-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

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