Course Learning Outcomes
The student will:
Explain the use of data collection and statistics as tools to reach reasonable conclusions.
Recognize, examine and interpret the basic principles of describing and presenting data.
Compute and interpret empirical and theoretical probabilities using the rules of probabilities and combinatorics.
Explain the role of probability in statistics.
Apply the Central Limit Theorem to the sampling process.
Examine, analyze and compare various sampling distributions for both discrete and continuous random variables.
Describe and compute confidence intervals.
Solve linear regression and correlation problems.
Perform hypothesis testing using statistical methods.
Contact Hour Information
Credit Hours: 3
Lecture Hours: 3
Lab Hours: 0
External Hours: 0
Total Contact Hours: 48
MATH 1314 OR placement by testing;
ENGL 0305 or ENGL 0365 OR higher level course (ENGL 1301), OR placement by testing
Chapter 1. Data Collection
1.1 Introduction to the Practice of Statistics
1.2 Observational Studies versus Designed Experiments
1.3 Simple Random Sampling
1.4 Other Effective Sampling Methods
1.5 Bias in Sampling
1.6 The Design of Experiments
Chapter 2. Descriptive Statistics
2.1 Organizing Qualitative Data
2.2 Organizing Quantitative Data: The Popular Displays
2.3 Graphical Misrepresentation of Data
Chapter 3. Numerically Summarizing Data
3.1 Measures of Central Tendency
3.2 Measures of Dispersion
3.4 Measures of Position and Outliers
3.5 The Five Number Summary and Boxplots
Chapter 4. Describing the Relation Between Two Variables
4.1 Scatter Diagrams and Correlation
4.2 Least Squares Regression
Chapter 5. Probability
5.1 Probability Rules
5.2 The Addition Rule and Complements
5.3 Independence and the Multiplication Rule
Chapter 6. Discrete Probability Distributions
6.1 Discrete Random Variables
6.2 The Binomial Probability Distribution
Chapter 7. The Normal Probability Distribution
7.1 Properties of the Normal Distribution
7.2 The Standard Normal Distribution
7.3 Applications of the Normal Distribution
7.4 Assessing Normality
Chapter 8. Sampling Distributions
8.1 Distribution of the Sample Mean
8.2 Distribution of the Sample Proportion
Chapter 9. Estimating the Value of a Parameter Using Confidence Intervals
9.1 The Logic of Constructing Confidence Intervals for a Population Mean when the Population Standard Deviation is Known
9.2 Confidence Intervals for a Population Mean in Practice When the Population Standard Deviation is Unknown
9.3 Confidence Intervals for a Population Proportion
9.4 Putting it All Together: Which Procedure Do I Use?
Chapter 10. Hypothesis Tests Regarding a Parameter
10.1 The Language of Hypothesis Testing
10.2 Hypothesis Tests for a Population Mean - Population Standard Deviation Known
10.3 Hypothesis Tests for a Population Mean - Population Standard Deviation Unknown
10.4 Testing Claims About a Population Proportion
10.5 Putting it All Together: Which Method Do I Use?
Chapter 11. Inference on Two Samples
(Cover a Selection of Topics from this Chapter as Time Permits)
11.1 Inference About Two Means: Dependent Samples
11.2 Inference About Two Means: Independent Samples
11.3 Inference About Two Population Proportions