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
Search for Sections Transfer Possibilities Michigan Transfer Network (MiTransfer)  Utilize this website to easily search how your credits transfer to colleges and universities. 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:
 Identify types of data.
 Identify levels of measurement.
 Identify sampling methods.
 Create a frequency distribution.
 Create a histogram.
 Create a Pareto chart.
 Create a stemandleaf.
 Create a boxplot.
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:
 The mean, median, mode, and midrange from a data set or frequency table.
 The standard deviation, variance, and range from a data set or frequency table.
 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:
 Find and interpret the probability of events that are mutually exclusive.
 Find and interpret the probability of events that are not mutually exclusive.
 Find and interpret the probability of events that are dependent.
 Find and interpret the probability of events that are independent.
 Find and interpret the probability of events using complements.
 Apply counting methods.
 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 Chisquare distributions.
Objectives: Students will:
 Create, use, and interpret a probability distribution.
 Find and interpret the mean and standard deviation for a probability distribution.
 Create, use, and interpret a binomial probability distribution.
 Find and interpret the mean and standard deviation for a binomial probability distribution.
 Create, use, and interpret a normal probability distribution.
 Use and interpret the student t distribution.
 Use and interpret the Chisquare 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:
 Create and interpret a confidence interval about one population mean.
 Create and interpret a confidence interval about one population proportion.
 Test and interpret a claim about a population mean.
 Students will be able to test and interpret a claim about a population proportion.
 Test and interpret a claim about two population means or population proportions.
 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:
 Compute the Pearson correlation coefficient for a sample.
 Test and interpret a claim about linear correlation.
 Create and interpret the equation of the regression line.
 Find the best predicted yvalue for a given xvalue.
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 goodnessoffit table and be able to explain the results.
Objectives: Students will:
 Use and interpret the goodnessoffit test from a multinomial experiment.
 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:
 Runs chart.
 X chart.
 R chart.
Outcome 9: Upon completion of this course, students will be able to use nonparametric tests.
Objectives: Students will create and interpret the:
 Runs test for randomness for n1 <= 20 and n2 <=20.
 Sign test for n <=25.
 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
 Introduction to Statistics
 Types of Data
 Types of Sampling Methods
 Describing and Interpreting Data
 Frequency Tables
 Graphs
 Measures of Center *
 Measures of Variation *
 Measures of Position
 Exploratory Data Analysis *
 Probability
 Basic Probability
 Addition Rule
 Multiplication Rule
 Counting
 Probability Distributions
 Random Variables
 Binomial Probability Distribution
 Mean and Standard Deviation for a Binomial Distribution
 Normal Probability Distributions
 Standard Normal Distribution
 Nonstandard Normal Distribution
 Central Limit Theorem
 Normal Distribution as Approximation to Binomial Distribution
 Confidence Intervals and Sample Size
 Estimating a Population Mean: Large Samples *
 Estimating a Population Mean: Small Samples *
 Estimation a Population Proportion *
 Determining sample size
 Hypothesis Testing
 Testing a Claim about One Mean: Large Samples *
 Testing a Claim about One Mean: Small Samples *
 Testing a Claim about One Proportion *
 Inferences from Two Samples
 Inferences about Two Means: Independent and Large Samples *
 Inferences about Two Means: Matched Pairs *
 Inferences about Two Proportions *
 Correlation and Regression
 Correlation *
 Regression *
 Multinomial Experiments and Contingency Tables
 GoodnessOfFit
 Contingency Tables
 Statistical Process Control
 Control Charts for Variation
 Control Charts for Mean
 Control Charts for Attributes
 Nonparametric Tests
 Sign Test
 Rank Correlation
 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
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