Part 14: Statistical Tests – Part /25 Statistics and Data Analysis Professor William Greene Stern School of Business IOMS Department Department of Economics

Part 14: Statistical Tests – Part /25 Statistics and Data Analysis Part 14 – Statistical Tests: 2

Part 14: Statistical Tests – Part /25 Statistical Testing Applications  Methodology  Analyzing Means  Analyzing Proportions

Part 14: Statistical Tests – Part /25 Classical Testing Methodology  Formulate the hypothesis.  Determine the appropriate test  Decide upon the α level. (How confident do we want to be in the results?) The worldwide standard is  Formulate the decision rule (reject vs. not reject) – define the rejection region  Obtain the data  Apply the test and make the decision.

Part 14: Statistical Tests – Part /25 Comparing Two Populations These are data on the number of calls cleared by the operators at two call centers on the same day. Call center 1 employs a different set of procedures for directing calls to operators than call center 2. Do the data suggest that the populations are different? Call Center 1 (28 observations) Call Center 2 (32 observations)

Part 14: Statistical Tests – Part /25 Application 1: Equal Means  Application: Mean calls cleared at the two call centers are the same  H 0 : μ 1 = μ 2 H 1 : μ 1 ≠ μ 2  Rejection region: Sample means from centers 1 and 2 are very different.  Complication: What to use for the variance(s) for the difference?

Part 14: Statistical Tests – Part /25 Standard Approach  H 0 : μ 1 = μ 2 H 1 : μ 1 ≠ μ 2  Equivalent: H 0 : μ 1 – μ 2 = 0  Test is based on the two means: Reject the null hypothesis if is very different from zero (in either direction. Rejection region is large positive or negative values of

Part 14: Statistical Tests – Part /25 Rejection Region for Two Means

Part 14: Statistical Tests – Part /25 Easiest Approach: Large Samples  Assume relatively large samples, so we can use the central limit theorem.  It won’t make much difference whether the variances are assumed (actually are) the same or not.

Part 14: Statistical Tests – Part /25 Variance Estimator

Part 14: Statistical Tests – Part /25 Test of Means  H 0 : μ Call Center 1 – μ Call Center 2 = 0 H 1 : μ Call Center 1 – μ Call Center 2 ≠ 0  Use α = 0.05  Rejection region:

Part 14: Statistical Tests – Part /25 Basic Comparisons Descriptive Statistics: Center1, Center2 Variable N Mean SE Mean StDev Min. Med. Max. Center Center Means look different Standard deviations (variances) look quite different.

Part 14: Statistical Tests – Part /25 Test for the Difference Stat  Basic Statistics  2 sample t (do not check equal variances box) This can also be done by providing just the sample sizes, means and standard deviations.

Part 14: Statistical Tests – Part /25 Application: Paired Samples  Example: Do-overs on SAT tests Hypothesis: Scores on the second test are no better than scores on the first. (Hmmm… one sided test…) Hypothesis: Scores on the second test are the same as on the first. Rejection region: Mean of a sample of second scores is very different from the mean of a sample of first scores.  Subsidiary question: Is the observed difference (to the extent there is one) explained by the test prep courses? How would we test this?  Interesting question: Suppose the samples were not paired – just two samples.

Part 14: Statistical Tests – Part /25 Paired Samples  No new theory is needed  Compute differences for each observation  Treat the differences as a single sample from a population with a hypothesized mean of zero.

Part 14: Statistical Tests – Part /25 Testing Application 2: Proportion  Investigate: Proportion = a value  Quality control: The rate of defectives produced by a machine has changed.  H 0 : θ = θ 0 (θ 0 = the value we thought it was) H 1 : θ ≠ θ 0  Rejection region: A sample of rates produces a proportion that is far from θ 0

Part 14: Statistical Tests – Part /25 Procedure for Testing a Proportion Use the central limit theorem: The sample proportion, p, is a sample mean. Treat this as normally distributed. The sample variance is p(1-p). The estimator of the variance of the mean is p(1-p)/N.

Part 14: Statistical Tests – Part /25 Testing a Proportion  H 0 : θ = θ 0 H 1 : θ ≠ θ 0  As usual, set α =.05  Treat this as a test of a mean.  Rejection region = sample proportions that are far from θ 0. Note, assuming θ=θ 0 implies we are assuming that the variance is θ 0 (1- θ 0 )

Part 14: Statistical Tests – Part /25 Default Rate  Investigation: Of the 13,444 card applications, 10,499 were accepted.  The default rate for those 10,499 was 996/10,499 =  I am fairly sure that this number is higher than was really appropriate for cardholders at this time. I think the right number is closer to 6%.  Do the data support my hypothesis?

Part 14: Statistical Tests – Part /25 Testing the Default Rate  p =  θ 0 = 0.06  As usual, use 5%.

Part 14: Statistical Tests – Part /25 Application 3: Comparing Proportions  Investigate: Owners and Renters have the same credit card acceptance rate  H 0 : θ RENTERS = θ OWNERS H 1 : θ RENTERS ≠ θ OWNERS  Rejection region: Acceptance rates for sample of the two types of applicants are very different.

Part 14: Statistical Tests – Part /25 Comparing Proportions Note, here we are not assuming a specific θ O or θ R so we use the sample variance.

Part 14: Statistical Tests – Part /25 Some Evidence = Homeowners

Part 14: Statistical Tests – Part /25 Analysis

Part 14: Statistical Tests – Part /25 Followup Analysis of Default DEFAULT OWNRENT 0 1 All All Are the default rates the same for owners and renters? The data for the 10,499 applicants who were accepted are in the table above. Test the hypothesis that the two default rates are the same.