# Course Syllabus STA301 Statistics for Economics and Business (6 ECTS credits)

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1 Course Syllabus STA301 Statistics for Economics and Business (6 ECTS credits) Instructor: Luc Hens Telephone: Web site: Course description Statistics for Business and Economics first reviews the basic concepts of statistical inference: sample variability, estimation with confidence intervals, and tests of statistical significance. The course then extends inference by looking into: (i) small-sample tests for averages (t-test); (ii) hypothesis tests comparing two sample averages; and (iii) Chi-square tests. The course finally introduces the student to simple regression (fitting a line to a scatter plot) and multiple regression (the generalization of the regression technique to more than one explanatory variable). Students learn how to use a statistical calculator and statistical software to do their own quantitative research. Emphasis on applying methods and interpreting results throughout. STA301 is equivalent to STA201 Intermediate Statistics. Course prerequisites STA101 Quantitative Methods / Introduction to Statistics is a prerequisite for STA301 Statistics for Business and Economics. Course objectives This course aims at providing students with an understanding of inferential statistics (making valid generalizations from sample data), including multiple regression. At the end of the course, students should be able to: critically analyze data sets and apply the tools of statistics to data in order to improve decision making; use statistical software and a scientific calculator to do statistical computations (enter data, generate descriptive statistics and graphs, estimate population parameters, perform hypothesis tests) 1

2 communicate the results of statistical work, and more specifically write up the results of statistical analysis in a report consisting of a non-technical abstract aimed at decision makers, so that they can improve their decisions, and a main section aimed at peers explaining the technical details and exact interpretation of the results. The report is written in APA Style. Course schedule Chapter numbers refer to Freedman et al. (2007). Week 1 How the course is organized. Read Joseph Stromberg (2014, August 21). Why you should take notes by hand not on a laptop. Vox ( 2014/6/4/ /note-taking-by-hand-versus-laptop Descriptive statistics (average, standard deviation, density histogram, approximating a density histogram by the normal curve). Chapter 1 of the lecture notes (posted on the course web site) (covers Freedman et al. (2007, Preface and Ch. 3, 4, 5)). Download and print STA101_Getting_started.pdf (posted on the course home page) and follow the instructions. Week 2 Probability distributions (chance processes, expected value, chance errors, standard error). Chapter 2 of the lecture notes (covers Freedman et al. (2007, Ch. 16, 17, 18)). Tuesday: An introduction to R and R Commander. Bring your laptop computer (if you have one) to class. Make sure R and R Commander are installed on your laptop computer, and that you can connect to the internet. Week 3 Sampling distributions: the sum of draws, the expected value and standard error of the sum of draws, the central limit theorem. The sampling distribution of a sample percentage. The sampling distribution of a sample average. Chapter 3 of the lecture notes (covers Freedman et al. (2007, Ch. 16, 17, 18, 20, 23 section 1)). Week 4 Inference: confidence intervals. Chapter 4 of the lecture notes (covers Freedman et al. (2007, Ch. 21, 23)). Assignment 1 is due. Week 5 Inference: hypothesis tests. Freedman et al. (2007, Ch. 26, 29). 2

3 Week 6 Inference: hypothesis tests for small samples. Freedman et al. (2007, Ch. 26, section 6). Assignment 2 is due. Week 7 midterm exam week Inference: hypothesis tests on two averages. Freedman et al. (2007, Ch. 27). Midterm examination. Covers all material covered to date. Bring your student ID (with picture), a mechanical pencil, an eraser, a pen, a ruler with a centimeter scale, the TI-84 calculator, a set of spare batteries for the TI-84, and the laminated formula sheets. Week 8 Inference: hypothesis tests on two averages. Freedman et al. (2007, Ch. 27). (There is a mistake in Freedman et al. (2007, Ch. 27). In example 2 (pp ), the authors use a one-sided alternative hypothesis; in example 3 (pp ), they do so, too. They base their one-sided hypothesis on the outcome of the sample. But this is data snooping, a practice they condemn on pp In both examples the alternative hypothesis should be two-sided.) In class we work exercise A-2 from Freedman et al. (2007, p. 503). At home, adapt the script SE-for-a-difference.R to reflect the givens of exercise A-2. Then run the script ten times, and construct a table with the results (column 1 is the run no. (1, 2, 3,..., 10), column 2 is the difference of the sample averages, column 3 is the chance error, and column 4 is the chance error without the minus sign. Are the simulation results in columns 2 and 4 in line with what the expectation and the standard error you found in exercise A-2? Week 9 Measures of linear relationship: correlation. Freedman et al. (2007, Ch. 8, 9). Friday of week 9 is the last day to drop a course. Week 10 Measures of linear relationship: the regression line. Freedman et al. (2007, Ch. 11, 12). Download, print and read line_of_best_fit.pdf. Assignment 3 is due. Week 11 Multiple regression (Chapter 10 of the lecture notes). Week 12 Multiple regression, continued (Chapter 10 of the lecture notes). Assignment 4 is due. 3

4 Week 13 Inference for regression (Chapter 11 of the lecture notes). Week 14 The Chi-Square test. Freedman et al. (2007, Ch. 28). Skip the explanation of how to use χ 2 -tables (starting on p. 527 with In principle, there is one table... and ending with the sketch on the top of p. 528; statisticians use a statistical calculator or statistical software to find areas under the χ 2 -curve. Also skip section 3 ( How Fisher used the χ 2 -test ), pp Week 15 Final exam week Final exam. Covers all material since the midterm exam. Bring your student ID (with picture), a mechanical pencil, an eraser, a pen, a ruler with a centimeter scale, the TI-84 calculator, a set of spare batteries for the TI-84, and the laminated formula sheets. Course materials The required textbook is Freedman et al. (2007). You also need the Texas Instruments TI-84 calculator. Lecture notes are posted on the course web site. Course assessment There are four short research papers and two written exams. The final grade for the course will be calculated on the following weighting: Participation and preparedness 10 % Assignments (four, each carrying 7.5%) 30 % Midterm exam 30 % Final exam 30 % Grading scale of Vesalius College Vesalius College grading policy, in line with the Flemish Educational norms, is now as stated follows: 4

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