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# MATH 1340 - Statistics

Credit Hours: 4.00

Prerequisites: MATH 1050 , MATH 1050X , MATH 1100 , or MATH 1100X  with grade C or better; or equivalent college course; or an acceptable score on a placement or prerequisite exam

(formerly MATH 1330)

MATH 1340 is for students in those fields where statistical investigations are necessary and includes description of sample data, probability, frequency distributions, sampling, confidence intervals, estimation, testing hypothesis, correlation, chi‑square distributions, and nonparametric tests.

Billable Contact Hours: 4

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OUTCOMES AND OBJECTIVES
Outcome 1: Upon completion of this course, students will be able to collect and organize data into a table and construct appropriate charts and plots to display the data.

Objectives: Students will:

1. Identify types of data.
2. Identify levels of measurement.
3. Identify sampling methods.
4. Create a frequency distribution.
5. Create a histogram.
6. Create a Pareto chart.
7. Create a stem-and-leaf.
8. Create a box-plot.

Outcome 2: Upon completion of this course, students will be able to define, interpret, and calculate measures of central tendency, dispersion, and position.

Objectives: Students will find and interpret:

1. The mean, median, mode, and midrange from a data set or frequency table.
2. The standard deviation, variance, and range from a data set or frequency table.
3. Quartiles, deciles, and percentiles.

Outcome 3: Upon completion of this course, students will be able to compute probabilities by applying the addition rule, multiplication rule, complement rule, and counting rules.

Objectives: Students will:

1. Find and interpret the probability of events that are mutually exclusive.
2. Find and interpret the probability of events that are not mutually exclusive.
3. Find and interpret the probability of events that are dependent.
4. Find and interpret the probability of events that are independent.
5. Find and interpret the probability of events using complements.
6. Apply counting methods.
7. Find and interpret the probability of events using counting methods.

Outcome 4: Upon completion of this course, students will be able to create, use, and interpret probability distributions, binomial probability distributions, normal probability distributions, student t distributions, and Chi-square distributions.

Objectives: Students will:

1. Create, use, and interpret a probability distribution.
2. Find and interpret the mean and standard deviation for a probability distribution.
3. Create, use, and interpret a binomial probability distribution.
4. Find and interpret the mean and standard deviation for a binomial probability distribution.
5. Create, use, and interpret a normal probability distribution.
6. Use and interpret the student t distribution.
7. Use and interpret the Chi-square distribution.

Outcome 5: Upon completion of this course, students will be able to create confidence intervals and test hypotheses about a mean or a proportion from a single sample or from two samples and arrive at a statistical decision and be able to estimate sample size.

Objectives: Students will:

1. Create and interpret a confidence interval about one population mean.
2. Create and interpret a confidence interval about one population proportion.
3. Test and interpret a claim about a population mean.
4. Students will be able to test and interpret a claim about a population proportion.
5. Test and interpret a claim about two population means or population proportions.
6. Determine sample size to estimate a population mean or a population proportion.

Outcome 6: Upon completion of this course, students will be able to explain what is meant by correlation and regression, and be able to compute the Pearson correlation coefficient for a sample and draw inferences about the population correlation coefficient.

Objectives: Students will:

1. Compute the Pearson correlation coefficient for a sample.
2. Test and interpret a claim about linear correlation.
3. Create and interpret the equation of the regression line.
4. Find the best predicted y-value for a given x-value.

Outcome 7: Upon completion of this course, students will be able to test a hypothesis about a multinomial experiment that can be expressed by a contingency or goodness-of-fit table and be able to explain the results.

Objectives: Students will:

1. Use and interpret the goodness-of-fit test from a multinomial experiment.
2. Use and interpret the test for independence from a contingency table.

Outcome 8: Upon completion of this course, students will be able to construct a control chart for individual values, means, variations, or proportions and be able to interpret control chart to determine whether or not a process is out of statistical control.

Objectives: Students will create and interpret a:

1. Runs chart.
2. X chart.
3. R chart.

Outcome 9: Upon completion of this course, students will be able to use nonparametric tests.

Objectives: Students will create and interpret the:

1. Runs test for randomness for n1 <= 20 and n2 <=20.
2. Sign test for n <=25.
3. Rank correlation test for n <= 30.

COMMON DEGREE OUTCOMES (CDO)
• Communication: The graduate can communicate effectively for the intended purpose and audience.
• Critical Thinking: The graduate can make informed decisions after analyzing information or evidence related to the issue.
• Global Literacy: The graduate can analyze human behavior or experiences through cultural, social, political, or economic perspectives.
• Information Literacy: The graduate can responsibly use information gathered from a variety of formats in order to complete a task.
• Quantitative Reasoning: The graduate can apply quantitative methods or evidence to solve problems or make judgments.
• Scientific Literacy: The graduate can produce or interpret scientific information presented in a variety of formats.

CDO marked YES apply to this course:
Critical Thinking: YES
Information Literacy: YES
Quantitative Reasoning: YES
Scientific Literacy: YES

COURSE CONTENT OUTLINE
1. Introduction to Statistics
1. Types of Data
2. Types of Sampling Methods
2. Describing and Interpreting Data
1. Frequency Tables
2. Graphs
3. Measures of Center *
4. Measures of Variation *
5. Measures of Position
6. Exploratory Data Analysis *
3. Probability
1. Basic Probability
3. Multiplication Rule
4. Counting
4. Probability Distributions
1. Random Variables
2. Binomial Probability Distribution
3. Mean and Standard Deviation for a Binomial Distribution
5. Normal Probability Distributions
1. Standard Normal Distribution
2. Nonstandard Normal Distribution
3. Central Limit Theorem
4. Normal Distribution as Approximation to Binomial Distribution
6. Confidence Intervals and Sample Size
1. Estimating a Population Mean: Large Samples *
2. Estimating a Population Mean: Small Samples *
3. Estimation a Population Proportion *
4. Determining sample size
7. Hypothesis Testing
1. Testing a Claim about One Mean: Large Samples *
2. Testing a Claim about One Mean: Small Samples *
3. Testing a Claim about One Proportion *
8. Inferences from Two Samples
1. Inferences about Two Means: Independent and Large Samples *
2. Inferences about Two Means: Matched Pairs *
3. Inferences about Two Proportions *
9. Correlation and Regression
1. Correlation *
2. Regression *
10. Multinomial Experiments and Contingency Tables
1. Goodness-Of-Fit
2. Contingency Tables
11. Statistical Process Control
1. Control Charts for Variation
2. Control Charts for Mean
3. Control Charts for Attributes
12. Nonparametric Tests
1. Sign Test
2. Rank Correlation
3. Runs Test for Randomness

* These topics will now have time available for the use of additional technology, such as Minitab, Excel, and Graphing calculators.

Primary Faculty
Donnelly, Christopher
Secondary Faculty
Wenson, James
Associate Dean
McMillen, Lisa
Dean
Pritchett, Marie

Official Course Syllabus - Macomb Community College, 14500 E 12 Mile Road, Warren, MI 48088