**Course** Description: | Application of statistics to business. Descriptive statistics,
sampling procedures, random variables, sampling distributions of
the means and proportions, estimation and inference, simple
linear regression, an introduction to multiple regression, and
categorical data models are emphasized. A current statistical
software package for microcomputers is utilized to analyze
business data. For non-Terry College of Business students. |

**Course Objectives:** | -Learn to create and interpret graphical summaries of data
sets; calculate and interpret numerical summaries of data sets;
calculate and interpret measures of relative standing for
individual observations from a data set
-Learn the basics of probability, including intersections,
unions, and conditional probability; be able to apply these
concepts to real-world problems
-Use basic counting rules (e.g., permutations and combinations)
both in calculating probabilities and in applied problems;
understand the concept of statistical independence
-Be introduced to sampling procedures, the notion of random
sampling, and the importance of representative samples
-Understand the concept of random variables and probability
distributions; work applied problems with binomial
distributions and normal distributions
-Understand the concept of sampling distribution, specifically
the sampling distribution of the sample mean and the sampling
distribution of the sample proportion (normal approximation of
binomial distributions)
-Understand the concept of and importance of the Central Limit
Theorem
-Calculate and interpret confidence intervals for point
estimates, including large-sample and small-sample confidence
intervals for a population mean and large-sample confidence
intervals for a population proportion
-Learn the basics of hypothesis testing, including large-sample
and small-sample hypothesis tests of a population mean, large
sample hypothesis tests of a population proportion, chi-square
tests of the equivalence of multiple proportions with
application to goodness of fit, and chi-square hypothesis tests
of independence; learn how and when to perform these procedures
and how to interpret the results
-Learn to calculate the parameter estimates for simple linear
regression models
-Learn to interpret the results of simple and multiple
regression models, including analyzing the significance of the
model and the individual coefficients; use the model for
estimation and prediction
-Be introduced to residual analysis and pitfalls of regression |

**Topical Outline:** | I. Descriptive Statistics
A. Creating and Interpreting Graphical Summaries
1. Qualitative Data
a. Pie Graphs
b. Bar Charts
2. Quantitative Data
a. Dot Plots
b. Stem and Leaf Diagrams
c. Histograms
d. Box Plots
B. Calculating and Interpreting Numerical Summaries
1. Measures of Central Tendency
a. Mean / Expected Value
b. Median
c. Mode
2. Measures of Variability
a. Range
b. Variance
c. Standard Deviation (Chebychev's and Empirical Rule)
d. Interquartile Range
e. Interpretation as Measures of Risk
3. Measures of Relative Standing
a. Percentile
b. Quartile
c. z-score (Introduction to logic of statistical
inference)
4. Measures of Correlation
a. Covariance (and its relationship to portfolio
theory)
b. Linear Correlation Coefficient
C. Identifying and Dealing with Outliers
II. Probability
A. Probability Basics
1. Sample Points, Sample Space, and Event
2. Complements
3. Classical Probability Rule (f/n Rule)
B. Basic Counting Rules
1. Multiplication Rule (Fundamental Counting Rule)
2. Permutations
3. Combinations
C. Intersections, Unions, and Conditional Probability
D. Statistical Independence
E. Random Sampling
III. Random Variables and Probability Distributions
A. Discrete Random Variables
1. Basics
a. Mean / Expected Value
b. Standard Deviation
2. Binomial Random Variables and Probability
Distributions
a. Basics
b. Applications
B. Continuous Random Variables
1. Basics
2. Uniform Distribution
3. Normal Distribution
a. Basics
b. Applied Problems
C. Sampling Distributions
1. Sampling Distribution of the Sample Mean
2. Central Limit Theorem
IV. Confidence Intervals
A. Confidence Intervals for a Population Mean
1. Large-Samples
a. Procedure
b. Applications
c. Determining Sample Size
2. Small Samples
a. Procedure
b. Applications
B. Large-Sample Confidence Intervals for a Population
Proportion
1. Sampling Distribution of Sample Proportion / Normal
Approximation of Binomial Distribution
2. Procedure
3. Applications
4. Determining Sample Size
V. Hypothesis Testing
A. Elements of Hypothesis Testing
1. Null and Alternative Hypotheses
2. Level of Significance
3. Decision Rules
4. p-values
5. Type I and Type II Errors
B. Application and Interpretation
1. Hypothesis Test of a Population Mean
a. Large Sample
b. Small Sample
2. Large-Sample Hypothesis Test of a Population
Proportion
3. Chi-square Test of Multiple Proportions
a. Multinomial Experiments / Probability
Distributions
b. Application to Tests of Goodness of Fit
4. Contingency Table Analysis / Chi-square Tests of
Independence
VI. Simple Linear Regression
A. Fitting the Model Using Ordinary Least Squares
1. Calculating Parameter Estimates
2. Interpreting Parameter Estimates
B. Assumptions of OLS
C. Variance of the Disturbance Term (Root Mean Square
Error)
1. Estimation
2. Interpretation
D. Assessing the Model
1. Usefulness of the Model/Significance of the Slope
Parameter
a. Statistical Significance
b. Practical Significance
2. Linear Correlation Coefficient
3. Coefficient of Determination (R2)
E. Use of the Results
1. Estimation
a. Interpreting Values of Parameter Estimates
b. Statistical and Practical Significance of Parameter
Estimates
2. Prediction
a. Calculating and Interpreting Point Estimates
b. Calculating and Interpreting Confidence Interval
for the Mean Value of the Dependent Variable at a Given Value
of the Independent Variable
c. Calculating and Interpreting Prediction Interval
for an Individual Value of the Dependent Variable at a Given
Value of the Independent Variable
VII. Multiple Regression
A. Model Assumptions (for OLS Estimation) / Root MSE
B. Usefulness of Model
1. Global F-test / ANOVA
2. R2
C. Use of the Results
1. Estimation
a. Interpreting Values of Parameter Estimates
b. Statistical and Practical Significance of
Parameter Estimates
2. Prediction
a. Calculating and Interpreting Point Estimates
b. Interpreting Confidence Interval for the Mean
Value of the Dependent Variable at a Given Value of the
Independent Variable
c. Interpreting Prediction Interval for an Individual
Value of the Dependent Variable at a Given Value of the
Independent Variable
D. Residual Analysis
E. Regression Pitfalls
1. Estimability
2. Multicollinearity
3. Extrapolation |