Primary vs. Secondary data types
Time Series vs. Cross-Sectional data
Exp
Learning Objectives
Learn about decision making under certainty, under
uncertainty, and under risk.
Learn several strategies for decision-making under
uncertainty, including expected payoff, expected opportunity
loss, maximin, maximax, and minimax regre
Learning Objectives
Understand the concepts of quality, quality
control, and total quality management.
Understand the importance of statistical quality
control in total quality management.
Learn about process analysis and some process
analysis tools, i
Learning Objectives
Recognize the advantages and disadvantages of
nonparametric statistics.
Understand how to use the runs test to test for
randomness.
Know when and how to use the Mann-Whitney U
test, the Wilcoxon matched-pairs signed rank test,
the K
Chapter Goals
After completing this chapter, you should be
able to:
Define the concept of sampling error
Determine the mean and standard deviation for the
_
sampling distribution of the sample mean, x
Determine the mean and standard deviation for_the
samp
Chapter Goals
After completing this chapter, you should be able
to:
Construct and interpret a confidence interval estimate for a
single population mean using both the z and t distributions
Determine the required sample size to estimate a single
population
Chapter Goals
After completing this chapter, you should be
able to:
Formulate a decision rule for testing a hypothesis
Know how to use the test statistic, critical value, and
p-value approaches to test the null hypothesis
Know what Type I and Type II erro
Chapter Goals
After completing this chapter, you should be
able to:
Use the seven basic tools of quality
Construct and interpret x-charts and R-charts
Construct and interpret p-charts
Construct and interpret c-charts
.
Chap 18-1
Chapter Overview
Quali
Chapter Goals
After completing this chapter, you should be
able to:
Recognize when and how to use the Wilcoxon
signed rank test for a population median
Recognize the situations for which the Wilcoxon
signed rank test applies and be able to use it for
de
Chapter Goals
After completing this chapter, you should be
able to:
Identify the components present in a time series
Compute and interpret basic index numbers
Apply trend-based forecasting models, including linear
trend, nonlinear trend, and seasonally ad
Chapter Goals
After completing this chapter, you should be
able to:
apply multiple regression analysis to business
decision-making situations
analyze and interpret the computer output for a
multiple regression model
.
explain model building using multiple
Chapter Goals
After completing this chapter, you should be
able to:
Determine whether the correlation is significant
Calculate and interpret the simple linear regression
equation for a set of data
Understand the assumptions behind regression
analysis
.
Ca
Chapter Goals
After completing this chapter, you should be
able to:
.
Use the chi-square goodness-of-fit test to
determine whether data fits a specified distribution
Set up a contingency analysis table and perform a
chi-square test of independence
Chap 13
Chapter Goals
After completing this chapter, you should be able
to:
Understand different analysis of variance designs
Perform a single-factor hypothesis test and interpret results
Conduct and interpret post-analysis of variance pairwise
comparisons proced
Chapter Goals
After completing this chapter, you should be
able to:
Find critical chi-square distribution values from the chisquare table
Formulate and complete hypothesis tests for the
difference between two population variances
.
Formulate and complete
Chapter Goals
After completing this chapter, you should be
able to:
Test hypotheses or form interval estimates for
two independent population means
.
Standard deviations known
Standard deviations unknown
two means from paired samples
the difference betwee
Learning Objectives
Gain a general understanding of time series forecasting
techniques.
Understand the four possible components of time-series data.
Understand stationary forecasting techniques.
Understand how to use regression models for trend analys
Learning Objectives
Analyze and interpret nonlinear variables in multiple
regression analysis.
Understand the role of qualitative variables and how to use
them in multiple regression analysis.
Learn how to build and evaluate multiple regression models.
Learning Objectives
Develop a multiple regression model.
Understand and apply significance tests of the regression
model and its coefficients.
Compute and interpret residuals, the standard error of the
estimate, and the coefficient of determination.
I
Learning Objectives
Define statistics
Become aware of a wide range of
applications of statistics in business
Differentiate between descriptive and
inferential statistics
Classify numbers by level of data and
understand why doing so is important
15-
St
Research Methods: Fall 2016
Module 15 Assignment
Due Friday, 9/30 to Moodle
Use the Excel file 240 Stats 1 Workbook to complete this assignment; save as another document. Type
your written answers into the appropriate sheet. You will need to submit this a
Section 1.3
Experiments and
Observational Studies
Statistics: Unlocking the Power of Data
Lock5
Outline
Association versus Causation
Confounding Variables
Observational Studies vs Experiments
Randomized Experiments
Statistics: Unlocking the Power of Data
Section 1.2
Sampling from a
Population
Statistics: Unlocking the Power of Data
Lock5
Outline
Sample versus Population
Statistical Inference
Sampling Bias
Simple Random Sample
Other Sources of Bias
Statistics: Unlocking the Power of Data
Lock5
Sample v
Section 1.1
The Structure of Data
Statistics: Unlocking the Power of Data
Lock5
Outline
Data
Cases and variables
Categorical and quantitative variables
Explanatory and response variables
Using data to answer a question
Statistics: Unlocking the Power of D