Statistical Inference:  Confidence Intervals, F-tests and t-tests Objective:  Create Confidence Intervals for Regression Coefficients by replicating  E.G. 5.2 on pp. 134-135 using the woody3.csv data file.  The data file contains information for 33 restaurants on Y (sales), NN (# of nearby competitors), P (local population) and I (average local family income).  I.   Prep Assignment using the woody3.csv data file.  The data file contains information for 33 restaurants on Y (sales), NN (# of nearby competitors), P (local population) and I (average local family income).   Note:  this data is cross-sectional data, i.e., it's measured across individuals at a point of time. a.   Run a regression of Y on NN, P and I.  Print out the regression results.  b.  What regression "model" are you estimating?  Specifically, what kind of relationship are you assuming exists between which variables?  Describe verbally and algebraically. II.  Create Confidence Intervals for the Regression Coefficients by replicating  E.G. 5.2 on pp. 134-135.  Create 90% confidence intervals for the regression coefficients in your model.  Interpret.  Note:  you can generate these confidence intervals after you have run the ordinary least squares regression by clicking on * Analysis * Confidence intervals for the coefficients *.   Click on the a icon to set the confidence level to a value other than 95%.  III.  Conduct an F-Test on the regression by replicating E.G. 5.6 on pp. 159-163.  Show your hypotheses, sample statistic, critical statistic and decision process.  Interpret the finding.  Also examine the p value and interpret. IV.  Conduct t-tests on the regression coefficients by replicating E.G. 5.2 on pp. 128-134.  Specifically test whether the constant and NN coefficient are zeros, and whether the other 2 coefficients are positive values.  In each case, show your hypotheses, sample statistic, critical statistic and decision process and interpret the findings.  V.  Examine the p-values of the estimated regression coefficients and interpret. VI:  Test whether the regression coefficient on I is greater than 1.  Show your hypotheses, sample statistic, critical statistic and decision process.  Interpret the finding.