For REQUIRED SYLLABUS information that is to be included on all syllabi starting Spring 2018, 201830 go to https://internal.cccs.edu/wp-content/uploads/documents/GT-MA1-Required-Syllabus-Info.docx.

 REQUIRED COURSE LEARNING OUTCOMES:

 1.  Communicate the language of statistics using the appropriate terminology.

 2.  Analyze numerical summaries and graphical displays of sample data.

 3.  Analyze bivariate data by applying the concepts of correlation and regression.

 4.  Apply the basic rules of probability.

 5.  Utilize the appropriate probability distribution.

 6.  Make inferences about one or more populations using sample data.

 7.  Utilize technology to further statistical understanding and reasoning.

 RECOMMENDED TOPICAL OUTLINE:

 I.    The language and appropriate terminology of statistics

         A.  Introduction to experimental vs observational studies

         B.  Introduction to qualitative vs quantitative studies

         C.  Introduction to the research process

         D.  Introduction to sources of bias

         E.  Population vs samples

         F.  Parameters vs statistics

         G.  Types of data (discrete/continuous, quantitative/qualitative)

         H.  Types of variables (lurking/confounding/explanatory/response)

         I.  Sampling techniques (random/systematic/stratified/cluster/convenience)

         J.  Relevance of statistics to scientific and other real-world problems

 II.   Numerical summaries and graphical displays of sample data

         A.  Frequency distribution tables including construction

         B.  Graphical displays of qualitative and quantitative data including construction

         C.  Distribution shapes (modality and skew)

         D.  Measures of central tendency including calculations with or without technology

         E.  Measures of variation including calculations with or without technology

         F.  Measures of relative position including calculations with or without technology

 III.  Bivariate data by applying the concepts of correlation and regression

         A.  Scatter plots including construction

         B.  Correlation including calculations with or without technology

         C.  Linear regression

         D.  Analysis of residuals

 IV.   Fundamentals of probability

         A.  Law of Large Numbers

         B.  Addition and multiplication rules

         C.  Introduction to permutations and combinations

         D.  Complementary events

         E.  Conditional probability

 V.    Probability distribution

         A.  Introduction to discrete probability distributions

         B.  Binomial distributions

         C.  Normal distributions

 VI.   Inferences about one or more populations using sample data

         A.  The Central Limit Theorem

         B.  Sample size for mean and proportion

         C.  Confidence intervals for one and two populations

         D.  Hypothesis tests for one and two populations

         E.  Chi-square and goodness of fit tests

         F.  Introduction to Analysis of variance (ANOVA)

 VII.  Technology to further statistical understanding and reasoning