STA 13

Elementary Statistics

Fall, 2009


  • Instructor : Debashis Paul


  • Meeting times


  • Syllabus
    Teaching Assistants
    TA
    Office
    Email
    Office hours
    Jung Won Hyun
    		MSB 1117
    jwhyun (at) ucdavis (dot) edu
    W 4:10-5:00 PM
    Wei-Shan Hsieh
    		MSB 1117
    wshsieh (at) ucdavis (dot) edu
    R 9:00-10:00 AM
    Lu Wang
    		MSB 1117
    luuwang (at) ucdavis (dot) edu
    F 11:00 AM -12:00 Noon
    Matt Yang
    		MSB 1117
    matyang (at) ucdavis (dot) edu
    MW 9:00-9:50 AM
    Xiaoke Zhang
    		MSB 1117
    xkzhang (at) ucdavis (dot) edu
    WF 4:10-5:00 PM
    Siyuan Zhou
    		MSB 1117
    ucdzhou (at) ucdvais (at) edu
    W 12:00-2:00 PM

  • Final Exam : December 9, from 8:00 to 10:00 AM in the lecture hall.

    Text

    A First Course in Statistics (10 Edition with MyStatLab) by J. T. McClave and T. Sincich


  • The package (which includes a text book AND an access kit) can be bought in the bookstore.
  • In order to register the course on line at www.coursecompass.com , you need the access kit AND the course ID (paul95797).
  • For detailed information about online registration, click here . This online system is used for homework assignments.


  • Topics


    Discussion Sessions
    Time
    Classroom
    TA
    T 8:00-8:50 AM
    Wellman 1
    Siyuan Zhou
    T 9:00-9:50 AM
    Olson 205
    Wei-Shan Hsieh
    T 3:10-4:00 PM
    Olson 147
    Wei-Shan Hsieh
    T 4:10-5:00 PM
    Huthison 115
    Jung Won Hyun
    T 5:10-6:00 PM
    Hart 1130
    Xiaoke Zhang
    T 6:10-7:00 PM
    Hart 1130
    Matt Yang
    Course Material
    Date
    Lecture Notes
    Material Covered
    Homework
    Due date
    September 25
    		Lecture 1 Course outline and organization
    September 28
    		Lecture 2 Data, Variables, Graphical summary of qualitative variables
    September 30
    		Lecture 3 Graphical summary of quantitative variables : dot plot, stem-and-leaf plot, histogram
    October 2
    		Lecture 4 Numerical summary of data; Measures of center - mean, median, mode Homework 1 Friday, October 9
    October 5
    		Lecture 5 Measures of dispersion; Range, Standard Deviation
    October 7
    		Lecture 6 Chebyshev's theorem; Empirical rule for quantifying spread
    October 9
    		Variance computation Percentiles, Quantiles, Box plot Homework 2 Friday, October 16
    October 12
    		Lecture 7 Graphical and numerical measures for describing bivariate data; Correlation coefficient
    October 14
    		Note on correlation and regression Linear regression - properies

    Syllabus for Midterm 1 : The materials covered up to and including the lecture on October 14

    October 16
    		Lecture 8 Introduction to probability theory - sample sapce, events Homework 3 Friday, October 23
    Sample Midterm 1 Solution
    October 19
    		Lecture 9 Calculus of probability; Multiplication Rule; Basics of combinatorics
    October 21
    		Midterm 1 Solution
    October 23
    		Lecture 10 Independence; Conditional probability Homework 4 Friday, October 30
    October 26
    		Multiplication rule for conditional probability
    October 28
    		Lecture 11 Random variables; Probability Distributions; Expectation
    October 30
    		Lecture 12 Binomial random variable Homework 5 Friday, November 6
    November 2
    		Lecture 13 Continuous probability distributions; Normal distribution
    November 4
    		Lecture 14 Probability computations using Normal distribution; Assessing the appropriateness of normality assumption
    November 6
    		Lecture 15 Assessing the appropriateness of normality assumption; Sampling distributions Homework 6 Friday, November 16
    November 9
    		Lecture 16 Sampling Distribution of Sample Mean; Central Limit Theorem
    November 13
    		Application of Central Limit Theorem; Probability computation for sampling distribution of sample mean and sample proportion
    Sample Midterm 2 Solution
    November 16
    		Lecture 17 Introduction to estimation of parameters
    Midterm 2 Solution
    November 20
    		Margin of Error of point estimates Homework 7 Tuesday, December 1
    November 23
    		Lecture 18 Interval estimation; Large sample confidence intervals for population mean and population proportion
    Debashis Paul