Department Homepage  Course Listings Degree Options Department Information Home Department Listings eSchedule Catalog Home Home
 
Master Course Outline
MATH 201
Introduction to Statistics


Credits: 5
Clock Hours per Quarter: 50

AA Discipline: [Quantitative] [Natural Sciences]

Lecture Hours:50


Description
Study of both descriptive and inferential statistics. Topics include data presentation, and analysis, measures of central tendency and dispersion, sampling distributions, parameter estimation hypothesis testing, and linear regression. Prerequisite: Grade of C or higher in MATH 078E, appropriate score on placement test, or permission of the Mathematics Department.

Intended Learning Outcomes
  • Describe the difference between descriptive statistics and inferential statistics.
  • Describe the difference between population and sample.
  • Describe the difference between quantative and categorical data.
  • Describe the difference between discrete and continuous numerical data.
  • Construct a stem-and-leaf plot of numerical data.
  • Construct frequency distributions for a data set.
  • Determine appropriate class limits to be used for the construction of a frequency distribution for data set and construct frequency distributions for continuous data.
  • Construct histograpms for categorical data, discrete data, and continuous data.
  • Read and interpret histograms for centers, spread, and skewness of data sets.
  • Compute the mean for a data set.
  • Describe the difference between a sample mean and population mean.
  • Compute the median for a data set.
  • Compute the variance and standard deviation for a data set using an appropriate technological tool.
  • Determine quartiles for a data set.
  • Compute standard scores z-scores for observations.
  • Determine percentiles for a data set.
  • Construct a boxplot for a data set.
  • Analyze relationships in bivariate data using a scatterplot.
  • Compute and interpret the correlations coefficient for bivariate data.
  • Describe why strong association does not imply causation.
  • Compute the slop and y-intercept for the sample regression line.
  • Plot the sample regression line together with the data set.
  • Use the sample regression line for prediction.
  • Compute and interpret the coefficient of determination.
  • Understand conceptually the meaning of probability and perform elementary probability computations.
  • Know the definition of a random variable.
  • Describe the difference between a discrete and continuous random variable.
  • Calculate probabilities for random variables taking on certain values.
  • Given a scenario, determine a probability distribution for a random variable.
  • Calculate the mean and standard deviation for a discrete random variable.
  • Describe the properties of a binomial experiment.
  • Define the binomial random variable.
  • Use the binomial probability distribution to determine probabilities in binomial settings.
  • Calculate the mean and standard deviation of a binomial random variable.
  • Describe the characteristics of a probability distribution for a continuous random variable.
  • Use a probability distribution to calculate probabilities for a random variable.
  • Describe the characteristics of a normal distribution.
  • Determine z critical values.
  • Use a table to calculate probabilities for a normally distributed random variable.
  • Know the definition of a sample statistic versus a population parameter.
  • Describe the sampling distribution of a statistic.
  • Describe the characteristics of a random sample.
  • Describe the sampling distribution of a sample mean.
  • Understand and apply the Central Limit Theorem at it applies to sample means and probabilities.
  • Use the sampling distribution of a sample mean to calculate probabilities.
  • Describe the sampling distribution of a sample proportion.
  • Use the sampling distribution of a sample propartion to calculate probabilities.
  • Understand a point estimate for a population parameter.
  • Describe the difference between confidence level and margin of error in a confidence interval.
  • Understand how the randomness of the sample affects the location of the confidence interval.
  • Caculate confidence intervals for the population proportion from large sample data.
  • Determine appropriate sample sizes to meet specified margin of errors.
  • Calculate confidence intervals for a population mean from large sample data.
  • Describe the differences between a t-distribution and the standard normal distribution.
  • Use a table to determine t critical values.
  • Calculate confidence intervals for the population mean from small sample data.
  • Describe the difference between a null hypothesis and alternative hypothesis.
  • Know the standard form for the statements of the null and alternative hypothesis.
  • Describe the difference between a Type I and Type II error.
  • Understand the level of significance of a test is the probability of committing a Type I error.
  • Memorize and follow specific steps in performing a hypothesis-testing analysis.
  • Perform a large-sample hypothesis test for a population mean.
  • Use p-values to justify decisions in a hypothesis testing situation.
  • Perform a samll-sample hypothesis test for a population mean where the populations are normally distributed.
  • Perform a large-sample hypothesis test for a population proportion.
  • Perform a hypothesis test for the difference between two population means.
  • Compute a confidence interval for the difference between two population means.
  • Peform a hypothesis test for the difference between two population proportions.
  • Compute a confidence interval for the difference between two population proportions.
  • Perform a hypothesis test for the difference between two population proportions.
  • Compute a confidence interval for the difference between two population proportions.
  • Perform a Chi-Squared test for independence.



  • Syllabi Listing See ALL Quarters
    Course
    Year Quarter
    Item
    Instructor  
    MATH 201
    Winter 2015
    6847
    Michael Shively
    MATH 201
    Winter 2015
    6848
    Michael Shively
    MATH 201
    Fall 2014
    1519
    Eric Schulz
    MATH 201
    Fall 2014
    1486
    Megan Schoessler
    MATH 201
    Fall 2014
    1487
    Shana Smith


    Two Year Projected Schedule

    Year One* Year Two**
    Fall
    Winter
    Spring
    Summer
    Mini 
    Fall
    Winter
    Spring
    Summer
    Mini
    X
    X
    X
    X
     
    X
    X
    X
    X
     

    *If fall quarter starts on an odd year (2003, 2005, etc.), it's Year One.
    **
    If fall quarter starts on an even year (2002, 2004, etc.), it's Year Two.