Also using the BYU Student Survey data, we wanted to see if males and females differed in likelihood of clinical level depression. Using our depression measure, we created a dichotomous variable for depression diagnosis (0 = Not Clinically Depressed; 1 = Clinical Levels of Depression). Here are the results.   Gender Depression Diagnosis Table Image Description . tab gender depressiondiag, all row expected Keyfrequencyexpected frequencyrow percentage Gender Differences in Depression Diagnosis What is your gender? Depression diagnosis 0 Depression diagnosis 1 Total Male 191 152 343 174.6 168.4 343.0 55.69 44.31 100.00 Female 289 311 600 305.4 294.6 600.0 48.17 51.83 100.00 Total 480 463 943   480.0 463.0 943.0 50.90 49.10 100.00   Pearson chi2(1) = 4.9362, Pr = 0.026 Likelihood-ratio chi2(1) = 4.9444, Pr = 0.026 Cramér’s V = 0.0724 Gamma = -0.1497, ASE = 0.066 Kendall’s tau-b = -0.0724, ASE = 0.032  

Please (a) write out the regression equation using numbers from the following output (which is from Example 2). Then, label the (b) slope and (c) intercept. Lastly, write out the interpretation of the (d) slope and (e) intercept, being specific using the number from the equation. .regress exam1 Table Image Description . regress exam1 gpa, beta ANOVA Results for Regression of exam1 on gpa Source SS df MS Model 1224.30834 1 1224.30834 Residual 2176.60833 106 20.5340408 Total 3400.91667 107 31.7842679 Number of obs: 108 F(1, 106) = 59.62 Prob > F: 0.0000 R-squared: 0.3600 Adjusted R-squared: 0.3540 Root MSE: 4.5315 Regression Coefficients for the Relationship Between gpa and _cons exam1 Coef. Std. Err. t P>|t| Beta gpa 5.027622 .6511101 7.72  0.000 .5999947 _cons 27.65899 2.037562 13.57 0.000 .  

When you retain the null hypothesis in a regression, how do you statistically interpret the results?

When you reject the null hypothesis in a correlation, how do you statistically interpret the results?

Please define the following terms. 

The following example is used for questions 41–50: It is often thought that ethnic minorities tend to be more family oriented than European Americans, often even having multiple generations under the same roof. We wanted to empirically test this folk notion using the MUSIC data of college students from 30 universities across the U.S. We used the Streidel and Contreras’ (2003) 18-item familism scale as a measure of family focus. We ran an ANOVA with follow-up pairwise comparisons. Here is the output.        Visual Description c.familism@ethnicity Mean Std. Err. [95% Conf. Interval] Black 3.689388 0.020961 3.6483 – 3.730476 White 3.52592 0.0076755 3.510874 – 3.540965 Asian 3.657346 0.0166046 3.624798 – 3.689895 Hispanic 3.676493 0.0153429 3.646417 – 3.706569 Number of obs = 8,911 R-squared = 0.0152 Root MSE = .573254 Adj R-squared = 0.0149 Source Partial SS df MS F Prob>F Model 45.177051 3 15.059017 45.83 0.0000 ethnicity 45.177051 3 15.059017 45.83 0.0000 Residual 2927.0157 8,907 .32861971 Total 2972.1928 8,910 .33357944   Visual Description ethnicity Contrast Std. Err. t P>|t| [95% Conf. Interval] White vs Black -.1634681 .0221063 -7.39 0.000 -.2202706, -.1066657 Asian vs Black -.0320414 .0265499 -1.21 0.622 -.1002618, .0361789 Hispanic vs Black -.0128949 .0259914 -0.50 0.960 -.0796802, .0538904 Asian vs White .1314267 .0182571 7.20 0.000 .0845147, .1783387 Hispanic vs White .1505733 .017435 8.64 0.000 .1057738,  .1953727 Hispanic vs Asian .0191466 .0228076 0.84 0.836 -.039458, .0777511

SSregression

pairwise comparisons

Which of the following is not a use of multiple regression?

F-ratio