Regression  Statistics Multiple R 0.9086 R square A Adjusted R square 0.8181 standard Error 398.0910 Observations B anova             df SS MS F Significance F Regression 2 D E G 1.50876E-18 Residual C 7448393.148 F     Total 49 42699148.82         coefficients Standard Error t-stats p-value intercept 1304.9048 197.6548 6.6019 3.28664E-08 Income ($ 1000s) 33.1330 3.9679 H 7.68206E-11 Household size 356.2959 33.2009 10.7315 3.12342E-14           The multiple regression output is based on data collected by a research company on annual income, household size and annual credit charges for a sample if 50 consumers. Compute the missing entries from A to H in this output.

pre-test post-test Mean Variance -2.25 0.75 9.30 13.30 Observations 12 12 Pearson Correlation 0.9478 Hypothesized Mean 0 Difference df 11 t Stat 2.076 P(T<=t) one-tail 0.035 t Critical one-tail 1.796 P(T<=t) two-tail 0.065 t Critical two-tail 2.201

Analysis of Variance results: Where: Age > 5Responses: AgeFactors: Sex Response statistics by factor Sex n Mean Std. Dev. Std. Error female 121 10.490083 4.2222704 0.38384277 male 155 9.0806452 3.0091656 0.241702 ANOVA table Source DF SS MS F-Stat P-value Sex 1 134.98939 134.98939 10.466692 0.0014 Error 274 3533.79 12.897044     Total 275 3668.7794       Tukey HSD results (95% level) female subtracted from   Difference Lower Upper P-value male -1.4094375 -2.2670907 -0.55178425 0.0014 In one complete sentence, interpret your confidence interval. Make sure in the interpretation it is clear the direction of the difference (i.e. which gender weighs more and by how much. Explain why a confidence interval provides more information to a tiger researcher than the results of a hypothesis test

ANOVA table  Source df sum of sqaures Mean squares F Regression 1 4.9 4.9 13.38 Error 3 1.1 0.366   Total 4 6     i. name the sampling distribution of the F-test statistic ii should we reject the null or fail to reject the null iii. justify using the rejection region and p-value, draw the sampling distribution and shade area corresponding to p-value iii

Treatment II III 2 N = 24 G = 72 EX2 = 292 3 M = 2 M = 3 T = 16 T= 24 T= 32 SS = 16 SS = 24 SS = 20 Use an ANOVA with a = .05 to determine whether there is a significant mean difference between the two treatments. (Round to two decimal places where needed.) Source df MS Feritical Between treatments Within treatments Total F Distribution Numerator Degrees of Freedom = 6 Denominator Degrees of Freedom = 16 0.0 1.0 3.0 4.0 5.0 6.0 7.0 9.0 11.0 120 Conclusion: Fail to reject the null hypothesis; there are no significant differences among the three treatments. Reject the null hypothesis; there are no significant differences among the three treatments. Reject the null hypothesis; there are significant differences among the three treatments. o Fail to reject the null hypothesis; there are significant differences among the three treatments.

Fill in the ANOVA table using the data attached.  Source df sum of squares MEan squares F Regression 1       Error 3       Total 4

35

Considering that the dependent variable (DV) is continuous, what ANOVA procedure would you like to use if there is a single between-subject Independent Variable (IV) with three distinct groups?  Group of answer choices One-Way ANOVA Two-Way ANOVA Split-Plot ANOVA Repeated Measures ANOVA All of the Above

Excessive fertilizer and surface runoff can result in heavy phosphorus concentrations and reduced oxygen levels in lakes and ponds. This can lead to die-off of insects, fish, an increase in algae, and overall reduced health of the ecosystem. With the increase in algae, you are concerned about the amount of dissolved oxygen (DO). You measure the average DO levels in randomly sampled lakes in two different types (urban and rural) at the same time of year. Conduct a hypothesis test at 5% level of significance. Is there evidence to suggest that the average DO levels are significantly higher in lakes found in rural areas when compared to lakes in more urban areas? Use the table below. What is the test statistic?

The following chart   Regression Statistics         Multiple R 0.896207         R Square 0.803187         Adjusted R Square 0.775071         Standard Error 10.31462         Observations 9                     ANOVA             df SS MS F Significance F Regression 1 3039.26 3039.26 28.5668 0.001070199 Residual 7 744.7395 106.3914     Total 8 3784                     Coefficients Standard Error t Stat P-value Lower 95% Intercept 92.26212 7.55045 12.21942 5.63E-06 74.40814014 X Variable 1 -2.36029 0.441605 -5.34479 0.00107 -3.404514314   shows a:       very strong positive correlation     very weak correlation     very strong negative correlation     we don't have the necessary information to evaluate the level of correlation