Applied Business Research Tools
Applied Business Research Tools ECO 6416
University of Central Florida
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This 3 page Class Notes was uploaded by Toy Kertzmann on Thursday October 22, 2015. The Class Notes belongs to ECO 6416 at University of Central Florida taught by Mark Dickie in Fall. Since its upload, it has received 50 views. For similar materials see /class/227630/eco-6416-university-of-central-florida in Economcs at University of Central Florida.
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Date Created: 10/22/15
1 Sample Data Types Cross Sectional I Obsenation ofdifferent units at same ti 0 Time Series I Obsenation of same unit overtime 0 Panel I Repeated observation ofdifferent units through time 2 Converting Nominalto real 3 Types of Data a Nom39 0 Real Syear s Nominal SyeartCP year tCPIyears Inal i Classification but no order distance or origin b Ordinal i Classification and order but no distance unique origin c Interval i Classification order and distance but no unique origin d Ratio i Classification order distance and uni ue origin 4 Measurement Precision Reliability Validity 5 Sampling Terminology a pulation complete set of items of interest b Population element Individual memberof popu a ion c Census Compelete enumeration of all elements in a population d Sample A subset of a population selected for investigation e Population Frame List ofall elements in population f Sample frame List ofelements from which sample will be drawn 6 Multistage sampling Primaw secondaw teriaw sampling units 7 Categories ofSampling Nonprobability Sampling Sampling error is wn i ConvenienceJudgment Quota Snowball b Probability Sampling Known nonzero probability for selecting any element i Simple random sample systematic sample stratified sample cluster sample mlultistage cluster sample c Systematic Sampling i Random startand evew kth name in list d Stratified Sampling i efine groups in population then select probability samples from within the different grou s ii Increased homgeneity within groups iii Increases heterogeneity between groups e ClusterSampling i Main ex mple is area sampling where the clusters are geographic areas 8 Variance a Average ofthe squared differences between each data value and the me n 9 Correlation coefficient a Values near 1 indicate a strong negative linear relationship Values near 1 indicate a strong positive linear relationship equals no linear relationship 10 zScores Standardized Va ue Inferential Statistics 11 Central LimitTheorem the sample size increases the sampling distribution ofthe samp e me n i Approaches a normal distribution ii With a mean equal to the population mean iii Standard deviation equaltothe population standard deviation divided by the square root of sample size 12 Point Estimation a Estimate i Numerical value calculated from data in the sample b Estimator i Fo mula 13 Hypothesis Testing a Type I Easierto control i The errorof rejectingatrue null hypothesis Probability Level of Significance 10 1 Confidence Level is the opposite 90 b Type II i Error of not rejecting a false null hypothesis c Null hypothesis The idea that you think is not true Exam 1 Notes 1 Sample Data Types Inferential Statistics Cross Sectional 12 Point Estimation I Obsenation of different units at same 3 EStlmate time i Numerical value calculated from data in 0 Time Series the sample I Obsenation of same unit overtime 0 Panel b Estimator i Formula 13 Hypothesis Testing a Type Easierto control i The errorof rejectingatrue null hypothesis Probability Level of Significance 10 1 Confidence Level is the opposite I Repeated observation ofdifferent units through time 8 Variance a Average ofthe squared differences between each data value and the me n 9 Correlation coefficient 90 a alues near 1 indicate a strong negative linear relations ip Values near 1 indicate a strong positive linear relations ip b Type H i Error of not rejecting a false null 5339 hypothesIs c Null hypothesis The idea that you think is not c equals no linear relationship true 10 zScores Standardized Value Exam 2 Notes 1 The Least Squares Assumptions b Explained sum of squares SSE measures a The errorterm u has conditional mean zero the variation in V that is quotexplained byquot the given Xi estimated regress39on i Ex ywage xschooling c Residual sum of squares SSR measures the u motivation variation in V that is quotunexplained byquot the b XV are independent and identically estimated regression distributed i SSR is minimized by OLS i With higher income you have ii SSR is used in estimating more variability with housing variance ofV given X ii It happens with Crosssectional d Standard Errorof Regression c Large outliers are unlikely i Measures how farfrom the i Vvisitors XTickets fitted regression is the typical urecession obsened data point ii It happens with Timeseries 7 R2 RSquared d There Is No Perfect Multicollinearity a It explains percent of the variation in the 2 Method of least squares givesthe line of best fit coeffi 39ents Residual Sum of Squares b the coefficients are zero R2 is zero 3 Properties of OLS Estimators c The adjusted R2 will never be largerthan R2 a Simple to compute d Adjusted R2 can be negative Each sample has different estimated values e R2 always increases when you add another ofthe slope and intercept adjusted deoes not necessarily increase c The sample average of the OLS residuals is 8 Omitted Variable Bias a and b must be true zero a The omitted independent variable is d OLS Estimators are unbiased Right on correlated with the included independent Average varia e Across alternative samples the mean ofthe b The omitted independent variable is a OLS estimator ofthe parameter equalsthe determinant of the dependent variable true value c Direction of bias is based off of the 4 Estimates vaw less between alternative possible relationship between the omitted variable samples when and the dependent and independent Variance ofV given X is smaller variable b Sample sizes are larger i fthe relationship between the c Variation in X is greater variables isthe same it is 5 GaussMarkovTheorem positive if it is opposite it is a OLS is best linear unbiased estimator negative BLUE 9 Chisquare b Thus OLS estimators are Unbiased a First chart Actual Frequencies Efficient minimum variance consistent b Second chart Expected frequencies Total 6 Sumsoquuares of row multiplied bytotalof column a Total sum of squares SST measures the c Third chart Actual 7 Expectedz total variation in V Expected d Chisquare is totalofthird chart critical values come from the x2 distribution chart e How to interpret regression 10 FStatistic Tests all of the coefficients at once 11 How to interpretthe regression Formulas a Explain R2 means of variation ofy is explained bythe regression line b Explain positive or negative association of coefficients c Explain 1 unit increase relative to dependent coefficient d Explain which coefficients are statistically significant Point and Interval Estimation Confidence interval for population proportion p i Z Poem n Z z 1 Sample Size n W E Confidence Interval for Population meanT i ZaZS 95bConfidence Interval for slope 31 i 1 6SEE1 Fir Fir Sample Value Test Statistic z or E a Regression o Y 1X Covariance sxy 1 Variance an 5 Sign of slope is determined by Sxy since Sm is always positive 7 o l39 lXi l39 H Population regression function V o le 1 Deviation of population element H K Squared Residuals 02 SSR Sum of Squared Residuals Sum of Y TY Sum ofu2 SST Variance of Y X N 1 65N 1 SSE SST SSR S E t statistic t statistic With n2 degrees of freedom Variance 0 1 Wh x For a large sample Confidence interval k i zaZSEM Multiple Regression n71 SSR nikil SST R21 55 K F SSR N491 F critical value coefficientsNKl F SSRrestriCted SSRmuesmczed M SSRerestrlcted nkmuesmczed 1 Two Sample Tests 52 quotM 1512VIquotF15 nMnF 2
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