E370 | ALL | All

E370 Statistical Analysis for Business and Economics ATTENTION: More details about this course are available at the Economics Department Internet site for E270/E370 at the following address: http://www.indiana.edu/~econstat/ This class includes two components: (i) the lectures where statistical concepts and methods are introduced and discussed, and (ii) computer labs (recitation sections) where you learn to apply statistical methods of data analysis using a standard computer package. Prerequisites: M118 (Finite Mathematics) completed prior to this class is an absolute requirement. At least concurrent registration in a calculus class (the M119 level or above) is also required. Required Computer Program: Microsoft Excel This program is available through the "spreadsheets" submenu in any of the UITS clusters. Statistical applications of EXCEL will be used in this class. The knowledge of these EXCEL applications will be required for exams, computer lab tests and homework assignments. Other Requirements: DOS formatted 3.5 inch floppy disk. Getting Help: An extensive (free) tutoring help in this class will be provided by the Economics Department. Specific schedule will be announced in the second week of classes. Course Objectives This class builds on your overall quantitative concepts and skills, as well as on the knowledge of basic probability and statistics you obtained in your Finite Math class. This class should substantially enhance this very important component of your college education. It will provide - the understanding of key statistical concepts used in economics and business; - the knowledge of basic statistical methods of data analysis which are rigorously founded in the theory of probability, and the ability to apply these methods with the help of statistical tools available on computer; - the ability to draw statistical inferences, i.e., to interpret the results obtained from the application of statistical methods. Course Grading The course grade will be based on the scores you earn for (i) Two mid-term exams and the departmental final exam consisting of multiple choice questions, and (ii) Evaluation of your computer lab work will include - two computer lab tests using statistical applications in Microsoft Excel (maximum 50 points each); - eight homework assignments which will be given to you in your computer lab section; - computer lab attendance/participation COURSE OUTLINE I. Statistics and Measurement A. Statistical Problems. Population and Sample Data B. Representation of Data: Frequency Distributions and Histograms C. Measures of Central Tendency and Dispersion EXCEL applications: By the end of this section students should be able to use alternative methods and measures to describe sets of data. Students should be able to enter data, do calculations of descriptive statistics and draw histograms both using a calculator and EXCEL program on computer, and interpret results from a computer printout. II. Basic Rules of Probability A brief review, knowledge presumed within the scope of M118. An important concept to review: independent random events. III. Random Variables, Expected Values A. Random Variables (brief review, knowledge presumed within the scope of M118) B. Expected Values with the emphasis on finance applications 1.Mean of a random variable, mean of a sum of random variables 2.Variance of a random variable, variance of a sum of random variables 3.Covariance IV. Probability Distributions A. Discrete: Binomial; Hypergeometric, Poisson (only the awareness of the concepts and applicability) B. Continuous: Uniform, Normal and Standard Normal By the end of this section students should be able to identify various probability distributions studied and to use EXCEL in probability calculations for binomial and normal distribution. V. Sampling Distribution and Estimation A. Sampling B. Sampling distribution of sample mean and proportion, Central Limit Theorem C. Point and Interval Estimation of a Population Mean; t-distribution D. Point and Interval Estimation of a Population Proportion EXCEL applications: By the end of this section students should be able to use EXCEL for determining confidence intervals, and to fully interpret the information in the EXCEL printout regarding sampling distribution and confidence intervals. They should identify the applications requiring probability calculations with z or t distributions, and to use EXCEL for it. VI. Hypothesis Testing A. Testing Concepts and Procedures B. Testing a Population Mean (z- and t-tests) C. Testing a Population Proportion EXCEL applications: By the end of this section students should be able to use sample data to test claims and propositions about population parameters. They should be able to use EXCEL to calculate critical values and p-values and to apply them in making an inference. VII. Regression and Correlation A. A Simple Linear Regression Model B. The Method of Least Squares 1.Parameter estimation 2.Regression, total and error sum of squares 3.Parameter variance estimation 4.Predicting a particular value for y for a given value of x C. Inference concerning the slope and intercept D. Correlation, goodness of fit EXCEL applications: Given paired data on dependent variable y and independent variable x, students should be able to apply EXCEL to obtain least squares estimation of the linear relationship between these two variables, to predict values of y given the values of x, based on the simple linear regression model, to test hypotheses about the coefficients of the model. Given the EXCEL printout, they will be expected to identify and to interpret coefficients of regression model and other statistics. VIII. Multiple Regression A. Linear Regression Model B. The Method of Least Squares in a Multiple Regression Model C. Predicting with a Multiple Regression Model D. Testing hypotheses about coefficients of a model E. R-squared measure of the goodness of fit of a model EXCEL applications: Students will be expected to interpret EXCEL printouts of least squares estimation of a linear relationship between a dependent variable and several independent variables, to make predictions about dependent variable based on the model, and to test hypotheses about its coefficients.